From 9a19b5c4272a6b7995bec7180375a355a642af42 Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Mon, 6 Apr 2015 11:58:42 -0600
Subject: [PATCH 01/85] adds TODOS to simpleFun and voronoiHistogram

---
 bet/calculateP/simpleFunP.py       |  7 +++++++
 bet/calculateP/voronoiHistogram.py | 15 ++++++++++-----
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/bet/calculateP/simpleFunP.py b/bet/calculateP/simpleFunP.py
index 7da96544..ae7c80b3 100644
--- a/bet/calculateP/simpleFunP.py
+++ b/bet/calculateP/simpleFunP.py
@@ -119,6 +119,7 @@ def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     '''
     return (rho_D_M, d_distr_samples, d_Tree)
 
+#TODO Empty function hist_regular 
 def hist_regular(data, distr_samples, nbins):
     """
     create nbins regulary spaced bins
@@ -130,6 +131,7 @@ def hist_regular(data, distr_samples, nbins):
     """
     pass
 
+# TODO Empty function hist_gaussian
 def hist_gaussian(data, distr_samples, nbins):
     """
     determine mean, standard deviation of distr_samples
@@ -141,6 +143,7 @@ def hist_gaussian(data, distr_samples, nbins):
     """
     pass
 
+# TODO Empty Function hist_unif
 def hist_unif(data, distr_samples, nbins):
     """
     same as hist_regular bit with uniformly spaced bins
@@ -148,10 +151,12 @@ def hist_unif(data, distr_samples, nbins):
     """
     pass
 
+# TODO Empty function gaussian_regular
 def gaussian_regular(data, Q_ref, std, nbins, num_d_emulate=1E6):
     pass
     #return (d_distr_prob, d_distr_samples, d_Tree)
 
+# TODO Comment or remove multivariate_gaussian
 def multivariate_gaussian(x, mean, std):
     dim = len(mean)
     detDiagCovMatrix = np.sqrt(np.prod(np.diag(std(std))))
@@ -306,6 +311,7 @@ def unif_normal(Q_ref, M, std, num_d_emulate=1E6):
     # solving the model EVER! This can be done "offline" so to speak.
     return (rho_D_M, d_distr_samples, d_Tree)
 
+# TODO gaussian_unif is a blank function
 def gaussian_unif(data, Q_ref, std, nbins, num_d_emulate=1E6):
     pass
     #return (d_distr_prob, d_distr_samples, d_Tree)
@@ -334,6 +340,7 @@ def uniform_hyperrectangle_user(data, domain, center_pts_per_edge=1):
         ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
+    # TODO following code could probably be reduced to a few lines
     # determine the center of the domain
     if len(domain.shape) == 1:
         domain = np.expand_dims(domain, axis=1)
diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index 6e0451bb..7cac1112 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -36,7 +36,8 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
         msg = "The hyperrectangle defined by this size is larger than the"
         msg += "original domain."
         print msg
-
+    
+    #TODO This can be done in fewer lines
     # determine the hyperrectangle defined by center and r_size
     rect_width = r_size*np.ones(sur_domain[:,0].shape)
     rect_domain = np.empty(sur_domain.shape)
@@ -62,7 +63,8 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
 
     return (points, interior_and_layer1, rect_domain)
 
-
+# TODO This and the previous function are almost identical. Maybe calculate r_size
+# from r_ratio and call the previous function.
 def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
     r"""
     Generates a regular grid of center points that define the voronoi
@@ -123,6 +125,7 @@ def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
         points = util.meshgrid_ndim(interior_and_layer1)
     return (points, interior_and_layer1, rect_domain)
 
+
 def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
     """
     Generates a sequence of arrays describing the edges of the finite voronoi
@@ -156,7 +159,7 @@ def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
         (center_pts_per_edge+4,) respectively.
 
     """
-
+    # TODO This is redoing stuff that was done when generating the points
     # determine the hyperrectangle defined by center and r_size
     rect_width = r_size*np.ones(sur_domain[:,0].shape)
     rect_domain = np.empty(sur_domain.shape)
@@ -178,7 +181,8 @@ def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
         rect_and_sur_edges.append(int_l2) 
 
     return rect_and_sur_edges
-
+# TODO This and the previous function are almost identical. Maybe calculate r_size
+# from r_ratio (or vice versa)  and call the previous function.
 def edges_regular(center_pts_per_edge, center, r_ratio, sur_domain):
     """
     Generates a sequence of arrays describing the edges of the finite voronoi
@@ -319,6 +323,7 @@ def histogramdd_volumes(edges, points):
     # adjust edges
     points_max = np.max(points, 0)
     points_min = np.min(points, 0)
+    # TODO replace with logical arrays to get rid of for loop.
     for dim, e in enumerate(edges):
         if len(edges) == 1:
             if e[0] >= points_min:
@@ -360,7 +365,7 @@ def simple_fun_uniform(points, volumes, rect_domain):
     """
     if len(points.shape) == 1:
         points = np.expand_dims(points, axis=1)
-   
+    # TODOthe lines below could be done in one or two lines.
     rect_left = np.repeat([rect_domain[:, 0]], points.shape[0], 0)
     rect_right = np.repeat([rect_domain[:,1]], points.shape[0], 0)
     rect_left = np.all(np.greater_equal(points, rect_left), axis=1)

From 4b8bea9ce3f23c862e082d8afceb45e76721bdec Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Mon, 6 Apr 2015 12:33:20 -0600
Subject: [PATCH 02/85] pylinted and audited plotDomains and plotP.

---
 bet/postProcess/__init__.py    |   8 +-
 bet/postProcess/plotDomains.py |  69 +++++++++++------
 bet/postProcess/plotP.py       | 130 ++++++++++++++++++++-------------
 3 files changed, 133 insertions(+), 74 deletions(-)

diff --git a/bet/postProcess/__init__.py b/bet/postProcess/__init__.py
index 212068d8..bfe467f5 100644
--- a/bet/postProcess/__init__.py
+++ b/bet/postProcess/__init__.py
@@ -1,8 +1,10 @@
-"""
+r"""
 This subpackage contains
 
-* :class:`~bet.postProcess.plotP` plots P and/or volumes of voronoi cells
-* :class:`~bet.postProcess.plotDomains` plots the data domain D in 2D
+* :class:`~bet.postProcess.plotP` plots :math:`P` and/or volumes (:math:`\mu`)
+    of voronoi cells
+* :class:`~bet.postProcess.plotDomains` plots the data domain
+    :math:`\mathcal{D} in 2D
 * :class:`~bet.postProcess.postTools` has tools for postprocessing
 
 """
diff --git a/bet/postProcess/plotDomains.py b/bet/postProcess/plotDomains.py
index 5cce4fea..5d02154d 100644
--- a/bet/postProcess/plotDomains.py
+++ b/bet/postProcess/plotDomains.py
@@ -1,11 +1,24 @@
 """
-Methods used to plot two-dimensional domains and/or two-dimensional
-slices/projections of domains.
+This module provides methods used to plot two-dimensional domains and/or
+two-dimensional slices/projections of domains.
+
+TODO : match method signatures with style in plotP
 """
 
 import matplotlib.tri as tri
 import numpy as np
 import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+markers = []
+for m in Line2D.markers:
+    try:
+        if len(m) == 1 and m != ' ':
+            markers.append(m)
+    except TypeError:
+        pass
+
+colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k')
 
 def scatter_2D(samples, sample_nos, color, p_ref, save, show,
         xlabel, ylabel, filename):
@@ -31,8 +44,7 @@ def scatter_2D(samples, sample_nos, color, p_ref, save, show,
         sample_nos = range(samples.shape[0])
     color = color[sample_nos]
     plt.scatter(samples[sample_nos, 0], samples[sample_nos, 1], c=color, s=10,
-            #edgecolor='none',
-            alpha=.75, linewidth=.1, cmap=plt.cm.Oranges)#Oranges_r)
+            alpha=.75, linewidth=.1, cmap=plt.cm.Oranges)
     cbar = plt.colorbar()
     cbar.set_label(r'$\rho_\mathcal{D}(Q)$')
     if p_ref != None:
@@ -77,7 +89,7 @@ def scatter_3D(samples, sample_nos, color, p_ref, save, show,
     ax = fig.add_subplot(111, projection='3d')
     ax.scatter(samples[sample_nos, 0], samples[sample_nos, 1],
             samples[sample_nos, 2], s=10, alpha=.75, linewidth=.1, c=color,
-            cmap=plt.cm.Oranges) #ax.colorbar()
+            cmap=plt.cm.Oranges)
     if p_ref != None:
         ax.scatter(p_ref[0], p_ref[1], p_ref[2], c='g')
         
@@ -96,6 +108,9 @@ def scatter_3D(samples, sample_nos, color, p_ref, save, show,
 def show_param(samples, data, rho_D=None, p_ref=None, sample_nos=None,
         save=True, show=False, lnums=None):
     """
+    TODO: add options for higher dimensional visualization or at least for
+    showing pairwise or tripletwise parameter point clouds
+
     Plot samples in parameter space and colors them either by rho_D or by
     sample batch number.
 
@@ -132,6 +147,9 @@ def show_param(samples, data, rho_D=None, p_ref=None, sample_nos=None,
 def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
         save=True, show=False, Q_nums=None):
     """
+    TODO: add options for higher dimensional visualization or at least for
+    showing pairwise or tripletwise sample point clouds
+
     Plot samples in data space and colors them either by rho_D or by
     sample batch number.
 
@@ -163,12 +181,15 @@ def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
                 zlabel, savename)
 
 def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
-        img_folder='figs/', ref_markers=['^', 's', 'o'],
-        ref_colors=['r', 'g', 'b']):
-    """
+        img_folder='figs/', ref_markers=None,
+        ref_colors=None):
+    r"""
+    TODO: add option for showing all pairs or altering which index for which
+    all pairs are shown
+
     Plot the data domain D using a triangulation based on the generating
-    samples where $Q={q_1, q_i}$ for ``i=Q_nums``, with a marker for various
-    ``Q_ref``. 
+    samples where :math:`Q={q_1, q_i}` for ``i=Q_nums``, with a marker for
+    various :math:`Q_{ref}`. 
 
     :param samples: Samples to plot
     :type samples: :class:`~numpy.ndarray` of shape (ndim, num_samples)
@@ -178,11 +199,14 @@ def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
     :type Q_ref: :class:`np.ndarray`
     :param list Q_nums: dimensions of the QoI to plot
     :param string img_folder: folder to save the plots to
-    :param list ref_markers: list of marker types for ``Q_ref``
-    :param list ref_colors: list of colors for ``Q_ref``
+    :param list ref_markers: list of marker types for :math:`Q_{ref}`
+    :param list ref_colors: list of colors for :math:`Q_{ref}`
 
     """
-    
+    if ref_markers == None:
+        ref_markers = markers
+    if ref_colors == None:
+        ref_colors = colors
     if  Q_nums == None:
         Q_nums = range(data.shape[1])
 
@@ -204,13 +228,13 @@ def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
             ref_markers, ref_colors, ylabel=r'$q_{'+str(i+1)+r'}$',
             triangles=triangles, save=True, show=False, filenames=filenames)
 
-def show_data_domain_2D(samples, data, Q_ref, ref_markers=['^', 's', 'o'],
-        ref_colors=['r', 'g', 'b'], xlabel=r'$q_1$', ylabel=r'$q_2',
+def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
+        ref_colors=None, xlabel=r'$q_1$', ylabel=r'$q_2',
         triangles=None, save=True, show=True, filenames=None):
-    """
+    r"""
     Plot the data domain D using a triangulation based on the generating
-    samples with a marker for various ``Q_ref``. Assumes that the first
-    dimension of data is $q_1$.
+    samples with a marker for various :math:`Q_{ref}`. Assumes that the first
+    dimension of data is :math:`q_1`.
 
     :param samples: Samples to plot
     :type samples: :class:`~numpy.ndarray` of shape (ndim, num_samples)
@@ -218,8 +242,8 @@ def show_data_domain_2D(samples, data, Q_ref, ref_markers=['^', 's', 'o'],
     :type data: :class:`np.ndarray`
     :param Q_ref: reference data value
     :type Q_ref: :class:`np.ndarray`
-    :param list ref_markers: list of marker types for ``Q_ref``
-    :param list ref_colors: list of colors for ``Q_ref``
+    :param list ref_markers: list of marker types for :math:`Q_{ref}`
+    :param list ref_colors: list of colors for :math:`Q_{ref}`
     :param string xlabel: x-axis label
     :param string ylabel: y-axis label
     :param triangles: triangulation defined by ``samples``
@@ -229,7 +253,10 @@ def show_data_domain_2D(samples, data, Q_ref, ref_markers=['^', 's', 'o'],
     :param list filenames: file names for the unmarked and marked domain plots
 
     """
-
+    if ref_markers == None:
+        ref_markers = markers
+    if ref_colors == None:
+        ref_colors = colors
     if triangles == None:
         triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
         triangles = triangulation.triangles
diff --git a/bet/postProcess/plotP.py b/bet/postProcess/plotP.py
index 25c367fe..f4317420 100644
--- a/bet/postProcess/plotP.py
+++ b/bet/postProcess/plotP.py
@@ -1,12 +1,17 @@
 """
 This module provides methods for plotting probabilities. 
 """
+
+# NOTE: We need nbins+1 because of plotting considerations.
+""" 
+TODO: Make sure plots are transparent when saving them. Copy or emulate code
+for saving the plots from plotDomains.py
+"""
+
 from bet.Comm import *
 import matplotlib.pyplot as plt
 import numpy as np
-import copy
-import math
-
+import copy, math
 
 def plot_voronoi_probs(P_samples, samples, lam_domain, nbins=20,
         plot_surface=False):
@@ -32,6 +37,7 @@ def plot_voronoi_probs(P_samples, samples, lam_domain, nbins=20,
         num_samples = samples.shape[0]
         #Add fake samples outside of lam_domain to close Voronoi 
         #tesselations at infinity
+    # TODO: finish this method
 
 def calculate_1D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
         
@@ -57,11 +63,16 @@ def calculate_1D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
     # Make list of bins if only an integer is given
     if isinstance(nbins, int):
         nbins = nbins*np.ones(num_dim, dtype=np.int)
-  
+ 
+    """TODO: determine if np.histogram or np.searchsorted is faster and change
+    to appropiate method. 
+    """
+
     # Create bins
     bins = []
     for i in range(num_dim):
-        bins.append(np.linspace(lam_domain[i][0], lam_domain[i][1], nbins[i]+1))
+        bins.append(np.linspace(lam_domain[i][0], lam_domain[i][1],
+            nbins[i]+1))
     bin_ptr = np.zeros((num_samples, num_dim), dtype=np.int)
     # Bin samples
     for j in range(num_dim):
@@ -76,7 +87,7 @@ def calculate_1D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
         for k in range(num_samples):
             marg[bin_ptr[k][i]] += P_samples[k]
         marg_temp = np.copy(marg)
-        comm.Allreduce([marg, MPI.DOUBLE],[marg_temp, MPI.DOUBLE], op=MPI.SUM)
+        comm.Allreduce([marg, MPI.DOUBLE], [marg_temp, MPI.DOUBLE], op=MPI.SUM)
         marginals[i] = marg_temp[:-1]
 
     return (bins, marginals)
@@ -105,7 +116,10 @@ def calculate_2D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
     # Make list of bins if only an integer is given
     if isinstance(nbins, int):
         nbins = nbins*np.ones(num_dim, dtype=np.int)
-    
+    """TODO: determine if np.histogram or np.searchsorted is faster and change
+    to appropiate method. 
+    """
+
     # Create bins
     bins = []
     for i in range(num_dim):
@@ -121,13 +135,13 @@ def calculate_2D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
     for i in range(num_dim):
         for j in range(i+1, num_dim):
             marg = np.zeros((nbins[i]+1, nbins[j]+1))
-            # This may be sped up with logical indices
+            # TODO: This may be sped up with logical indices
             for k in range(num_samples):
                 marg[bin_ptr[k][i]][bin_ptr[k][j]] += P_samples[k]
             marg_temp = np.copy(marg)
-            comm.Allreduce([marg, MPI.DOUBLE],[marg_temp, MPI.DOUBLE], op=MPI.SUM)
-            marginals[(i, j)] = marg_temp[:-1,:-1]
-
+            comm.Allreduce([marg, MPI.DOUBLE], [marg_temp, MPI.DOUBLE],
+                    op=MPI.SUM) 
+            marginals[(i, j)] = marg_temp[:-1, :-1]
     return (bins, marginals)
 
 def plot_1D_marginal_probs(marginals, bins, lam_domain,
@@ -139,8 +153,9 @@ def plot_1D_marginal_probs(marginals, bins, lam_domain,
     input probability measure defined by P_samples on a 1D  grid.
 
     :param marginals: 1D marginal probabilities
-    :type marginals: dictionary with int as keys and :class:'~numpy.ndarray' of shape (nbins+1,) as values
-    :param bins: Endpoints of bins used in calculating marginals
+    :type marginals: dictionary with int as keys and :class:'~numpy.ndarray' of
+        shape (nbins+1,) as values :param bins: Endpoints of bins used in
+        calculating marginals
     :type bins: :class:'~numpy.ndarray' of shape (nbins+1,)
     :param lam_domain: The domain for each parameter for the model.
     :type lam_domain: :class:'~numpy.ndarray' of shape (ndim, 2)
@@ -154,28 +169,28 @@ def plot_1D_marginal_probs(marginals, bins, lam_domain,
     :type lambda_label: list of length nbins of strings or None
 
     """
-    from scipy.interpolate import interp1d
-    from scipy.integrate import quad
-    from matplotlib import cm
     if rank == 0:
         index = copy.deepcopy(marginals.keys())
         index.sort()
         for i in index:
-            x_range = np.linspace(lam_domain[i,0], lam_domain[i,1], len(bins[i])-1)
+            x_range = np.linspace(lam_domain[i, 0], lam_domain[i, 1],
+                    len(bins[i])-1) 
             fig = plt.figure(i)
             ax = fig.add_subplot(111)
-            ax.plot(x_range,marginals[i]/(bins[i][1]-bins[i][0]))
+            ax.plot(x_range, marginals[i]/(bins[i][1]-bins[i][0]))
             if lam_ref != None:
                 ax.plot(lam_ref[i], 0.0, 'ko', markersize=10)
             if lambda_label == None:
-                label1 = '$\lambda_{' + `i+1` + '}$'
+                label1 = r'$\lambda_{' + str(i+1) + '}$'
             else:
                 label1 = lambda_label[i]
             ax.set_xlabel(label1) 
             ax.set_ylabel(r'$\rho$')
-            fig.savefig(filename + "_1D_" + `i` + ".eps")
+            fig.savefig(filename + "_1D_" + str(i) + ".eps")
             if interactive:
                 plt.show()
+            else:
+                plt.close()
 
 def plot_2D_marginal_probs(marginals, bins, lam_domain,
         filename="file", lam_ref=None, plot_surface=False, interactive=True,
@@ -186,7 +201,8 @@ def plot_2D_marginal_probs(marginals, bins, lam_domain,
     input probability measure defined by P_samples on a rectangular grid.
 
     :param marginals: 2D marginal probabilities
-    :type marginals: dictionary with tuples of 2 integers as keys and :class:'~numpy.ndarray' of shape (nbins+1,) as values
+    :type marginals: dictionary with tuples of 2 integers as keys and
+        :class:'~numpy.ndarray' of shape (nbins+1,) as values 
     :param bins: Endpoints of bins used in calculating marginals
     :type bins: :class:'~numpy.ndarray' of shape (nbins+1,2)
     :param lam_domain: The domain for each parameter for the model.
@@ -212,26 +228,32 @@ def plot_2D_marginal_probs(marginals, bins, lam_domain,
             fig = plt.figure(k)
             ax = fig.add_subplot(111)
             boxSize = (bins[i][1]-bins[i][0])*(bins[j][1]-bins[j][0])
-            quadmesh = ax.imshow(marginals[(i, j)].transpose()/boxSize, interpolation='bicubic', cmap=cm.jet, extent = [lam_domain[i][0], lam_domain[i][1], lam_domain[j][0],
-                                                                                                                      lam_domain[j][1]],origin='lower', vmax=marginals[(i, j)].max()/boxSize, vmin=marginals[(i, j)].min()/boxSize, aspect='auto')
-
+            quadmesh = ax.imshow(marginals[(i, j)].transpose()/boxSize,
+                    interpolation='bicubic', cmap=cm.jet, 
+                    extent=[lam_domain[i][0], lam_domain[i][1],
+                    lam_domain[j][0], lam_domain[j][1]], origin='lower',
+                    vmax=marginals[(i, j)].max()/boxSize, vmin=marginals[(i,
+                        j)].min()/boxSize, aspect='auto')
             if lam_ref != None:
                 ax.plot(lam_ref[i], lam_ref[j], 'ko', markersize=10)
             if lambda_label == None:
-                label1 = '$\lambda_{' + `i+1` + '}$'
-                label2 = '$\lambda_{' + `j+1` + '}$'
+                label1 = r'$\lambda_{' + str(i+1) + '}$'
+                label2 = r'$\lambda_{' + str(j+1) + '}$'
             else:
                 label1 = lambda_label[i]
                 label2 = lambda_label[j]
             ax.set_xlabel(label1) 
             ax.set_ylabel(label2)
-            label_cbar = r'$\rho_{' + '\lambda_{' + `i+1` + '}' + ',' + '\lambda_{' + `j+1` + '}' + '}$ (Lesbesgue)'
+            label_cbar = r'$\rho_{\lambda_{' + str(i+1) + '}, ' 
+            label_cbar += r'\lambda_{' + str(j+1) + '}' + '}$ (Lesbesgue)'
             fig.colorbar(quadmesh, ax=ax, label=label_cbar)
             plt.axis([lam_domain[i][0], lam_domain[i][1], lam_domain[j][0],
                 lam_domain[j][1]]) 
-            fig.savefig(filename + "_2D_" + `i` + "_" + `j` + ".eps")
+            fig.savefig(filename + "_2D_" + str(i) + "_" + str(j) + ".eps")
             if interactive:
                 plt.show()
+            else:
+                plt.close()
  
         if plot_surface:
             for k, (i, j) in enumerate(pairs):
@@ -245,22 +267,25 @@ def plot_2D_marginal_probs(marginals, bins, lam_domain,
                         antialiased=False)
                 ax.zaxis.set_major_locator(LinearLocator(10))
                 ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
-                ax.set_xlabel('$\lambda_{' + `i+1` + '}$') 
-                ax.set_ylabel('$\lambda_{' + `j+1` + '}$')
-                ax.set_zlabel('$P$')
+                ax.set_xlabel(r'$\lambda_{' + str(i+1) + '}$') 
+                ax.set_ylabel(r'$\lambda_{' + str(j+1) + '}$')
+                ax.set_zlabel(r'$P$')
                 plt.backgroundcolor = 'w'
                 fig.colorbar(surf, shrink=0.5, aspect=5, label=r'$P$')
-                fig.savefig(filename + "_surf_"+ `i` + "_" +`j` + ".eps")
+                fig.savefig(filename + "_surf_"+str(i)+"_"+str(j)+".eps")
                 if interactive:
                     plt.show()
+                else:
+                    plt.close()
 
-def smooth_marginals_1D(marginals, bins,  sigma=10.0):
+def smooth_marginals_1D(marginals, bins, sigma=10.0):
     """
     This function smooths 1D marginal probabilities.
 
     :param marginals: 1D marginal probabilities
-    :type marginals: dictionary with int as keys and :class:'~numpy.ndarray' of shape (nbins+1,) as values
-    :param bins: Endpoints of bins used in calculating marginals
+    :type marginals: dictionary with int as keys and :class:'~numpy.ndarray' of
+        shape (nbins+1,) as values :param bins: Endpoints of bins used in
+        calculating marginals
     :type bins: :class:'~numpy.ndarray' of shape (nbins+1,)
     :param sigma: Smoothing parameter in each direction.
     :type sigma: :float or :class:'~numpy.ndarray' of shape (ndim,)
@@ -270,7 +295,7 @@ def smooth_marginals_1D(marginals, bins,  sigma=10.0):
     from scipy.fftpack import fftshift, ifft, fft 
 
     if isinstance(sigma, float):
-        sigma = sigma*np.ones(len(bins),dtype=np.int)
+        sigma = sigma*np.ones(len(bins), dtype=np.int)
     marginals_smooth = {}
     index = copy.deepcopy(marginals.keys())
     index.sort()
@@ -286,20 +311,21 @@ def smooth_marginals_1D(marginals, bins,  sigma=10.0):
         aug_kernel[augx:augx+nx] = kernel
         aug_marginals[augx:augx+nx] = marginals[i]
 
-        aug_kernel=fftshift(aug_kernel)       
+        aug_kernel = fftshift(aug_kernel)       
 
         aug_marginals_smooth = np.real(ifft(fft(aug_kernel)*fft(aug_marginals)))
         marginals_smooth[i] = aug_marginals_smooth[augx:augx+nx]
-        marginals_smooth[i] =   marginals_smooth[i]/np.sum(marginals_smooth[i])
+        marginals_smooth[i] = marginals_smooth[i]/np.sum(marginals_smooth[i])
 
     return marginals_smooth
 
-def smooth_marginals_2D(marginals, bins,  sigma=10.0):
+def smooth_marginals_2D(marginals, bins, sigma=10.0):
     """
     This function smooths 2D marginal probabilities.
 
     :param marginals: 2D marginal probabilities
-    :type marginals: dictionary with tuples of 2 integers as keys and :class:'~numpy.ndarray' of shape (nbins+1,) as values
+    :type marginals: dictionary with tuples of 2 integers as keys and
+        :class:'~numpy.ndarray' of shape (nbins+1,) as values 
     :param bins: Endpoints of bins used in calculating marginals
     :type bins: :class:'~numpy.ndarray' of shape (nbins+1,)
     :param sigma: Smoothing parameter in each direction.
@@ -310,10 +336,11 @@ def smooth_marginals_2D(marginals, bins,  sigma=10.0):
     from scipy.fftpack import fftshift, ifft2, fft2 
 
     if isinstance(sigma, float):
-        sigma = sigma*np.ones(len(bins),dtype=np.int)
+        sigma = sigma*np.ones(len(bins), dtype=np.int)
     marginals_smooth = {}
     pairs = copy.deepcopy(marginals.keys())
     pairs.sort()
+    #TODO: k is an unused variable
     for k, (i, j) in enumerate(pairs):   
         nx = len(bins[i])-1
         ny = len(bins[j])-1
@@ -328,17 +355,20 @@ def smooth_marginals_2D(marginals, bins,  sigma=10.0):
         X, Y = np.meshgrid(x_kernel, y_kernel, indexing='ij')
 
         kernel = np.exp(-(X/sigma[i])**2-(Y/sigma[j])**2)
-        aug_kernel = np.zeros((nx+2*augx,ny+2*augy))
-        aug_marginals = np.zeros((nx+2*augx,ny+2*augy))
+        aug_kernel = np.zeros((nx+2*augx, ny+2*augy))
+        aug_marginals = np.zeros((nx+2*augx, ny+2*augy))
 
-        aug_kernel[augx:augx+nx,augy:augy+ny] = kernel
-        aug_marginals[augx:augx+nx,augy:augy+ny] = marginals[(i,j)]
+        aug_kernel[augx:augx+nx, augy:augy+ny] = kernel
+        aug_marginals[augx:augx+nx, augy:augy+ny] = marginals[(i, j)]
 
-        aug_kernel=fftshift(aug_kernel,0) 
-        aug_kernel=fftshift(aug_kernel,1)
+        aug_kernel = fftshift(aug_kernel, 0) 
+        aug_kernel = fftshift(aug_kernel, 1)
 
-        aug_marginals_smooth = np.real(ifft2(fft2(aug_kernel)*fft2(aug_marginals)))
-        marginals_smooth[(i,j)] = aug_marginals_smooth[augx:augx+nx,augy:augy+ny]
-        marginals_smooth[(i,j)] =   marginals_smooth[(i,j)]/np.sum(marginals_smooth[(i,j)])
+        aug_marginals_smooth = ifft2(fft2(aug_kernel)*fft2(aug_marginals))
+        aug_marginals_smooth = np.real(aug_marginals_smooth)
+        marginals_smooth[(i, j)] = aug_marginals_smooth[augx:augx+nx, 
+                augy:augy+ny]
+        marginals_smooth[(i, j)] = marginals_smooth[(i, 
+            j)]/np.sum(marginals_smooth[(i, j)])
 
     return marginals_smooth

From 8554168d999b5fcc53b3c74538f426f2ae740746 Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Mon, 6 Apr 2015 12:34:14 -0600
Subject: [PATCH 03/85] adds todos

---
 bet/sampling/adaptiveSampling.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 8dbad904..58be8d7d 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -133,6 +133,7 @@ def run_gen(self, kern_list, rho_D, maximum, param_min, param_max,
         sort_ind = np.argsort(results_rD)
         return (results, r_step_size, results_rD, sort_ind, mean_ss)
 
+    # TODO This appears to be an unused function
     def run_reseed(self, kern_list, rho_D, maximum, param_min, param_max,
             t_kernel, savefile, initial_sample_type="lhs", criterion='center',
             reseed=3):
@@ -311,6 +312,7 @@ def generalized_chains(self, param_min, param_max, t_kernel, kern,
         # Initialize Nx1 vector Step_size = something reasonable (based on size
         # of domain and transition set type)
         # Calculate domain size
+        # TODO The following could probably be done in one line
         param_left = np.repeat([param_min], self.num_chains_pproc, 0)
         param_right = np.repeat([param_max], self.num_chains_pproc, 0)
         param_width = param_right - param_left
@@ -374,7 +376,6 @@ def generalized_chains(self, param_min, param_max, t_kernel, kern,
             mdat['data'] = data
             super(sampler, self).save(mdat, "p"+str(rank)+savefile)
 
-            # samples_old = samples_new
             MYsamples_old = samples_new
 
         # collect everything
@@ -398,7 +399,8 @@ def generalized_chains(self, param_min, param_max, t_kernel, kern,
         super(sampler, self).save(mdat, savefile)
 
         return (samples, data, all_step_ratios)
-
+        
+    #TODO MOve this function to a dev branch since it is not implemented.
     def reseed_chains(self, param_min, param_max, t_kernel, kern,
             savefile, initial_sample_type="lhs", criterion='center', reseed=1):
         """

From 89dadccaee4abed41d3443556e24189a4e139dcd Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Mon, 6 Apr 2015 13:44:31 -0600
Subject: [PATCH 04/85] removes loadBalance

---
 bet/loadBalance/__init__.py     | 12 ------------
 bet/loadBalance/lb_DIAMOND.py   |  0
 bet/loadBalance/lb_PADCIRC.py   |  5 -----
 bet/loadBalance/load_balance.py | 12 ------------
 4 files changed, 29 deletions(-)
 delete mode 100644 bet/loadBalance/__init__.py
 delete mode 100644 bet/loadBalance/lb_DIAMOND.py
 delete mode 100644 bet/loadBalance/lb_PADCIRC.py
 delete mode 100644 bet/loadBalance/load_balance.py

diff --git a/bet/loadBalance/__init__.py b/bet/loadBalance/__init__.py
deleted file mode 100644
index 949dbfe6..00000000
--- a/bet/loadBalance/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""
-This subpackage contains 
-
-* :class:`~bet.loadBalance.load_balance` an interface to control the scheduling and load
-    balancing of model runs on an HPC infrastructure
-* :mod:`~bet.loadBalance.lb_DIAMOND` implements the :class:`~bet.loadBalance.load_balance` infrastructure
-    for the DIAMOND model
-* :mod:`~bet.loadBalance.lb_PADCIRC` implements the :class:`~bet.loadBalance.load_balance` infrastructure
-    for the PADCIRC model
-
-"""
-__all__ = ['load_balance','lb_DIAMOND','lb_PADCIRC']
diff --git a/bet/loadBalance/lb_DIAMOND.py b/bet/loadBalance/lb_DIAMOND.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/bet/loadBalance/lb_PADCIRC.py b/bet/loadBalance/lb_PADCIRC.py
deleted file mode 100644
index e7fa9dd4..00000000
--- a/bet/loadBalance/lb_PADCIRC.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""
-This is a placeholder. See the ``run_framework`` subpackage of `PolyADCIRC
-<http://lcgraham.github.io/PolyADCIRC/>`_ and the examples in
-``examples/fromADCIRCMap`` for a yet to be generalized prototype.
-"""
diff --git a/bet/loadBalance/load_balance.py b/bet/loadBalance/load_balance.py
deleted file mode 100644
index d36fc27d..00000000
--- a/bet/loadBalance/load_balance.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""
-This module handles the following
-    store HPC topology
-    set up file structure for data, parameter fields
-    compose parameter fields
-    prep computational domain(s) (model specific)
-    run model (model specific)
-    read and preprocess data (model specific)
-
-"""
-class load_balance():
-    pass

From 6fedc02569259daa9360cc0857ef4f662490b3d8 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Mon, 6 Apr 2015 14:02:38 -0600
Subject: [PATCH 05/85] audited and pylinted

---
 bet/calculateP/__init__.py   |  4 ++
 bet/calculateP/calculateP.py | 25 ++++++----
 bet/postProcess/__init__.py  |  2 +-
 bet/postProcess/postTools.py | 97 +++++++++++++++++-------------------
 bet/util.py                  |  3 ++
 5 files changed, 70 insertions(+), 61 deletions(-)

diff --git a/bet/calculateP/__init__.py b/bet/calculateP/__init__.py
index 1950ab39..62c859d3 100644
--- a/bet/calculateP/__init__.py
+++ b/bet/calculateP/__init__.py
@@ -6,5 +6,9 @@
     densities 
 * :mod:`~bet.calculateP.simpleFunP` provides methods for creating simple function
     approximations of probability densisties
+* :mod:`~bet.calculateP.voronoiHistogram` provides methods for creating the
+    generating points for cells that define a regular grid for use by
+    :meth:`numpy.histogramdd` and for determining their volumes, etc. This
+    module is only for use by :mod:`~bet.calculateP.simpleFunP`.
 """
 __all__ = ['calculateP', 'simpleFunP', 'voronoiHistogram']
diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index 9a42bf53..b55c8a08 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -38,6 +38,10 @@ def emulate_iid_lebesgue(lam_domain, num_l_emulate):
 def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
         lambda_emulate=None, d_Tree=None): 
     r"""
+
+    TODO: remove lam_domain as an argument, make sure to fix in all examples
+    and tests
+
     Calculates :math:`P_{\Lambda}(\mathcal{V}_{\lambda_{emulate}})`, the
     probability assoicated with a set of voronoi cells defined by
     ``num_l_emulate`` iid samples :math:`(\lambda_{emulate})`.
@@ -74,16 +78,11 @@ def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
     
     # Determine which emulated samples match with which model run samples
     l_Tree = spatial.KDTree(samples)
-    (tree_length, emulate_ptr) = l_Tree.query(lambda_emulate)
+    (_, emulate_ptr) = l_Tree.query(lambda_emulate)
     
     # Calculate Probabilties
     P = np.zeros((lambda_emulate.shape[0],))
     d_distr_emu_ptr = np.zeros(emulate_ptr.shape)
-    #io_ptr_inverse = np.zeros(io_ptr.shape)
-    # for i in range(rho_D_M.shape[0]): 
-    #     Itemp = np.equal(io_ptr, i)
-    #     l_ind = np.nonzero(Itemp)
-    #     io_ptr_inverse[l_ind] = i
     d_distr_emu_ptr = io_ptr[emulate_ptr] #io_ptr_inverse[emulate_ptr] 
     for i in range(rho_D_M.shape[0]):
         Itemp = np.equal(d_distr_emu_ptr, i)
@@ -96,6 +95,10 @@ def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
 
 def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None): 
     r"""
+    
+    TODO: remove lam_domain as an argument, make sure to fix in all examples
+    and tests
+
     Calculates :math:`P_{\Lambda}(\mathcal{V}_{\lambda_{samples}})`, the
     probability assoicated with a set of voronoi cells defined by the model
     solves at :math:`(\lambda_{samples})` where the volumes of these voronoi
@@ -122,7 +125,7 @@ def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None):
 
     """
     # Calculate pointers and volumes
-    (P, lambda_emulate, io_ptr, emulate_ptr) = prob_emulated(samples, data,
+    (P, _, io_ptr, emulate_ptr) = prob_emulated(samples, data,
             rho_D_M, d_distr_samples, lam_domain, None, d_Tree)
     
     # Apply the standard MC approximation 
@@ -141,6 +144,9 @@ def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None):
 def prob_qhull(samples, data, rho_D_M, d_distr_samples,
         lam_domain, d_Tree=None): 
     r"""
+
+    TODO: FIX OR REMOVE
+
     Calculates :math:`P_{\Lambda}(\mathcal{V}_{\lambda_{emulate}})`, the
     probability assoicated with a set of voronoi cells defined by
     ``num_l_emulate`` iid samples :math:`(\lambda_{emulate})`.
@@ -170,7 +176,6 @@ def prob_qhull(samples, data, rho_D_M, d_distr_samples,
         d_Tree = spatial.KDTree(d_distr_samples)
         
     # Determine which inputs go to which M bins using the QoI
-    #io_ptr = dsearchn(d_distr_samples, data);
     io_ptr = d_Tree.query(data)
 
     # Calcuate the bounding region for the parameters
@@ -224,7 +229,7 @@ def prob_qhull(samples, data, rho_D_M, d_distr_samples,
 
 def prob_mc(samples, data, rho_D_M, d_distr_samples,
         lam_domain, lambda_emulate=None, d_Tree=None): 
-    """
+    r"""
     Calculates :math:`P_{\Lambda}(\mathcal{V}_{\lambda_{samples}})`, the
     probability assoicated with a set of voronoi cells defined by the model
     solves at :math:`(\lambda_{samples})` where the volumes of these voronoi
@@ -260,7 +265,7 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
     # Apply the standard MC approximation to determine the number of emulated
     # samples per model run sample. This is for approximating 
     # \mu_Lambda(A_i \intersect b_j)
-    lam_vol = np.zeros((samples.shape[0],)) #lambda_emulate),))
+    lam_vol = np.zeros((samples.shape[0],)) 
     for i in range(samples.shape[0]):
         lam_vol[i] = np.sum(np.equal(emulate_ptr, i))
     clam_vol = np.copy(lam_vol) 
diff --git a/bet/postProcess/__init__.py b/bet/postProcess/__init__.py
index bfe467f5..3fc54256 100644
--- a/bet/postProcess/__init__.py
+++ b/bet/postProcess/__init__.py
@@ -4,7 +4,7 @@
 * :class:`~bet.postProcess.plotP` plots :math:`P` and/or volumes (:math:`\mu`)
     of voronoi cells
 * :class:`~bet.postProcess.plotDomains` plots the data domain
-    :math:`\mathcal{D} in 2D
+    :math:`\mathcal{D}` in 2D
 * :class:`~bet.postProcess.postTools` has tools for postprocessing
 
 """
diff --git a/bet/postProcess/postTools.py b/bet/postProcess/postTools.py
index 7690b96b..db0ebf3a 100644
--- a/bet/postProcess/postTools.py
+++ b/bet/postProcess/postTools.py
@@ -1,18 +1,18 @@
 """
+TODO: rewrite to match what the module acutally does
+
 This module provides methods for plotting probabilities. 
 """
-from bet.Comm import *
+from bet.Comm import rank
 import numpy as np
-import copy
-import math
 import scipy.io as sio
 
 
 def sort_by_rho(P_samples, samples, lam_vol=None, data=None):
     """
-    This sorts the samples by probability density. It returns the sorted values.
-    If the samples are iid, no volume data is needed. It is optional to sort the QoI 
-    data, but be sure to do so if using it later.
+    This sorts the samples by probability density. It returns the sorted
+    values.  If the samples are iid, no volume data is needed. It is optional
+    to sort the QoI data, but be sure to do so if using it later.
 
     :param P_samples: Probabilities.
     :type P_samples: :class:'~numpy.ndarray' of shape (num_samples,)
@@ -26,13 +26,13 @@ def sort_by_rho(P_samples, samples, lam_vol=None, data=None):
     :returns: (P_samples, samples, lam_vol, data)
 
     """
-    nnz = np.sum(P_samples>0)
+    nnz = np.sum(P_samples > 0)
     if lam_vol == None:
         indices = np.argsort(P_samples)[::-1][0:nnz]
     else:
         indices = np.argsort(P_samples/lam_vol)[::-1][0:nnz]
     P_samples = P_samples[indices]
-    samples = samples[indices,:]
+    samples = samples[indices, :]
     if lam_vol != None:
         lam_vol = lam_vol[indices]
     if data != None:
@@ -40,12 +40,15 @@ def sort_by_rho(P_samples, samples, lam_vol=None, data=None):
 
     return (P_samples, samples, lam_vol, data)
 
-def sample_highest_prob(top_percentile, P_samples, samples, lam_vol=None, data=None, sort=True):
+def sample_highest_prob(top_percentile, P_samples, samples, lam_vol=None,
+        data=None, sort=True): 
     """
-    This calculates the highest probability samples whose probability sum to a given value. 
-    The number of high probability samples that sum to the value and the probabilities, 
-    samples, volumes, and data are returned. This assumes that ``P_samples``, ``samples``, 
-    ``lam_vol``, and ``data`` have all be sorted using :meth:`~bet.postProcess.sort_by_rho`.
+    This calculates the highest probability samples whose probability sum to a
+    given value.  The number of high probability samples that sum to the value
+    and the probabilities, samples, volumes, and data are returned. This
+    assumes that ``P_samples``,
+    ``samples``, ``lam_vol``, and ``data`` have all be sorted using
+    :meth:`~bet.postProcess.sort_by_rho`.
 
     :param top_percentile: ratio of highest probability samples to select
     :type top_percentile: float
@@ -61,29 +64,28 @@ def sample_highest_prob(top_percentile, P_samples, samples, lam_vol=None, data=N
     :returns: ( num_samples, P_samples, samples, lam_vol, data)
 
     """
-
+    # TODO: should we trust the user or should we do a quick check of part of
+    # the array to see if it's already sorted?
     if sort:
-        (P_samples, samples, lam_vol, data) = sort_by_rho(P_samples, samples, lam_vol, data)
+        (P_samples, samples, lam_vol, data) = sort_by_rho(P_samples, samples,
+                lam_vol, data)
 
     P_sum = np.cumsum(P_samples)
     num_samples = np.sum(P_sum <= top_percentile)
     P_samples = P_samples[0:num_samples]
-    samples = samples[0:num_samples,:]
+    samples = samples[0:num_samples, :]
     if lam_vol != None:
         lam_vol = lam_vol[0:num_samples]
     if data != None:
-        data = data[0:num_samples,:]
+        data = data[0:num_samples, :]
         
     return  (num_samples, P_samples, samples, lam_vol, data)
     
-
-def save_parallel_probs_csv(P_samples,
-                            samples,
-                            P_file,
-                            lam_file,
-                            compress=False):
+def save_parallel_probs_csv(P_samples, samples, P_file, lam_file,
+        compress=False):
     """
-    Saves probabilites and samples from parallel runs in individual .csv files for each process.
+    Saves probabilites and samples from parallel runs in individual .csv files
+    for each process.
 
     :param P_samples: Probabilities.
     :type P_samples: :class:'~numpy.ndarray' of shape (num_samples,)
@@ -102,16 +104,14 @@ def save_parallel_probs_csv(P_samples,
     else:
         suffix = '.csv'
 
-    np.savetxt(P_file + `rank` + suffix ,P_samples, delimiter = ',')
-    np.savetxt(lam_file + `rank` + suffix ,samples, delimiter = ',')
+    np.savetxt(P_file + str(rank) + suffix, P_samples, delimiter=',')
+    np.savetxt(lam_file + str(rank) + suffix, samples, delimiter=',')
 
-def collect_parallel_probs_csv(P_file,
-                               lam_file,
-                               num_files,
-                               save = False,
-                               compress=False):
+def collect_parallel_probs_csv(P_file, lam_file, num_files, save=False,
+        compress=False):
     """
-    Collects probabilities and samples saved in .csv format from parallel runs into single arrays.
+    Collects probabilities and samples saved in .csv format from parallel runs
+    into single arrays.
 
     :param P_file: file prefix for probabilities
     :type P_file: str
@@ -133,9 +133,9 @@ def collect_parallel_probs_csv(P_file,
 
     P = np.loadtxt(P_file + '0' + suffix)
     lam = np.loadtxt(lam_file + '0' + suffix)
-    for i in range(1,num_files):
-        P = np.vstack((P, np.loadtxt(P_file + `i` + suffix)))
-        lam = np.vstack((lam,np.loadtxt(lam_file + `i` + suffix)))
+    for i in range(1, num_files):
+        P = np.vstack((P, np.loadtxt(P_file + str(i) + suffix)))
+        lam = np.vstack((lam, np.loadtxt(lam_file + str(i) + suffix)))
 
     if save:
         np.savetxt(P_file + 'all' + suffix, P)
@@ -143,12 +143,10 @@ def collect_parallel_probs_csv(P_file,
 
     return (P, lam)
 
-def save_parallel_probs_mat(P_samples,
-                            samples,
-                            file_prefix,
-                            compress=False):
+def save_parallel_probs_mat(P_samples, samples, file_prefix, compress=False):
     """
-    Saves probabilites and samples from parallel runs in individual .mat files for each process.
+    Saves probabilites and samples from parallel runs in individual .mat files
+    for each process.
 
     :param P_samples: Probabilities.
     :type P_samples: :class:'~numpy.ndarray' of shape (num_samples,)
@@ -158,16 +156,15 @@ def save_parallel_probs_mat(P_samples,
     :type file_prefix: str
     :returns: None
     """
-    file_dict={"P_samples": P_samples,
+    file_dict = {"P_samples": P_samples,
                "samples": samples}
-    sio.savemat(file_prefix + `rank` , file_dict, do_compression=compress)
+    sio.savemat(file_prefix + str(rank), file_dict, do_compression=compress)
 
-def collect_parallel_probs_mat(file_prefix,
-                               num_files,
-                               save = False,
-                               compress = False):
+def collect_parallel_probs_mat(file_prefix, num_files, save=False,
+       compress=False):
     """
-    Collects probabilities and samples saved in .mat format from parallel runs into single arrays.
+    Collects probabilities and samples saved in .mat format from parallel runs
+    into single arrays.
 
     :param file_prefix: file prefix 
     :type file_prefix: str
@@ -183,10 +180,10 @@ def collect_parallel_probs_mat(file_prefix,
     file_dict = sio.io.loadmat(file_prefix + "0")
     P = file_dict["P_samples"]
     lam = file_dict["samples"]
-    for i in range(1,num_files):
-        file_dict = sio.io.loadmat(file_prefix + `i`)
+    for i in range(1, num_files):
+        file_dict = sio.io.loadmat(file_prefix + str(i))
         P = np.vstack((P, file_dict["P_samples"]))
-        lam = np.vstack((lam,file_dict["samples"]))
+        lam = np.vstack((lam, file_dict["samples"]))
 
     if save:
         file_dict = {"P_samples": P,
diff --git a/bet/util.py b/bet/util.py
index 1a10a57b..89456bf9 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -46,6 +46,9 @@ def meshgrid_ndim(X):
     
 def get_global_values(array):
     """
+    TODO: write a version that also works with captial mpi4py method calls
+    (optional?)
+
     Concatenates local arrays into global array.
 
     :param array: Array.

From f05b24fac8d8defe03e0e5901d73475e81a07d2f Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Mon, 6 Apr 2015 17:28:51 -0600
Subject: [PATCH 06/85] added tests for Comm and util. removed tests for
 loadBalance. added markers for future tests

---
 test/test_Comm.py                             | 64 +++++++++++++++++++
 .../test_voronoiHistogram.py}                 |  0
 test/test_loadBalance/__init__.py             |  5 --
 test/test_loadBalance/test_load_balance.py    |  8 ---
 .../test_plotDomains.py                       |  0
 .../test_plotP.py                             |  0
 .../test_postTools.py}                        |  0
 test/test_util.py                             |  0
 8 files changed, 64 insertions(+), 13 deletions(-)
 create mode 100644 test/test_Comm.py
 rename test/{test_loadBalance/test_lb_DIAMOND.py => test_calculateP/test_voronoiHistogram.py} (100%)
 delete mode 100644 test/test_loadBalance/__init__.py
 delete mode 100644 test/test_loadBalance/test_load_balance.py
 rename test/{test_visualize => test_postProcess}/test_plotDomains.py (100%)
 rename test/{test_visualize => test_postProcess}/test_plotP.py (100%)
 rename test/{test_loadBalance/test_lb_PADCIRC.py => test_postProcess/test_postTools.py} (100%)
 create mode 100644 test/test_util.py

diff --git a/test/test_Comm.py b/test/test_Comm.py
new file mode 100644
index 00000000..982f1511
--- /dev/null
+++ b/test/test_Comm.py
@@ -0,0 +1,64 @@
+"""
+This module contains unittests for :mod:`~bet.Comm`
+"""
+
+import unittest
+import bet.Comm as Comm
+from pkgutil import iter_modules
+
+class Test_comm_for_no_mpi4py(unittest.TestCase):
+    """
+    Test :class:`bet.Comm.comm_for_no_mpi4py`.
+    """
+    def setUp(self):
+        self.comm = Comm.comm_for_no_mpi4py()
+    def test_Get_size(self):
+        self.assertEqual(self.comm.Get_size(),1)
+    def test_Get_rank(self):
+        self.assertEqual(self.comm.Get_rank(),0)
+    def test_allgrather(self):
+        thing = range(4)
+        self.assertEqual(self.comm.allgather(thing), thing)
+    def test_allreduce(self):
+        thing = 4
+        self.assertEqual(self.comm.allreduce(thing,op=None), thing)
+    def test_bcast(self):
+        thing = range(4)
+        self.assertEqual(self.comm.bcast(thing, root=0), thing)
+    def test_Allgather(self):
+        thing = range(4)
+        self.assertEqual(self.comm.Allgather(thing), thing)
+    def test_Allreduce(self):
+        thing1 = range(4)
+        thing2 = range(4)
+        self.assertEqual(self.comm.Allreduce(thing1, thing2,
+            op=None), thing1)
+    def test_Bcast(self):
+        thing = range(4)
+        self.assertEqual(self.comm.Bcast(thing, root=0), thing)
+    def test_Scatter(self):
+        thing1 = range(4)
+        thing2 = range(4)
+        self.assertEqual(self.comm.Scatter(thing1, thing2,
+            root=0), thing1)
+
+class Test_Comm(unittest.TestCase):
+    """
+    Test :mod:`bet.Comm`
+    """
+    def test(self):
+        if 'mpi4py' in (name for loader, name, ispkg in iter_modules()):
+            pass
+        else:
+            self.assertEqual(Comm.comm.size, 1)
+            self.assertEqual(Comm.comm.rank, 0)
+
+class Test_MPI_for_no_mpi4py(unittest.TestCase):
+    """
+    Test :class:`bet.Comm.MPI_fort_no_mpi4py`
+    """
+    def test(self):
+        MPI_no = Comm.MPI_for_no_mpi4py()
+        self.assertEqual(MPI_no.SUM, None)
+        self.assertEqual(MPI_no.DOUBLE, float)
+        self.assertEqual(MPI_no.INT, int)
diff --git a/test/test_loadBalance/test_lb_DIAMOND.py b/test/test_calculateP/test_voronoiHistogram.py
similarity index 100%
rename from test/test_loadBalance/test_lb_DIAMOND.py
rename to test/test_calculateP/test_voronoiHistogram.py
diff --git a/test/test_loadBalance/__init__.py b/test/test_loadBalance/__init__.py
deleted file mode 100644
index e373a35c..00000000
--- a/test/test_loadBalance/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""
-This subpackage contains the tests for the loadBalance subpackage. The user
-should add tests for custom :class:`bet.loadBalance` classes.
-"""
-__all__ = ['test_load_balance', 'test_lb_DIAMOND', 'test_lb_PADCIRC']
diff --git a/test/test_loadBalance/test_load_balance.py b/test/test_loadBalance/test_load_balance.py
deleted file mode 100644
index ea7d0616..00000000
--- a/test/test_loadBalance/test_load_balance.py
+++ /dev/null
@@ -1,8 +0,0 @@
-"""
-This is module provides tests for :module:`bet.loadBalance.load_balance`.
-
-    * check the dimension of the output and input
-    * output/input file presence (this is model specific...)
-    * optimal(?) feeding jobs out to the allocation correctly
-"""
-pass
diff --git a/test/test_visualize/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
similarity index 100%
rename from test/test_visualize/test_plotDomains.py
rename to test/test_postProcess/test_plotDomains.py
diff --git a/test/test_visualize/test_plotP.py b/test/test_postProcess/test_plotP.py
similarity index 100%
rename from test/test_visualize/test_plotP.py
rename to test/test_postProcess/test_plotP.py
diff --git a/test/test_loadBalance/test_lb_PADCIRC.py b/test/test_postProcess/test_postTools.py
similarity index 100%
rename from test/test_loadBalance/test_lb_PADCIRC.py
rename to test/test_postProcess/test_postTools.py
diff --git a/test/test_util.py b/test/test_util.py
new file mode 100644
index 00000000..e69de29b

From b9a63c2d31de01dac41944f118b3578b6e0ca840 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Mon, 6 Apr 2015 18:24:34 -0600
Subject: [PATCH 07/85] working on util tests

---
 bet/util.py       |  2 ++
 test/test_util.py | 43 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 45 insertions(+)

diff --git a/bet/util.py b/bet/util.py
index 89456bf9..4ea75d77 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -39,7 +39,9 @@ def meshgrid_ndim(X):
                                       alist[9],
                                       indexing='ij')
 
+    # TODO: use x.flat instead of x.flat[:]
     X_new = np.vstack((a.flat[:],b.flat[:],c.flat[:],d.flat[:],e.flat[:],f.flat[:],g.flat[:],h.flat[:],i.flat[:],j.flat[:])).transpose()
+    # TODO: I don't think this line does anything, remove it
     X_new  = X_new[:,0:n]
     
     return X_new
diff --git a/test/test_util.py b/test/test_util.py
index e69de29b..d5cab9f7 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -0,0 +1,43 @@
+"""
+This module contains unittests for :mod:`~bet.util`
+"""
+
+import unittest
+import bet.util as util
+from bet.Comm import *
+import numpy.testing as nptest
+import numpy as np
+
+def get_binary_rep(i, dim):
+    short = bin(i).paritition('b')[-1]
+    full = '0'*(dim-len(short))+short
+
+def compare_to_bin_rep(xnew):
+    """
+    xnew[i] == binaryformof(i, dim) where binaryformof(i, dim) returns a list
+    of length dim of 1s and 0s corresponding to the binary represenation of i
+    """
+    for i, row in enumerate(xnew):
+        row_rep = ''
+        for v in row:
+            row_rep += str(v)
+        assert row_rep == get_binary_rep(i, len(row))
+
+def test_meshgrid_ndim():
+    """
+    Tests :meth:`bet.util.meshgrid_ndim` for 10 vectors where each vector is
+    equal to ``[0, 1]``.
+    """
+    for i in xrange(10):
+        x = [[0,1,] for v in xrange(i)]
+        yield compare_to_bin_rep, util.meshgrid_ndim(x)
+
+def test_get_global_values():
+    """
+    Tests :meth:`bet.util.get_global_values`.
+    """
+    original_array = np.array(range(rank*2))
+    my_array = comm.scatter(original_array, root=0)
+    recomposed_array = util.get_global_values(my_array)
+    nptest.assert_array_equal(original_array, recomposed_array)
+

From 7a21421ca0772aae8144f70136e916b6194886d8 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Mon, 6 Apr 2015 23:06:06 -0600
Subject: [PATCH 08/85] working on util tests

---
 test/test_util.py | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/test/test_util.py b/test/test_util.py
index d5cab9f7..47c54df4 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -7,33 +7,43 @@
 from bet.Comm import *
 import numpy.testing as nptest
 import numpy as np
+from pkgutil import iter_modules
 
 def get_binary_rep(i, dim):
-    short = bin(i).paritition('b')[-1]
+    short = bin(i).partition('b')[-1]
     full = '0'*(dim-len(short))+short
+    print full
 
 def compare_to_bin_rep(xnew):
     """
     xnew[i] == binaryformof(i, dim) where binaryformof(i, dim) returns a list
     of length dim of 1s and 0s corresponding to the binary represenation of i
     """
+    rep_compare = np.zeros((xnew.shape[0],), np.bool)
     for i, row in enumerate(xnew):
         row_rep = ''
         for v in row:
             row_rep += str(v)
-        assert row_rep == get_binary_rep(i, len(row))
+        print row_rep
+        rep_compare[i] = (row_rep == get_binary_rep(i, len(row)))
+    assert np.all(rep_compare)
 
 def test_meshgrid_ndim():
     """
     Tests :meth:`bet.util.meshgrid_ndim` for 10 vectors where each vector is
     equal to ``[0, 1]``.
     """
-    for i in xrange(10):
+    for i in xrange(2):
         x = [[0,1,] for v in xrange(i)]
         yield compare_to_bin_rep, util.meshgrid_ndim(x)
 
+@unittest.skipIf(size == 1 and 'mpi4py' in (name for loader, name, 
+    ispkg in iter_modules()), 
+    "This test only runs in parallel. If mpi4py is installed")
 def test_get_global_values():
     """
+    TODO: Separate out parallel tests so that they are run in parallel if
+    mpi4py is installed
     Tests :meth:`bet.util.get_global_values`.
     """
     original_array = np.array(range(rank*2))

From 4862a927aff5a567e5165852d0a9cae66c8214d1 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Tue, 7 Apr 2015 09:46:53 -0600
Subject: [PATCH 09/85] finished util tests

---
 bet/util.py       |  2 +-
 test/test_util.py | 35 +++++++++++++++++++++--------------
 2 files changed, 22 insertions(+), 15 deletions(-)

diff --git a/bet/util.py b/bet/util.py
index 4ea75d77..c2f431eb 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -51,7 +51,7 @@ def get_global_values(array):
     TODO: write a version that also works with captial mpi4py method calls
     (optional?)
 
-    Concatenates local arrays into global array.
+    Concatenates local arrays into global array using :meth:`np.vstack`.
 
     :param array: Array.
     :type P_samples: :class:'~numpy.ndarray' 
diff --git a/test/test_util.py b/test/test_util.py
index 47c54df4..35bdfb16 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -10,44 +10,51 @@
 from pkgutil import iter_modules
 
 def get_binary_rep(i, dim):
+    """
+    A ``dim`` bit representation of ``i`` in binary.
+
+    :param int i: number to represent in binary
+    :param int dim: number of bits to use in the representation
+    :rtype: string
+    :returns: string representation of binary represenation of i
+
+    """
     short = bin(i).partition('b')[-1]
     full = '0'*(dim-len(short))+short
-    print full
+    return full
 
 def compare_to_bin_rep(xnew):
     """
-    xnew[i] == binaryformof(i, dim) where binaryformof(i, dim) returns a list
-    of length dim of 1s and 0s corresponding to the binary represenation of i
+    xnew[i] == get_binar_rep(i, dim)     
     """
     rep_compare = np.zeros((xnew.shape[0],), np.bool)
     for i, row in enumerate(xnew):
         row_rep = ''
         for v in row:
             row_rep += str(v)
-        print row_rep
         rep_compare[i] = (row_rep == get_binary_rep(i, len(row)))
+        print rep_compare[i]
     assert np.all(rep_compare)
 
 def test_meshgrid_ndim():
     """
-    Tests :meth:`bet.util.meshgrid_ndim` for 10 vectors where each vector is
+    Tests :meth:`bet.util.meshgrid_ndim` for upto 10 vectors where each vector is
     equal to ``[0, 1]``.
     """
-    for i in xrange(2):
-        x = [[0,1,] for v in xrange(i)]
+    for i in xrange(10):
+        x = [[0,1] for v in xrange(i+1)]
         yield compare_to_bin_rep, util.meshgrid_ndim(x)
 
-@unittest.skipIf(size == 1 and 'mpi4py' in (name for loader, name, 
-    ispkg in iter_modules()), 
-    "This test only runs in parallel. If mpi4py is installed")
 def test_get_global_values():
     """
-    TODO: Separate out parallel tests so that they are run in parallel if
-    mpi4py is installed
     Tests :meth:`bet.util.get_global_values`.
     """
-    original_array = np.array(range(rank*2))
-    my_array = comm.scatter(original_array, root=0)
+    original_array = np.array([range(size*2)])
+    if rank > 0:
+        my_index = range(0+rank, len(original_array), size)
+    else:
+        my_index = range(0+rank, len(original_array), size)
+    my_array = original_array[my_index]
     recomposed_array = util.get_global_values(my_array)
     nptest.assert_array_equal(original_array, recomposed_array)
 

From c136fc06746fa352391c2e6c7ffdfb4f9092c5bb Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Tue, 7 Apr 2015 14:44:17 -0600
Subject: [PATCH 10/85] adds tests for calcP and modifies util

---
 bet/calculateP/calculateP.py                |  18 +-
 bet/util.py                                 |   7 +-
 setup.py                                    |   2 +-
 test/test_calculateP/test_calculateP.py     | 830 +++++++++-----------
 test/test_calculateP/test_calculateP_old.py | 558 +++++++++++++
 5 files changed, 938 insertions(+), 477 deletions(-)
 create mode 100644 test/test_calculateP/test_calculateP_old.py

diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index b55c8a08..0a1ba807 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -64,7 +64,8 @@ def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
     :returns: (P, lambda_emulate, io_ptr, emulate_ptr, lam_vol)
 
     """
-    
+    if len(data.shape) == 1:
+        data = np.expand_dims(data, axis=1) 
     if lambda_emulate == None:
         lambda_emulate = samples
     if len(d_distr_samples.shape) == 1:
@@ -124,20 +125,26 @@ def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None):
         a pointer from emulated samples to samples (in parameter space)
 
     """
+    if len(data.shape) == 1:
+        data = np.expand_dims(data, axis=1) 
+
     # Calculate pointers and volumes
     (P, _, io_ptr, emulate_ptr) = prob_emulated(samples, data,
             rho_D_M, d_distr_samples, lam_domain, None, d_Tree)
     
     # Apply the standard MC approximation 
-    lam_vol = np.ones((samples.shape[0],))
+    lam_vol = np.ones((samples.shape[0],1))
     # Calculate Probabilities
     P = np.zeros((samples.shape[0],))
+    # TODO Fix issue of unecessary mpi call
+
     for i in range(rho_D_M.shape[0]):
         Itemp = np.equal(io_ptr, i)
         Itemp_sum = np.sum(lam_vol[Itemp])
         Itemp_sum = comm.allreduce(Itemp_sum, op=MPI.SUM)
         if Itemp_sum > 0:
             P[Itemp] = rho_D_M[i]*lam_vol[Itemp]/Itemp_sum 
+    lam_vol = (1.0/float(samples.shape[0]))*np.ones((samples.shape[0],1))
 
     return (P, lam_vol, io_ptr, emulate_ptr)
 
@@ -213,7 +220,7 @@ def prob_qhull(samples, data, rho_D_M, d_distr_samples,
     
     # Calculate the Voronoi diagram for samples. Calculate the volumes of 
     # the convex hulls of the corresponding Voronoi regions.
-    lam_vol = np.zeros((samples.shape[-1],))
+    lam_vol = np.zeros((samples.shape[-1],1))
     for i in range((samples.shape[0])):
         vornoi = spatial.Voronoi(lam_bound)
         lam_vol[i] = float(pyhull.qconvex('Qt FA', vornoi.vertices).split()[-1])
@@ -257,6 +264,8 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
         a pointer from emulated samples to samples (in parameter space)
 
     """
+    if len(data.shape) == 1:
+        data = np.expand_dims(data, axis=1) 
     # Calculate pointers and volumes
     (P, lambda_emulate, io_ptr, emulate_ptr) = prob_emulated(samples,
             data, rho_D_M, d_distr_samples, lam_domain, lambda_emulate,
@@ -265,7 +274,7 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
     # Apply the standard MC approximation to determine the number of emulated
     # samples per model run sample. This is for approximating 
     # \mu_Lambda(A_i \intersect b_j)
-    lam_vol = np.zeros((samples.shape[0],)) 
+    lam_vol = np.zeros((samples.shape[0],1)) 
     for i in range(samples.shape[0]):
         lam_vol[i] = np.sum(np.equal(emulate_ptr, i))
     clam_vol = np.copy(lam_vol) 
@@ -274,6 +283,7 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
     lam_vol = lam_vol/(len(lambda_emulate)*size)
 
     # Calculate Probabilities
+    # TODO Fix issue of unecessary mpi call
     P = np.zeros((samples.shape[0],))
     for i in range(rho_D_M.shape[0]):
         Itemp = np.equal(io_ptr, i)
diff --git a/bet/util.py b/bet/util.py
index 89456bf9..378c3130 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -56,6 +56,9 @@ def get_global_values(array):
     :rtype: :class:'~numpy.ndarray' 
     :returns: array
     """
-
+    dir=len(array.shape)
     array = comm.allgather(array, array)   
-    return np.vstack(array)
+    if dir==1:
+        return np.hstack(array)
+    else:
+        return np.vstack(array)
diff --git a/setup.py b/setup.py
index cab40391..c5eba602 100644
--- a/setup.py
+++ b/setup.py
@@ -10,4 +10,4 @@
       author = 'Steven Mattis',
       author_email ='steve.a.mattis@gmail.com',
       url= 'https://github.com/smattis/BET',
-      packages =['bet', 'bet.sampling', 'bet.calculateP', 'bet.postProcess', 'bet.loadBalance'])
+      packages =['bet', 'bet.sampling', 'bet.calculateP', 'bet.postProcess'])
diff --git a/test/test_calculateP/test_calculateP.py b/test/test_calculateP/test_calculateP.py
index 721b18e6..e99dab33 100644
--- a/test/test_calculateP/test_calculateP.py
+++ b/test/test_calculateP/test_calculateP.py
@@ -1,10 +1,7 @@
-# Lindley Graham 05/22/2014
-
+# Steven Mattis and Lindley Graham 04/06/2015
 """
 This module contains tests for :module:`bet.caculcateP.calculateP`.
 
-    * compare using same lambda_emulated (iid and regular grid)
-    * compare using different lambda_emulated
 
 Most of these tests should make sure certain values are within a tolerance
 rather than exact due to the stocastic nature of the algorithms being tested.
@@ -12,16 +9,20 @@
 
 import unittest
 import bet.calculateP.calculateP as calcP
-import numpy sa np
+import bet.calculateP.simpleFunP as simpleFunP
+import numpy as np
 import scipy.spatial as spatial
 import numpy.testing as nptest
+import bet.util as util
+from bet.Comm import *
+
 
 class TestEmulateIIDLebesgue(unittest.TestCase):
     """
     Test :meth:`bet.calculateP.calculateP.emulate_iid_lebesgue`.
     """
     
-    def runTest(self):
+    def setUp(self):
         """
         Test dimension, number of samples, and that all the samples are within
         lambda_domain.
@@ -30,529 +31,418 @@ def runTest(self):
         lam_left = np.array([0.0, .25, .4])
         lam_right = np.array([1.0, 4.0, .5])
 
-        lam_domain = np.zeros((3,3))
-        lam_domain[:,0] = lam_left
-        lam_domain[:,1] = lam_right
-
-        num_l_emulate = 1e6
-
-        lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
+        self.lam_domain = np.zeros((3,3))
+        self.lam_domain[:,0] = lam_left
+        self.lam_domain[:,1] = lam_right
 
-        # check the dimension
-        np.assert_array_equal(lambda_emulate.shape, (3, num_l_emulate))
+        self.num_l_emulate = 1000000
 
-        # check that the samples are all within the correct bounds
-        np.assertGreaterEqual(0.0, np.min(lambda_emulate[0, :]))
-        np.assertGreaterEqual(.25, np.min(lambda_emulate[1, :]))
-        np.assertGreaterEqual(.4, np.min(lambda_emulate[2, :]))
-        np.assertLessEqual(1.0, np.max(lambda_emulate[0, :]))
-        np.assertLessEqual(4.0, np.max(lambda_emulate[1, :]))
-        np.assertLessEqual(.5, np.max(lambda_emulate[2, :]))
-
-# make sure probabilties and lam_vol follow the MC assumption (i.e. for uniform
-# over the entire space they should all be the same, the hyperrectangle case
-# will be different)
+        self.lambda_emulate = calcP.emulate_iid_lebesgue(self.lam_domain, self.num_l_emulate)
+ 
+    def test_dimension(self):
+        """
+        Check the dimension.
+        """
+        nptest.assert_array_equal(self.lambda_emulate.shape, (int(self.num_l_emulate/size)+1,3))
 
-# compare the P calculated by the different methods on the same samples 
+    def test_bounds(self):
+        """
+        Check that the samples are all within the correct bounds
+        """
+        self.assertGreaterEqual(np.min(self.lambda_emulate[:, 0]),0.0)
+        self.assertGreaterEqual(np.min(self.lambda_emulate[:,1]), 0.25)
+        self.assertGreaterEqual(np.min(self.lambda_emulate[:, 2]), 0.4)
+        self.assertLessEqual(np.max(self.lambda_emulate[:, 0]),1.0)
+        self.assertLessEqual(np.max(self.lambda_emulate[:,1]), 4.0)
+        self.assertLessEqual(np.max(self.lambda_emulate[:,2]), 0.5)
 
-# add a skip thing to the tests involving qhull so that it only runs if that
-# package is installed
+class TestProbMethod_3to2(unittest.TestCase):
+    """
+    Sets up 3 to 2 map problem.
+    """
+    def setUp(self):
+        self.samples = np.loadtxt("datafiles/3to2_samples.txt.gz")
+        self.data = np.loadtxt("datafiles/3to2_data.txt.gz")
+        Q_ref =  np.array([0.422, 0.9385])
+        (self.d_distr_prob, self.d_distr_samples, self.d_Tree) = simpleFunP.uniform_hyperrectangle(data=self.data,Q_ref=Q_ref, bin_ratio=0.2, center_pts_per_edge = 1)
+        self.lam_domain= np.array([[0.0, 1.0],
+                                   [0.0, 1.0],
+                                   [0.0, 1.0]])
+        import numpy.random as rnd
+        rnd.seed(1)
+        self.lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain=self.lam_domain, 
+                                                  num_l_emulate = 1000)
+class Test_prob_3to2(TestProbMethod_3to2):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob` on 3 to 2 map.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(Test_prob_3to2, self).setUp()
+        (self.P, self.lam_vol , _ , _ ) = calcP.prob(samples=self.samples,
+                                                     data=self.data,
+                                                     rho_D_M = self.d_distr_prob,
+                                                     d_distr_samples = self.d_distr_samples,
+                                                     lam_domain = self.lam_domain,
+                                                     d_Tree = self.d_Tree)
+        self.P_ref = np.loadtxt("datafiles/3to2_prob.txt.gz")
 
-# test where P is uniform over the entire domain
-# test where P is uniform over a hyperrectangle subdomain
-# test with and without optional arguments, together and separatly
-# compare using same lambda_emulated (iid and regular grid)
-# compare using different lambda_emulated
-# some will need to be within a tolerance and some will need to be exact
-# also tolerances will need to be depend on if the lambda_emulated are the same
-# and if there voronoi cells are the same, etc.
-# test on a linear model and maybe a non-linear, read from file model
+    def test_prob_sum_to_1(self):
+        """
+        Test to see if the prob. sums to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
+    def test_P_matches_true(self):
+        """
+        Test against reference probs. (Only in serial)
+        """
+        if size == 1:
+            nptest.assert_almost_equal(self.P_ref,self.P)
+    def test_vol_sum_to_1(self):
+        """
+        Test that volume ratios sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
+    def test_prob_pos(self):
+        """
+        Test that all probs are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)
 
-class TestProb(object):
+class Test_prob_emulated_3to2(TestProbMethod_3to2):
     """
-    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP` with a linear
-    model.
+    Test :meth:`bet.calculateP.calculateP.prob_emulated` on a 3 to 2 map.
     """
-    @classmethod
-    def setUpClass(cls):
-        """
-        Create inputs for ``prob*`` methods. This should run only once per
-        grouping of tests from this class. But, for isolated tests it doesn't
-        make any sense to do it this way. Since many of these tests are
-        comparision tests it should be fine to structure things in this manner
-        rather using inheritance everywhere.
-        """
-        # Create model associated inputs (samples, data, lam_domain)
-        data_domain = np.array([[0.0, 1.0], [-1.0, 1.0], [-1.0, 0.0]])
-        self.lam_domain = np.array([[.1, .2], [3, 4], [50, 60]])
-        # iid samples
-        self.u_samples = None #calcP.emulate_iid_lebesgue(lam_domain,
-                #20**lam_domain.shape[0])
-        # regular grid samples
-        lam1 = np.linspace(lam_domain[0, 0], lam_domain[0, 1], 20)
-        lam2 = np.linspace(lam_domain[1, 0], lam_domain[1, 1], 20)
-        lam3 = np.linspace(lam_domain[2, 0], lam_domain[2, 1], 20)
-        lam1, lam2, lam3 = np.meshgrid(lam1, lam2, lam3)
-        self.r_samples = np.column_stack((lam1.ravel(), lam2.ravel(), lam3.ravel()))
-    
-        # Create lambda_emulate
-        # iid
-        self.u_lambda_emulate = None #calcP.emulate_iid_lebesgue(lam_domain,
-            1e6)
-        # regular grid
-        lam1 = np.linspace(lam_domain[0, 0], lam_domain[0, 1], 100)
-        lam2 = np.linspace(lam_domain[1, 0], lam_domain[1, 1], 100)
-        lam3 = np.linspace(lam_domain[2, 0], lam_domain[2, 1], 100)
-        lam1, lam2, lam3 = np.meshgrid(lam1, lam2, lam3)
-        self.r_lambda_emulate = np.column_stack((lam1.ravel(), lam2.ravel(),
-            lam3.ravel()))
-
-    def compare_to_vol(result_vol, lambda_domain):
+    def setUp(self):
+        """
+        Set up 3 to 2 map.
         """
-        Compare lambda_vol from the algorithm to an analytic solution.
+        super(Test_prob_emulated_3to2, self).setUp()
+        (self.P_emulate, self.lambda_emulate, _ , _) = calcP.prob_emulated(samples=self.samples,
+                                                              data=self.data,
+                                                              rho_D_M = self.d_distr_prob,
+                                                              d_distr_samples = self.d_distr_samples,
+                                                              lam_domain = self.lam_domain,
+                                                              lambda_emulate = self.lambda_emulate,
+                                                              d_Tree = self.d_Tree)
+        self.P_emulate_ref=np.loadtxt("datafiles/3to2_prob_emulated.txt.gz")
+        self.P_emulate = util.get_global_values(self.P_emulate)
 
-        :param result_vol: lambda_vol from any of the methods in
-            :mod:`~bet.calculatevol.calculatevol`
-        :type result_vol: :class:`numpy.ndarray`
+    def test_P_sum_to_1(self):
+        """
+        Test that prob. sums to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P_emulate),1.0)
+    def test_P_matches_true(self):
+        """
+        Test that probabilites match reference values.
+        """
+        if size == 1:
+            nptest.assert_almost_equal(self.P_emulate_ref,self.P_emulate)
+    def test_prob_pos(self):
+        """
+        Test that all probabilites are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P_emulate,0)),0)
 
+class Test_prob_mc_3to2(TestProbMethod_3to2):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob_mc` on a 3 to 2 map.
+    """
+    def setUp(self):
         """
-        lambda_vol = np.product(lambda_domain[:,1]-lambda_domain[:,0])
-        lambda_vol = lambda_vol / float(len(result_vol))
-        nptest.assert_array_equal(result_vol,
-                np.ones(result_vol.shape)*lambda_vol)
-        
-    def compare_to_vol_ae(result_vol, lambda_domain):
+        Set up 3 to 2 problem.
         """
-        Compare ``lambda_vol`` from the algorithm to an analytic solution.
+        super(Test_prob_mc_3to2, self).setUp()
+        (self.P, self.lam_vol , _ , _, _) = calcP.prob_mc(samples=self.samples,
+                                                           data=self.data,
+                                                           rho_D_M = self.d_distr_prob,
+                                                           d_distr_samples = self.d_distr_samples,
+                                                           lam_domain = self.lam_domain,
+                                                           lambda_emulate = self.lambda_emulate,
+                                                           d_Tree = self.d_Tree)
+        self.P_ref = np.loadtxt("datafiles/3to2_prob_mc.txt.gz")
 
-        :param result_vol: lambda_vol from any of the methods in
-            :mod:`~bet.calculatevol.calculatevol`
-        :type result_vol: :class:`numpy.ndarray`
+    def test_P_sum_to_1(self):
+        """
+        Test that probs sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
+    def test_P_matches_true(self):
+        """
+        Test the probs. match reference values.
+        """
+        if size==1:
+            nptest.assert_almost_equal(self.P_ref,self.P)
+    def test_vol_sum_to_1(self):
+        """
+        Test that volume ratios sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
+    def test_prob_pos(self):
+        """
+        Test that all probs are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)
 
+class TestProbMethod_3to1(unittest.TestCase):
+    """
+    Set up 3 to 1 map problem.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.samples = np.loadtxt("datafiles/3to2_samples.txt.gz")
+        self.data = np.loadtxt("datafiles/3to2_data.txt.gz")[:,0]
+        Q_ref =  np.array([0.422])
+        (self.d_distr_prob, self.d_distr_samples, self.d_Tree) = simpleFunP.uniform_hyperrectangle(data=self.data,Q_ref=Q_ref, bin_ratio=0.2, center_pts_per_edge = 1)
+        self.lam_domain= np.array([[0.0, 1.0],
+                                   [0.0, 1.0],
+                                   [0.0, 1.0]])
+        import numpy.random as rnd
+        rnd.seed(1)
+        self.lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain=self.lam_domain, 
+                                                  num_l_emulate = 1000)
+class Test_prob_3to1(TestProbMethod_3to1):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob` on a 3 to 1 map.
+    """
+    def setUp(self):
         """
-        lambda_vol = np.product(lambda_domain[:,1]-lambda_domain[:,0])
-        lambda_vol = lambda_vol / float(len(result_vol))
-        nptest.assert_array_almost_equal_nulp(result_vol,
-                np.ones(result_vol.shape)*lambda_vol)
+        Set up problem.
+        """
+        super(Test_prob_3to1, self).setUp()
+        (self.P, self.lam_vol , _ , _ ) = calcP.prob(samples=self.samples,
+                                                     data=self.data,
+                                                     rho_D_M = self.d_distr_prob,
+                                                     d_distr_samples = self.d_distr_samples,
+                                                     lam_domain = self.lam_domain,
+                                                     d_Tree = self.d_Tree)
+        self.P_ref = np.loadtxt("datafiles/3to1_prob.txt.gz")
 
-    def compare_prob_dtree(result_wtree, result_wotree):
+    def test_prob_sum_to_1(self):
+        """
+        Test that probs. sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
+    def test_P_matches_true(self):
         """
+        Test that probs match reference data.
+        """
+        if size==1:
+            nptest.assert_almost_equal(self.P_ref,self.P)
+    def test_vol_sum_to_1(self):
+        """
+        Test that volume ratios sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
+    def test_prob_pos(self):
+        """
+        Test that probs. are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)
 
-        Make sure the output from
-        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
-        option arguments.
 
+class Test_prob_emulated_3to1(TestProbMethod_3to1):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob_emulated` on a 3 to 1 map.
+    """
+    def setUp(self):
         """
-        # calculate with d_tree
-        (P, lem, io_ptr, emulate_ptr) = result_wtree
-        # calculate without d_tree
-        (Pt, lemt, io_ptrt, emulate_ptrt) = result_wotree
-        # Compare results
-        nptest.assert_array_equal(P,Pt)
-        nptest.assert_array_equal(lem, lemt)
-        nptest.assert_array_equal(lem, self.r_lambda_emulate)
-        nptest.assert_array_equal(io_ptrt, ioptr)
-        nptest.assert_array_equal(emulate_ptr, emulate_ptrt)
-
-    def compare_prob_emulate(result_wsamples, result_wosamples):
+        Set up problem.
         """
+        super(Test_prob_emulated_3to1, self).setUp()
+        (self.P_emulate, self.lambda_emulate, _ , _) = calcP.prob_emulated(samples=self.samples,
+                                                              data=self.data,
+                                                              rho_D_M = self.d_distr_prob,
+                                                              d_distr_samples = self.d_distr_samples,
+                                                              lam_domain = self.lam_domain,
+                                                              lambda_emulate = self.lambda_emulate,
+                                                              d_Tree = self.d_Tree)
+        self.P_emulate_ref=np.loadtxt("datafiles/3to1_prob_emulated.txt.gz")
+        self.P_emulate = util.get_global_values(self.P_emulate)
 
-        Make sure the output from
-        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
-        ``lambda_emulate`` when ``lambda_emulate == samples``.
+    def test_P_sum_to_1(self):
+        """
+        Test the probs sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P_emulate),1.0)
 
+    def test_P_matches_true(self):
         """
-        # calculate with samples
-        (P, lem, io_ptr, emulate_ptr) = result_wsamples
-        # calculate without samples
-        (Pt, lemt, io_ptrt, emulate_ptrt) = result_wosamples
-        # Compare results
-        nptest.assert_array_equal(P,Pt)
-        nptest.assert_array_equal(lem, lemt)
-        nptest.assert_array_equal(lem, self.r_lambda_emulate)
-        nptest.assert_array_equal(io_ptrt,ioptr)
-        nptest.assert_array_equal(emulate_ptr, emulate_ptrt)
+        Test the probs. match reference values.
+        """
+        if size==1:
+            nptest.assert_almost_equal(self.P_emulate_ref,self.P_emulate)
 
-    def compare_prob(result_emulated, result_prob, result_mc):
+    def test_prob_pos(self):
+        """
+        Test that all probs are non-negative.
         """
+        self.assertEqual(np.sum(np.less(self.P_emulate,0)),0)
 
-        Make sure that the output from 
-        :meth:`~bet.calculateP.calculateP.prob_emulated`,
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when ``lambda_emulate == samples``.
-
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
+class Test_prob_mc_3to1(TestProbMethod_3to1):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob_mc` on a 3 to 1 map.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(Test_prob_mc_3to1, self).setUp()
+        (self.P, self.lam_vol , _ , _, _) = calcP.prob_mc(samples=self.samples,
+                                                           data=self.data,
+                                                           rho_D_M = self.d_distr_prob,
+                                                           d_distr_samples = self.d_distr_samples,
+                                                           lam_domain = self.lam_domain,
+                                                           lambda_emulate = self.lambda_emulate,
+                                                           d_Tree = self.d_Tree)
+        self.P_ref = np.loadtxt("datafiles/3to1_prob_mc.txt.gz")
 
+    def test_P_sum_to_1(self):
         """
-        # Calculate prob 
-        (P, lem, io_ptr, emulate_ptr) = result_emulated
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_mc
+        Test that probs sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
 
-        # Compare results
-        nptest.assert_array_equal(P,P1)
-        nptest.assert_array_equal(P,P3)
-        nptest.assert_array_equal(P1,P3)
+    def test_P_matches_true(self):
+        """
+        Test that probs match reference vals.
+        """
+        if size==1:
+            nptest.assert_almost_equal(self.P_ref,self.P)
 
-        nptest.assert_array_equal(lam_vol1,lam_vol3)
+    def test_vol_sum_to_1(self):
+        """
+        Test that volume ratios sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
 
-        nptest.assert_array_equal(lem,lem1)
-        nptest.assert_array_equal(lem,lem3)
-        nptest.assert_array_equal(lem1,lem3)
+    def test_prob_pos(self):
+        """
+        Test that all probs are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)
 
-        nptest.assert_array_equal(io_ptr,io_ptr1)
-        nptest.assert_array_equal(io_ptr,io_ptr3)
-        nptest.assert_array_equal(io_ptr1,io_ptr3)
-       
-        nptest.assert_array_equal(emulate_ptr,emulate_ptr1)
-        nptest.assert_array_equal(emulate_ptr,emulate_ptr3)
-        nptest.assert_array_equal(emulate_ptr1,emulate_ptr3)
-
-    def compare_volume_rg(result_prob, result_mc):
-        """
-
-        Make sure that the voronoi cell volumes from 
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
-        all on a regular grid and ``lambda_emulate == samples``.
-
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-
-        """
-        # Calculate prob 
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_mc
-
-        nptest.assert_array_equal(lam_vol1,lam_vol3)
-        self.compare_to_mean(lam_vol1)
-        self.compare_to_vol_linear(lam_vol1)
-
-    def compare_volume_ae(result_prob, result_mc):
-        """
-
-        Make sure that the voronoi cell volumes from 
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
-        i.i.d and ``lambda_emulate == samples``.
-
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-
-        """
-        # Calculate prob 
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_mc
-
-        nptest.assert_array_equal(lam_vol1, lam_vol3)
-        self.compare_to_mean_ae(lam_vol1)
-        self.compare_to_vol_linear_ae(lam_vol1)
-
-    def compare_lambda_emulate(result_prob, result_rg, result_iid):
-        """
-        Compare results when lambda_emulate != samples
-            
-            * lambda_emulate is i.i.d. or on a regular grid
-       
-       .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-       
-        """
-        # Calculate prob (has no lambda_emulate)
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
-        # Calculate prob_mc (has lambda_emulate), regular grid
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_rg
-        # Calculate prob_mc (has lambda_emulate), iid samples
-        (P4, lam_vol4, lem4, io_ptr4, emulate_ptr4) = result_iid
-
-        # Compare results
-        nptest.assert_array_almost_equal_nulp(P1,P3)
-        nptest.assert_array_almost_equal_nulp(lam_vol1, lam_vol3)
-        
-        nptest.assert_array_almost_equal_nulp(P4,P3)
-        nptest.assert_array_almost_equal_nulp(lam_vol4, lam_vol3)
-        
-        nptest.assert_array_almost_equal_nulp(P1,P4)
-        nptest.assert_array_almost_equal_nulp(lam_vol1, lam_vol4)
-
-    def generate_results(self):
-        """
-        Generate the mix of results from
-        :meth:~`bet.calculateP.calculateP.prob_emulated`,
-        :meth:~`bet.calculateP.calculateP.prob`, and
-        :meth:~`bet.calculateP.calculateP.prob_mc` to be used in test
-        subclasses
-        """
-
-        # RESULTS WHERE SAMPLES = LAMBDA_EMULATE
-        # samples are on a regular grid
-        # result_wtree, result_wsamples, result_emulated_rg
-        self.result_emulated_rg = calcP.prob_emulated(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_samples, self.d_Tree)
-        self.result_wtree = result_emulated_rg
-        self.result_wsamples = result_emulated_rg
-        self.result_wotree = calcP.prob_emulated(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_samples)
-        self.result_wosamples = calcP.prob_emulated(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain)
-
-        self.result_prob_rg = calcP.prob(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.d_Tree)
-        self.result_mc_rg = calcP.prob_mc(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_samples, self.d_Tree)
-
-        # samples are iid
-        self.result_emulated_iid = calcP.prob_emulated(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.u_samples, self.d_Tree)
-        self.result_prob_iid = calcP.prob(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.d_Tree)
-        self.result_mc_iid = calcP.prob_mc(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.u_samples, self.d_Tree)
-
-        # RESULTS WHERE SAMPLES != LAMBDA_EMULATE
-        # result_emu_samples_emulatedsamples
-        self.result_emu_rg_rg = calcP.prob_mc(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_lambda_emulate, self.d_Tree)
-        self.result_emu_rg_iid = calcP.prob_mc(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.u_lambda_emulate, self.d_Tree)
-        self.result_emu_iid_rg = calcP.prob_mc(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_lambda_emulate, self.d_Tree)
-        self.result_emu_iid_iid = calcP.prob_mc(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.u_lambda_emulate, self.d_Tree)
-
-
-class LinearModel(CompareProb):
-    """
-    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP` with a linear
-    model.
-    """
-    @classmethod
-    def setUpClass(cls):
-        """
-        Create inputs for ``prob*`` methods. This should run only once per
-        grouping of tests from this class. But, for isolated tests it doesn't
-        make any sense to do it this way. Since many of these tests are
-        comparision tests it should be fine to structure things in this manner
-        rather using inheritance everywhere.
-        """
-        # This might not be the right invocation.
-        super(TestLinearModel, self).setUpClass(TestLinearModel)
-        # linear model
-        self.rl_data = np.dot(r_samples, data_domain)
-        self.ul_data = np.dot(u_samples, data_domain)
-
-class NonLinearModel(CompareProb):
+class TestProbMethod_10to4(unittest.TestCase):
     """
-    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP` with a linear
-    model.
+    Sets up 10 to 4 map problem.
     """
-    @classmethod
-    def setUpClass(cls):
-        """
-        Create inputs for ``prob*`` methods. This should run only once per
-        grouping of tests from this class. But, for isolated tests it doesn't
-        make any sense to do it this way. Since many of these tests are
-        comparision tests it should be fine to structure things in this manner
-        rather using inheritance everywhere.
-        """
-        # This might not be the right invocation.
-        super(NonLinearModel, self).setUpClass(NonLinearModel)
-        # non-linear model
-        def nonlinear_model(l_data):
-            n_data = l_data
-            n_data[:,0] = l_data[:,0] + l_data[:,2]
-            n_data[:,1] = np.square(l_data[:,1])
-            n_data[:,2] = l_data[:,0] - n_data[:,1]
-            return n_data
-        self.rn_data = nonlinear_model(rl_data)
-        self.un_data = nonlinear_model(ul_data)
-
-class TestProbUnifLinear(LinearModel, unittest.TestCase):
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        import numpy.random as rnd
+        rnd.seed(1)
+        self.lam_domain=np.zeros((10,2))
+        self.lam_domain[:,0]=0.0
+        self.lam_domain[:,1]=1.0
+        self.num_l_emulate = 1000
+        self.lambda_emulate = calcP.emulate_iid_lebesgue(self.lam_domain, self.num_l_emulate)
+        self.samples =  calcP.emulate_iid_lebesgue(self.lam_domain, 100)
+        self.data = np.dot(self.samples,rnd.rand(10,4))
+        Q_ref =  np.mean(self.data, axis=0)
+        (self.d_distr_prob, self.d_distr_samples, self.d_Tree) = simpleFunP.uniform_hyperrectangle(data=self.data,Q_ref=Q_ref, bin_ratio=0.2, center_pts_per_edge = 1)
+
+class Test_prob_10to4(TestProbMethod_10to4):
     """
-    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP`.
+    Test :meth:`bet.calculateP.calculateP.prob` on a 10 to 4 map.
     """
-    @classmethod
-    def setUpClass(cls):
+    def setUp(self):
         """
-        Create inputs for ``prob*`` methods. This should run only once per
-        grouping of tests from this class. But, for isolated tests it doesn't
-        make any sense to do it this way. Since many of these tests are
-        comparision tests it should be fine to structure things in this manner
-        rather using inheritance everywhere.
+        Set up problem.
         """
-        # This might not be the right invocation.
-        super(TestProbUnifLinear, self).setUpClass(TestProbUnifLinear)
-    
-        # Create rho_D_M associated inputs (rho_D_M, d_distr_samples, d_tree)
-        # UNIFORM
-        self.d_distr_dsamples = np.mean(self.data_domain, 1)
-        self.rho_D_M = 1
-        self.d_Tree = spatial.KDTree(self.d_distr_samples)
-
-        # Generate results
-        super(TestProbUnifLinear, self).generate_results(self)
+        super(Test_prob_10to4, self).setUp()
+        (self.P, self.lam_vol , _ , _ ) = calcP.prob(samples=self.samples,
+                                                     data=self.data,
+                                                     rho_D_M = self.d_distr_prob,
+                                                     d_distr_samples = self.d_distr_samples,
+                                                     lam_domain = self.lam_domain,
+                                                     d_Tree = self.d_Tree)
 
-    def compare_to_unif_linear(result_P):
+    def test_prob_sum_to_1(self):
         """
-        Compare P from the algorithm to an analytic solution where $P_\mathcal{D}$ is uniform
-        over $\mathbf{D}$ (linear model only).
-
-        :param result_P: P from any of the methods in
-            :mod:`~bet.calculateP.calculateP`
-        :type result_P: :class:`numpy.ndarray`
+        Test that probs sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
 
+    def test_vol_sum_to_1(self):
         """
-        nptest.assert_array_equal(result_P,
-                np.ones(result_P.shape)/float(len(result_P)))
-        
-    def compare_to_unif_linear_ae(result_P):
+        Test that volume ratios sum to 1.
         """
-        Compare P from the algorithm to an analytic solution where $P_\mathcal{D}$ is uniform
-        over $\mathbf{D}$ (linear model only).
-
-        :param result_P: P from any of the methods in
-            :mod:`~bet.calculateP.calculateP`
-        :type result_P: :class:`numpy.ndarray`
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
 
+    def test_prob_pos(self):
         """
-        nptest.assert_array_almost_equal_nulp(result_P,
-                np.ones(result_P.shape)/float(len(result_P)))
-
-    def test_prob_dtree(self):
+        Test that all probs are non-negative.
         """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)
 
-        Make sure the output from
-        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
-        option arguments.
-
-        """  
-        compare_prob_dtree(self.result_wtree, self.result_wotree)
-
-    def test_prob_emulate(self):
+class Test_prob_emulated_10to4(TestProbMethod_10to4):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob_emulated` on a 10 to 4 map.
+    """
+    def setUp(self):
+        """
+        Set up problem.
         """
+        super(Test_prob_emulated_10to4, self).setUp()
 
-        Make sure the output from
-        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
-        ``lambda_emulate`` when ``lambda_emulate == samples``.
+        (self.P_emulate, self.lambda_emulate, _ , _) = calcP.prob_emulated(samples=self.samples,
+                                                              data=self.data,
+                                                              rho_D_M = self.d_distr_prob,
+                                                              d_distr_samples = self.d_distr_samples,
+                                                              lam_domain = self.lam_domain,
+                                                              lambda_emulate = self.lambda_emulate,
+                                                              d_Tree = self.d_Tree)
+        self.P_emulate = util.get_global_values(self.P_emulate)
 
+    def test_P_sum_to_1(self):
         """
-        compare_prob_emulate(self.result_wsamples, self.result_wosamples)
-
-    def test_prob_rg(self):
+        Test that probs. sum to 1.
         """
+        nptest.assert_almost_equal(np.sum(self.P_emulate),1.0)
 
-        Make sure that the output from 
-        :meth:`~bet.calculateP.calculateP.prob_emulated`,
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
-        all on a regular grid when ``lambda_emulate == samples``.
-
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-
+    def test_prob_pos(self):
+        """
+        Test that all probs are non-negative.
         """
-        compare_prob(self.result_emulated_rg, self.result_prob_rg,self.result_mc_rg)
-        compare_volume_rg(self.results_prob_rg, self.result_mc_rg)
+        self.assertEqual(np.sum(np.less(self.P_emulate,0)),0)
 
-    def test_prob_iid(self):
+class Test_prob_mc_10to4(TestProbMethod_10to4):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob_mc` on a 10 to 4 map.
+    """
+    def setUp(self):
+        """
+        Set up problem.
         """
+        super(Test_prob_mc_10to4, self).setUp()
+        (self.P, self.lam_vol , _ , _, _) = calcP.prob_mc(samples=self.samples,
+                                                           data=self.data,
+                                                           rho_D_M = self.d_distr_prob,
+                                                           d_distr_samples = self.d_distr_samples,
+                                                           lam_domain = self.lam_domain,
+                                                           lambda_emulate = self.lambda_emulate,
+                                                           d_Tree = self.d_Tree)
 
-        Make sure that the output from 
-        :meth:`~bet.calculateP.calculateP.prob_emulated`,
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
-        i.i.d. with respect to the Lebesgue measure when ``lambda_emulate == samples``.
+    def test_P_sum_to_1(self):
+        """
+        Test the probs. sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
 
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
+    def test_vol_sum_to_1(self):
+        """
+        Test that volume ratios sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
 
+    def test_prob_pos(self):
         """
-        compare_prob(self.result_emulated_iid, self.result_prob_iid,
-                self.result_mc_iid)
-        compare_volume_ae(self.results_prob_iid, self.result_mc_iid)
- 
-    def test_l_emulate_rg(self):
-        """
-        Compare results when lambda_emulate != samples
-            
-            * samples are on a regular grid
-            * lambda_emulate is i.i.d. or on a regular grid
-       
-       .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-       
-        """
-        compare_lambda_emulate(self.result_prob_rg, self.result_emu_rg_rg,
-                self.result_emu_rg_iid)
-        compare_volume_ae(
-        compare_volume_ae(
-        compare_volume_ae(
-
-        # Calculate prob (has no lambda_emulate)
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = calc.prob(self.u_samples,
-                self.ul_data, self.u_rho, self.u_dsamples, self.lam_domain,
-                self.ud_tree)
-
-        # Calculate prob_mc (has lambda_emulate)
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = calc.prob_mc(self.u_samples,
-                self.ul_data, self.u_rho, self.u_dsamples, self.lam_domain,
-                self.u_lambda_emulate, self.ud_tree)
-
-        # Compare to mean
-        self.compare_to_mean_ae(P3)
-        self.compare_to_mean_ae(lam_vol3)
-
-        (P4, lam_vol4, lem4, io_ptr4, emulate_ptr4) = calc.prob_mc(self.u_samples,
-                self.ul_data, self.u_rho, self.u_dsamples, self.lam_domain,
-                self.u_lambda_emulate, self.ud_tree)
-
-        # Compare to mean
-        self.compare_to_mean_ae(P4)
-        self.compare_to_mean_ae(lam_vol4)
-
-        # Compare results
-        nptest.assert_array_almost_equal_nulp(P1,P3)
-        nptest.assert_array_almost_equal_nulp(lam_vol1,lam_vol3)
-        
-        nptest.assert_array_almost_equal_nulp(P4,P3)
-        nptest.assert_array_almost_equal_nulp(lam_vol4,lam_vol3)
-        
-        nptest.assert_array_almost_equal_nulp(P1,P4)
-        nptest.assert_array_almost_equal_nulp(lam_vol1,lam_vol4
-                )
-
-    def test_compare_to_analytic_solution(self):
-        pass
+        Test that probs. are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)
diff --git a/test/test_calculateP/test_calculateP_old.py b/test/test_calculateP/test_calculateP_old.py
new file mode 100644
index 00000000..721b18e6
--- /dev/null
+++ b/test/test_calculateP/test_calculateP_old.py
@@ -0,0 +1,558 @@
+# Lindley Graham 05/22/2014
+
+"""
+This module contains tests for :module:`bet.caculcateP.calculateP`.
+
+    * compare using same lambda_emulated (iid and regular grid)
+    * compare using different lambda_emulated
+
+Most of these tests should make sure certain values are within a tolerance
+rather than exact due to the stocastic nature of the algorithms being tested.
+"""
+
+import unittest
+import bet.calculateP.calculateP as calcP
+import numpy sa np
+import scipy.spatial as spatial
+import numpy.testing as nptest
+
+class TestEmulateIIDLebesgue(unittest.TestCase):
+    """
+    Test :meth:`bet.calculateP.calculateP.emulate_iid_lebesgue`.
+    """
+    
+    def runTest(self):
+        """
+        Test dimension, number of samples, and that all the samples are within
+        lambda_domain.
+
+        """
+        lam_left = np.array([0.0, .25, .4])
+        lam_right = np.array([1.0, 4.0, .5])
+
+        lam_domain = np.zeros((3,3))
+        lam_domain[:,0] = lam_left
+        lam_domain[:,1] = lam_right
+
+        num_l_emulate = 1e6
+
+        lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
+
+        # check the dimension
+        np.assert_array_equal(lambda_emulate.shape, (3, num_l_emulate))
+
+        # check that the samples are all within the correct bounds
+        np.assertGreaterEqual(0.0, np.min(lambda_emulate[0, :]))
+        np.assertGreaterEqual(.25, np.min(lambda_emulate[1, :]))
+        np.assertGreaterEqual(.4, np.min(lambda_emulate[2, :]))
+        np.assertLessEqual(1.0, np.max(lambda_emulate[0, :]))
+        np.assertLessEqual(4.0, np.max(lambda_emulate[1, :]))
+        np.assertLessEqual(.5, np.max(lambda_emulate[2, :]))
+
+# make sure probabilties and lam_vol follow the MC assumption (i.e. for uniform
+# over the entire space they should all be the same, the hyperrectangle case
+# will be different)
+
+# compare the P calculated by the different methods on the same samples 
+
+# add a skip thing to the tests involving qhull so that it only runs if that
+# package is installed
+
+# test where P is uniform over the entire domain
+# test where P is uniform over a hyperrectangle subdomain
+# test with and without optional arguments, together and separatly
+# compare using same lambda_emulated (iid and regular grid)
+# compare using different lambda_emulated
+# some will need to be within a tolerance and some will need to be exact
+# also tolerances will need to be depend on if the lambda_emulated are the same
+# and if there voronoi cells are the same, etc.
+# test on a linear model and maybe a non-linear, read from file model
+
+class TestProb(object):
+    """
+    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP` with a linear
+    model.
+    """
+    @classmethod
+    def setUpClass(cls):
+        """
+        Create inputs for ``prob*`` methods. This should run only once per
+        grouping of tests from this class. But, for isolated tests it doesn't
+        make any sense to do it this way. Since many of these tests are
+        comparision tests it should be fine to structure things in this manner
+        rather using inheritance everywhere.
+        """
+        # Create model associated inputs (samples, data, lam_domain)
+        data_domain = np.array([[0.0, 1.0], [-1.0, 1.0], [-1.0, 0.0]])
+        self.lam_domain = np.array([[.1, .2], [3, 4], [50, 60]])
+        # iid samples
+        self.u_samples = None #calcP.emulate_iid_lebesgue(lam_domain,
+                #20**lam_domain.shape[0])
+        # regular grid samples
+        lam1 = np.linspace(lam_domain[0, 0], lam_domain[0, 1], 20)
+        lam2 = np.linspace(lam_domain[1, 0], lam_domain[1, 1], 20)
+        lam3 = np.linspace(lam_domain[2, 0], lam_domain[2, 1], 20)
+        lam1, lam2, lam3 = np.meshgrid(lam1, lam2, lam3)
+        self.r_samples = np.column_stack((lam1.ravel(), lam2.ravel(), lam3.ravel()))
+    
+        # Create lambda_emulate
+        # iid
+        self.u_lambda_emulate = None #calcP.emulate_iid_lebesgue(lam_domain,
+            1e6)
+        # regular grid
+        lam1 = np.linspace(lam_domain[0, 0], lam_domain[0, 1], 100)
+        lam2 = np.linspace(lam_domain[1, 0], lam_domain[1, 1], 100)
+        lam3 = np.linspace(lam_domain[2, 0], lam_domain[2, 1], 100)
+        lam1, lam2, lam3 = np.meshgrid(lam1, lam2, lam3)
+        self.r_lambda_emulate = np.column_stack((lam1.ravel(), lam2.ravel(),
+            lam3.ravel()))
+
+    def compare_to_vol(result_vol, lambda_domain):
+        """
+        Compare lambda_vol from the algorithm to an analytic solution.
+
+        :param result_vol: lambda_vol from any of the methods in
+            :mod:`~bet.calculatevol.calculatevol`
+        :type result_vol: :class:`numpy.ndarray`
+
+        """
+        lambda_vol = np.product(lambda_domain[:,1]-lambda_domain[:,0])
+        lambda_vol = lambda_vol / float(len(result_vol))
+        nptest.assert_array_equal(result_vol,
+                np.ones(result_vol.shape)*lambda_vol)
+        
+    def compare_to_vol_ae(result_vol, lambda_domain):
+        """
+        Compare ``lambda_vol`` from the algorithm to an analytic solution.
+
+        :param result_vol: lambda_vol from any of the methods in
+            :mod:`~bet.calculatevol.calculatevol`
+        :type result_vol: :class:`numpy.ndarray`
+
+        """
+        lambda_vol = np.product(lambda_domain[:,1]-lambda_domain[:,0])
+        lambda_vol = lambda_vol / float(len(result_vol))
+        nptest.assert_array_almost_equal_nulp(result_vol,
+                np.ones(result_vol.shape)*lambda_vol)
+
+    def compare_prob_dtree(result_wtree, result_wotree):
+        """
+
+        Make sure the output from
+        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
+        option arguments.
+
+        """
+        # calculate with d_tree
+        (P, lem, io_ptr, emulate_ptr) = result_wtree
+        # calculate without d_tree
+        (Pt, lemt, io_ptrt, emulate_ptrt) = result_wotree
+        # Compare results
+        nptest.assert_array_equal(P,Pt)
+        nptest.assert_array_equal(lem, lemt)
+        nptest.assert_array_equal(lem, self.r_lambda_emulate)
+        nptest.assert_array_equal(io_ptrt, ioptr)
+        nptest.assert_array_equal(emulate_ptr, emulate_ptrt)
+
+    def compare_prob_emulate(result_wsamples, result_wosamples):
+        """
+
+        Make sure the output from
+        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
+        ``lambda_emulate`` when ``lambda_emulate == samples``.
+
+        """
+        # calculate with samples
+        (P, lem, io_ptr, emulate_ptr) = result_wsamples
+        # calculate without samples
+        (Pt, lemt, io_ptrt, emulate_ptrt) = result_wosamples
+        # Compare results
+        nptest.assert_array_equal(P,Pt)
+        nptest.assert_array_equal(lem, lemt)
+        nptest.assert_array_equal(lem, self.r_lambda_emulate)
+        nptest.assert_array_equal(io_ptrt,ioptr)
+        nptest.assert_array_equal(emulate_ptr, emulate_ptrt)
+
+    def compare_prob(result_emulated, result_prob, result_mc):
+        """
+
+        Make sure that the output from 
+        :meth:`~bet.calculateP.calculateP.prob_emulated`,
+        :meth:`~bet.calculateP.calculateP.prob`,
+        :meth:`~bet.calculateP.calculateP.prob_mc` matches when ``lambda_emulate == samples``.
+
+        .. note::
+            This method also needs to include
+            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
+            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
+            package installed.
+
+        """
+        # Calculate prob 
+        (P, lem, io_ptr, emulate_ptr) = result_emulated
+        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
+        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_mc
+
+        # Compare results
+        nptest.assert_array_equal(P,P1)
+        nptest.assert_array_equal(P,P3)
+        nptest.assert_array_equal(P1,P3)
+
+        nptest.assert_array_equal(lam_vol1,lam_vol3)
+
+        nptest.assert_array_equal(lem,lem1)
+        nptest.assert_array_equal(lem,lem3)
+        nptest.assert_array_equal(lem1,lem3)
+
+        nptest.assert_array_equal(io_ptr,io_ptr1)
+        nptest.assert_array_equal(io_ptr,io_ptr3)
+        nptest.assert_array_equal(io_ptr1,io_ptr3)
+       
+        nptest.assert_array_equal(emulate_ptr,emulate_ptr1)
+        nptest.assert_array_equal(emulate_ptr,emulate_ptr3)
+        nptest.assert_array_equal(emulate_ptr1,emulate_ptr3)
+
+    def compare_volume_rg(result_prob, result_mc):
+        """
+
+        Make sure that the voronoi cell volumes from 
+        :meth:`~bet.calculateP.calculateP.prob`,
+        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
+        all on a regular grid and ``lambda_emulate == samples``.
+
+        .. note::
+            This method also needs to include
+            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
+            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
+            package installed.
+
+        """
+        # Calculate prob 
+        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
+        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_mc
+
+        nptest.assert_array_equal(lam_vol1,lam_vol3)
+        self.compare_to_mean(lam_vol1)
+        self.compare_to_vol_linear(lam_vol1)
+
+    def compare_volume_ae(result_prob, result_mc):
+        """
+
+        Make sure that the voronoi cell volumes from 
+        :meth:`~bet.calculateP.calculateP.prob`,
+        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
+        i.i.d and ``lambda_emulate == samples``.
+
+        .. note::
+            This method also needs to include
+            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
+            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
+            package installed.
+
+        """
+        # Calculate prob 
+        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
+        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_mc
+
+        nptest.assert_array_equal(lam_vol1, lam_vol3)
+        self.compare_to_mean_ae(lam_vol1)
+        self.compare_to_vol_linear_ae(lam_vol1)
+
+    def compare_lambda_emulate(result_prob, result_rg, result_iid):
+        """
+        Compare results when lambda_emulate != samples
+            
+            * lambda_emulate is i.i.d. or on a regular grid
+       
+       .. note::
+            This method also needs to include
+            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
+            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
+            package installed.
+       
+        """
+        # Calculate prob (has no lambda_emulate)
+        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
+        # Calculate prob_mc (has lambda_emulate), regular grid
+        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_rg
+        # Calculate prob_mc (has lambda_emulate), iid samples
+        (P4, lam_vol4, lem4, io_ptr4, emulate_ptr4) = result_iid
+
+        # Compare results
+        nptest.assert_array_almost_equal_nulp(P1,P3)
+        nptest.assert_array_almost_equal_nulp(lam_vol1, lam_vol3)
+        
+        nptest.assert_array_almost_equal_nulp(P4,P3)
+        nptest.assert_array_almost_equal_nulp(lam_vol4, lam_vol3)
+        
+        nptest.assert_array_almost_equal_nulp(P1,P4)
+        nptest.assert_array_almost_equal_nulp(lam_vol1, lam_vol4)
+
+    def generate_results(self):
+        """
+        Generate the mix of results from
+        :meth:~`bet.calculateP.calculateP.prob_emulated`,
+        :meth:~`bet.calculateP.calculateP.prob`, and
+        :meth:~`bet.calculateP.calculateP.prob_mc` to be used in test
+        subclasses
+        """
+
+        # RESULTS WHERE SAMPLES = LAMBDA_EMULATE
+        # samples are on a regular grid
+        # result_wtree, result_wsamples, result_emulated_rg
+        self.result_emulated_rg = calcP.prob_emulated(self.r_samples, self.r_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.r_samples, self.d_Tree)
+        self.result_wtree = result_emulated_rg
+        self.result_wsamples = result_emulated_rg
+        self.result_wotree = calcP.prob_emulated(self.r_samples, self.r_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.r_samples)
+        self.result_wosamples = calcP.prob_emulated(self.r_samples, self.r_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain)
+
+        self.result_prob_rg = calcP.prob(self.r_samples, self.r_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.d_Tree)
+        self.result_mc_rg = calcP.prob_mc(self.r_samples, self.r_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.r_samples, self.d_Tree)
+
+        # samples are iid
+        self.result_emulated_iid = calcP.prob_emulated(self.u_samples, self.u_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.u_samples, self.d_Tree)
+        self.result_prob_iid = calcP.prob(self.u_samples, self.u_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.d_Tree)
+        self.result_mc_iid = calcP.prob_mc(self.u_samples, self.u_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.u_samples, self.d_Tree)
+
+        # RESULTS WHERE SAMPLES != LAMBDA_EMULATE
+        # result_emu_samples_emulatedsamples
+        self.result_emu_rg_rg = calcP.prob_mc(self.r_samples, self.r_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.r_lambda_emulate, self.d_Tree)
+        self.result_emu_rg_iid = calcP.prob_mc(self.r_samples, self.r_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.u_lambda_emulate, self.d_Tree)
+        self.result_emu_iid_rg = calcP.prob_mc(self.u_samples, self.u_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.r_lambda_emulate, self.d_Tree)
+        self.result_emu_iid_iid = calcP.prob_mc(self.u_samples, self.u_data,
+                self.rho_D_M, self.d_distr_samples, self.lam_domain,
+                self.u_lambda_emulate, self.d_Tree)
+
+
+class LinearModel(CompareProb):
+    """
+    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP` with a linear
+    model.
+    """
+    @classmethod
+    def setUpClass(cls):
+        """
+        Create inputs for ``prob*`` methods. This should run only once per
+        grouping of tests from this class. But, for isolated tests it doesn't
+        make any sense to do it this way. Since many of these tests are
+        comparision tests it should be fine to structure things in this manner
+        rather using inheritance everywhere.
+        """
+        # This might not be the right invocation.
+        super(TestLinearModel, self).setUpClass(TestLinearModel)
+        # linear model
+        self.rl_data = np.dot(r_samples, data_domain)
+        self.ul_data = np.dot(u_samples, data_domain)
+
+class NonLinearModel(CompareProb):
+    """
+    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP` with a linear
+    model.
+    """
+    @classmethod
+    def setUpClass(cls):
+        """
+        Create inputs for ``prob*`` methods. This should run only once per
+        grouping of tests from this class. But, for isolated tests it doesn't
+        make any sense to do it this way. Since many of these tests are
+        comparision tests it should be fine to structure things in this manner
+        rather using inheritance everywhere.
+        """
+        # This might not be the right invocation.
+        super(NonLinearModel, self).setUpClass(NonLinearModel)
+        # non-linear model
+        def nonlinear_model(l_data):
+            n_data = l_data
+            n_data[:,0] = l_data[:,0] + l_data[:,2]
+            n_data[:,1] = np.square(l_data[:,1])
+            n_data[:,2] = l_data[:,0] - n_data[:,1]
+            return n_data
+        self.rn_data = nonlinear_model(rl_data)
+        self.un_data = nonlinear_model(ul_data)
+
+class TestProbUnifLinear(LinearModel, unittest.TestCase):
+    """
+    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP`.
+    """
+    @classmethod
+    def setUpClass(cls):
+        """
+        Create inputs for ``prob*`` methods. This should run only once per
+        grouping of tests from this class. But, for isolated tests it doesn't
+        make any sense to do it this way. Since many of these tests are
+        comparision tests it should be fine to structure things in this manner
+        rather using inheritance everywhere.
+        """
+        # This might not be the right invocation.
+        super(TestProbUnifLinear, self).setUpClass(TestProbUnifLinear)
+    
+        # Create rho_D_M associated inputs (rho_D_M, d_distr_samples, d_tree)
+        # UNIFORM
+        self.d_distr_dsamples = np.mean(self.data_domain, 1)
+        self.rho_D_M = 1
+        self.d_Tree = spatial.KDTree(self.d_distr_samples)
+
+        # Generate results
+        super(TestProbUnifLinear, self).generate_results(self)
+
+    def compare_to_unif_linear(result_P):
+        """
+        Compare P from the algorithm to an analytic solution where $P_\mathcal{D}$ is uniform
+        over $\mathbf{D}$ (linear model only).
+
+        :param result_P: P from any of the methods in
+            :mod:`~bet.calculateP.calculateP`
+        :type result_P: :class:`numpy.ndarray`
+
+        """
+        nptest.assert_array_equal(result_P,
+                np.ones(result_P.shape)/float(len(result_P)))
+        
+    def compare_to_unif_linear_ae(result_P):
+        """
+        Compare P from the algorithm to an analytic solution where $P_\mathcal{D}$ is uniform
+        over $\mathbf{D}$ (linear model only).
+
+        :param result_P: P from any of the methods in
+            :mod:`~bet.calculateP.calculateP`
+        :type result_P: :class:`numpy.ndarray`
+
+        """
+        nptest.assert_array_almost_equal_nulp(result_P,
+                np.ones(result_P.shape)/float(len(result_P)))
+
+    def test_prob_dtree(self):
+        """
+
+        Make sure the output from
+        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
+        option arguments.
+
+        """  
+        compare_prob_dtree(self.result_wtree, self.result_wotree)
+
+    def test_prob_emulate(self):
+        """
+
+        Make sure the output from
+        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
+        ``lambda_emulate`` when ``lambda_emulate == samples``.
+
+        """
+        compare_prob_emulate(self.result_wsamples, self.result_wosamples)
+
+    def test_prob_rg(self):
+        """
+
+        Make sure that the output from 
+        :meth:`~bet.calculateP.calculateP.prob_emulated`,
+        :meth:`~bet.calculateP.calculateP.prob`,
+        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
+        all on a regular grid when ``lambda_emulate == samples``.
+
+        .. note::
+            This method also needs to include
+            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
+            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
+            package installed.
+
+        """
+        compare_prob(self.result_emulated_rg, self.result_prob_rg,self.result_mc_rg)
+        compare_volume_rg(self.results_prob_rg, self.result_mc_rg)
+
+    def test_prob_iid(self):
+        """
+
+        Make sure that the output from 
+        :meth:`~bet.calculateP.calculateP.prob_emulated`,
+        :meth:`~bet.calculateP.calculateP.prob`,
+        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
+        i.i.d. with respect to the Lebesgue measure when ``lambda_emulate == samples``.
+
+        .. note::
+            This method also needs to include
+            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
+            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
+            package installed.
+
+        """
+        compare_prob(self.result_emulated_iid, self.result_prob_iid,
+                self.result_mc_iid)
+        compare_volume_ae(self.results_prob_iid, self.result_mc_iid)
+ 
+    def test_l_emulate_rg(self):
+        """
+        Compare results when lambda_emulate != samples
+            
+            * samples are on a regular grid
+            * lambda_emulate is i.i.d. or on a regular grid
+       
+       .. note::
+            This method also needs to include
+            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
+            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
+            package installed.
+       
+        """
+        compare_lambda_emulate(self.result_prob_rg, self.result_emu_rg_rg,
+                self.result_emu_rg_iid)
+        compare_volume_ae(
+        compare_volume_ae(
+        compare_volume_ae(
+
+        # Calculate prob (has no lambda_emulate)
+        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = calc.prob(self.u_samples,
+                self.ul_data, self.u_rho, self.u_dsamples, self.lam_domain,
+                self.ud_tree)
+
+        # Calculate prob_mc (has lambda_emulate)
+        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = calc.prob_mc(self.u_samples,
+                self.ul_data, self.u_rho, self.u_dsamples, self.lam_domain,
+                self.u_lambda_emulate, self.ud_tree)
+
+        # Compare to mean
+        self.compare_to_mean_ae(P3)
+        self.compare_to_mean_ae(lam_vol3)
+
+        (P4, lam_vol4, lem4, io_ptr4, emulate_ptr4) = calc.prob_mc(self.u_samples,
+                self.ul_data, self.u_rho, self.u_dsamples, self.lam_domain,
+                self.u_lambda_emulate, self.ud_tree)
+
+        # Compare to mean
+        self.compare_to_mean_ae(P4)
+        self.compare_to_mean_ae(lam_vol4)
+
+        # Compare results
+        nptest.assert_array_almost_equal_nulp(P1,P3)
+        nptest.assert_array_almost_equal_nulp(lam_vol1,lam_vol3)
+        
+        nptest.assert_array_almost_equal_nulp(P4,P3)
+        nptest.assert_array_almost_equal_nulp(lam_vol4,lam_vol3)
+        
+        nptest.assert_array_almost_equal_nulp(P1,P4)
+        nptest.assert_array_almost_equal_nulp(lam_vol1,lam_vol4
+                )
+
+    def test_compare_to_analytic_solution(self):
+        pass

From 02de53a6a5c015224a9558eac050ab72d5a2e69e Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Tue, 7 Apr 2015 15:57:56 -0600
Subject: [PATCH 11/85] update tests and fix 1D issues

---
 bet/calculateP/calculateP.py                  |   6 +
 .../datafiles/3to1_prob.txt.gz                | Bin 0 -> 1406 bytes
 .../datafiles/3to1_prob_emulated.txt.gz       | Bin 0 -> 402 bytes
 .../datafiles/3to1_prob_mc.txt.gz             | Bin 0 -> 846 bytes
 .../datafiles/3to2_data.txt.gz                | Bin 0 -> 107831 bytes
 .../datafiles/3to2_prob.txt.gz                | Bin 0 -> 1126 bytes
 .../datafiles/3to2_prob_emulated.txt.gz       | Bin 0 -> 333 bytes
 .../datafiles/3to2_prob_mc.txt.gz             | Bin 0 -> 663 bytes
 .../datafiles/3to2_samples.txt.gz             | Bin 0 -> 162836 bytes
 test/test_calculateP/test_calculateP.py       | 120 ++++++++++++++++++
 10 files changed, 126 insertions(+)
 create mode 100644 test/test_calculateP/datafiles/3to1_prob.txt.gz
 create mode 100644 test/test_calculateP/datafiles/3to1_prob_emulated.txt.gz
 create mode 100644 test/test_calculateP/datafiles/3to1_prob_mc.txt.gz
 create mode 100644 test/test_calculateP/datafiles/3to2_data.txt.gz
 create mode 100644 test/test_calculateP/datafiles/3to2_prob.txt.gz
 create mode 100644 test/test_calculateP/datafiles/3to2_prob_emulated.txt.gz
 create mode 100644 test/test_calculateP/datafiles/3to2_prob_mc.txt.gz
 create mode 100644 test/test_calculateP/datafiles/3to2_samples.txt.gz

diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index 0a1ba807..d1ae5e93 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -64,6 +64,8 @@ def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
     :returns: (P, lambda_emulate, io_ptr, emulate_ptr, lam_vol)
 
     """
+    if len(samples.shape) == 1:
+        samples = np.expand_dims(samples, axis=1) 
     if len(data.shape) == 1:
         data = np.expand_dims(data, axis=1) 
     if lambda_emulate == None:
@@ -125,6 +127,8 @@ def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None):
         a pointer from emulated samples to samples (in parameter space)
 
     """
+    if len(samples.shape) == 1:
+        samples = np.expand_dims(samples, axis=1) 
     if len(data.shape) == 1:
         data = np.expand_dims(data, axis=1) 
 
@@ -264,6 +268,8 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
         a pointer from emulated samples to samples (in parameter space)
 
     """
+    if len(samples.shape) == 1:
+        samples = np.expand_dims(samples, axis=1) 
     if len(data.shape) == 1:
         data = np.expand_dims(data, axis=1) 
     # Calculate pointers and volumes
diff --git a/test/test_calculateP/datafiles/3to1_prob.txt.gz b/test/test_calculateP/datafiles/3to1_prob.txt.gz
new file mode 100644
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diff --git a/test/test_calculateP/datafiles/3to1_prob_emulated.txt.gz b/test/test_calculateP/datafiles/3to1_prob_emulated.txt.gz
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new file mode 100644
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diff --git a/test/test_calculateP/datafiles/3to2_samples.txt.gz b/test/test_calculateP/datafiles/3to2_samples.txt.gz
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HcmV?d00001

diff --git a/test/test_calculateP/test_calculateP.py b/test/test_calculateP/test_calculateP.py
index e99dab33..6e789bb3 100644
--- a/test/test_calculateP/test_calculateP.py
+++ b/test/test_calculateP/test_calculateP.py
@@ -446,3 +446,123 @@ def test_prob_pos(self):
         Test that probs. are non-negative.
         """
         self.assertEqual(np.sum(np.less(self.P,0)),0)
+
+class TestProbMethod_1to1(unittest.TestCase):
+    """
+    Sets up 1 to 1 map problem. Uses vectors instead of 2D arrays.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        import numpy.random as rnd
+        rnd.seed(1)
+        self.lam_domain=np.zeros((1,2))
+        self.lam_domain[0,0]=0.0
+        self.lam_domain[0,1]=1.0
+        self.num_l_emulate = 1000
+        self.lambda_emulate = calcP.emulate_iid_lebesgue(self.lam_domain, self.num_l_emulate)
+        self.samples =  rnd.rand(100,)
+        self.data = 2.0*self.samples
+        Q_ref =  np.mean(self.data, axis=0)
+        (self.d_distr_prob, self.d_distr_samples, self.d_Tree) = simpleFunP.uniform_hyperrectangle(data=self.data,Q_ref=Q_ref, bin_ratio=0.2, center_pts_per_edge = 1)
+
+class Test_prob_1to1(TestProbMethod_1to1):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob` on a 1 to 1 map.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(Test_prob_1to1, self).setUp()
+        (self.P, self.lam_vol , _ , _ ) = calcP.prob(samples=self.samples,
+                                                     data=self.data,
+                                                     rho_D_M = self.d_distr_prob,
+                                                     d_distr_samples = self.d_distr_samples,
+                                                     lam_domain = self.lam_domain,
+                                                     d_Tree = self.d_Tree)
+
+    def test_prob_sum_to_1(self):
+        """
+        Test that probs sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
+
+    def test_vol_sum_to_1(self):
+        """
+        Test that volume ratios sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
+
+    def test_prob_pos(self):
+        """
+        Test that all probs are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)
+
+class Test_prob_emulated_1to1(TestProbMethod_1to1):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob_emulated` on a 1 to 1 map.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(Test_prob_emulated_1to1, self).setUp()
+
+        (self.P_emulate, self.lambda_emulate, _ , _) = calcP.prob_emulated(samples=self.samples,
+                                                              data=self.data,
+                                                              rho_D_M = self.d_distr_prob,
+                                                              d_distr_samples = self.d_distr_samples,
+                                                              lam_domain = self.lam_domain,
+                                                              lambda_emulate = self.lambda_emulate,
+                                                              d_Tree = self.d_Tree)
+        self.P_emulate = util.get_global_values(self.P_emulate)
+
+    def test_P_sum_to_1(self):
+        """
+        Test that probs. sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P_emulate),1.0)
+
+    def test_prob_pos(self):
+        """
+        Test that all probs are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P_emulate,0)),0)
+
+class Test_prob_mc_1to1(TestProbMethod_1to1):
+    """
+    Test :meth:`bet.calculateP.calculateP.prob_mc` on a 1 to 1 map.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(Test_prob_mc_1to1, self).setUp()
+        (self.P, self.lam_vol , _ , _, _) = calcP.prob_mc(samples=self.samples,
+                                                           data=self.data,
+                                                           rho_D_M = self.d_distr_prob,
+                                                           d_distr_samples = self.d_distr_samples,
+                                                           lam_domain = self.lam_domain,
+                                                           lambda_emulate = self.lambda_emulate,
+                                                           d_Tree = self.d_Tree)
+
+    def test_P_sum_to_1(self):
+        """
+        Test the probs. sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
+
+    def test_vol_sum_to_1(self):
+        """
+        Test that volume ratios sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
+
+    def test_prob_pos(self):
+        """
+        Test that probs. are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)

From 88360890a82356c71feb843658e676474ed97e0e Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Tue, 7 Apr 2015 17:13:03 -0600
Subject: [PATCH 12/85] updated tests, fixed dimension issues in basicSampling

---
 bet/sampling/basicSampling.py            |  44 ++++--
 test/test_sampling/test_basicSampling.py | 188 ++++++++++++++++++++++-
 test/test_util.py                        |  32 +++-
 3 files changed, 243 insertions(+), 21 deletions(-)

diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index 8eca6b91..14ee91f7 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -16,6 +16,8 @@
 
 def compare_yield(sort_ind, sample_quality, run_param, column_headings=None):
     """
+    TODO: Maybe move to bet.postProcessing.postTools
+
     Compare the quality of samples where ``sample_quality`` is the measure of
     quality by which the sets of samples have been indexed and ``sort_ind`` is
     an array of the sorted indicies.
@@ -36,6 +38,8 @@ def compare_yield(sort_ind, sample_quality, run_param, column_headings=None):
 
 def in_high_prob(data, rho_D, maximum, sample_nos=None):
     """
+    TODO: Maybe move to bet.postProcessing.postTools
+
     Estimates the number of samples in high probability regions of D.
 
     :param data: Data associated with ``samples``
@@ -58,6 +62,8 @@ def in_high_prob(data, rho_D, maximum, sample_nos=None):
 
 def in_high_prob_multi(results_list, rho_D, maximum, sample_nos_list=None):
     """
+    TODO: Maybe move to bet.postProcessing.postTools
+
     Estimates the number of samples in high probability regions of D for a list
     of results.
 
@@ -96,6 +102,7 @@ def loadmat(save_file, model=None):
     # load the data from a *.mat file
     mdat = sio.loadmat(save_file)
     # load the samples
+    # TODO: calculate the number of samples rather than loading it
     if mdat.has_key('samples'):
         samples = mdat['samples']
     else:
@@ -201,18 +208,15 @@ def user_samples(self, samples, savefile, parallel=False):
         Numpy and other Python packages. Instead of reimplementing them here we
         provide sampler that utilizes user specified samples.
 
-        Note: Parallel implementation with changes ordering of
-        the samples if ``samples.shape[0]`` is not divisible by ``size``.
-
         :param samples: samples to evaluate the model at
-        :type samples: :class:`~numpy.ndarray` of shape (ndim, num_samples)
+        :type samples: :class:`~numpy.ndarray` of shape (num_smaples, ndim)
         :param string savefile: filename to save samples and data
         :param boolean parallel: Flag for parallel implementation. Uses
             lowercase ``mpi4py`` methods if ``samples.shape[0]`` is not
             divisible by ``size``. Default value is ``False``. 
         :rtype: tuple
         :returns: (``parameter_samples``, ``data_samples``) where
-            ``parameter_samples`` is np.ndarray of shape (ndim, num_samples)
+            ``parameter_samples`` is np.ndarray of shape (num_samples, ndim)
             and ``data_samples`` is np.ndarray of shape (num_samples, mdim)
 
         """
@@ -226,18 +230,38 @@ def user_samples(self, samples, savefile, parallel=False):
         if not(parallel) or size == 1:
             data = self.lb_model(samples)
         elif parallel and self.num_samples%size == 0:
-            my_samples = np.empty((samples.shape[0]/size, samples.shape[1]))
+            if len(samples.shape)  == 1:
+                my_samples = np.empty((samples.shape[0]/size, ))
+            else:
+                my_samples = np.empty((samples.shape[0]/size, samples.shape[1]))
             comm.Scatter([samples, MPI.DOUBLE], [my_samples, MPI.DOUBLE])
             my_data = self.lb_model(my_samples)
-            data = np.empty((self.num_samples, my_data.shape[1]),
-                    dtype=np.float64)
+            if len(my_data.shape) == 1:
+                data = np.empty((self.num_samples,),
+                        dtype=np.float64)
+            else:
+                data = np.empty((self.num_samples, my_data.shape[1]),
+                        dtype=np.float64)
             comm.Allgather([my_data, MPI.DOUBLE], [data, MPI.DOUBLE])
         elif parallel:
-            my_index = range(0+rank, self.num_samples, size)
-            my_samples = samples[my_index, :]
+            my_len = self.num_samples/size
+            if rank != size-1:
+                my_index = range(0+rank*my_len, (rank+1)*my_len)
+            else:
+                my_index = range(0+rank*my_len, self.num_samples)
+            if len(my_samples.shape) == 1:
+                my_samples = samples[my_index, :]
+            else:
+                my_samples = samples[my_index, :]
             my_data = self.lb_model(my_samples)
             data = util.get_global_values(my_data)
             samples = util.get_global_values(my_data)
+        
+        # if data or samples are of shape (num_samples,) expand dimensions
+        if len(samples.shape) == 1:
+            samples = np.expand_dims(samples, axis=1)
+        if len(data.shape) == 1:
+            data = np.expand_dims(data, axis=1)
 
         mdat = dict()
         self.update_mdict(mdat)
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index 171c66eb..1b42934f 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -1,7 +1,185 @@
 """
-This module contains tests for the :mod:`bet.sampling.basicSampling` module.
-
-    * check input/output dimensions and format
-    * make sure that $\lambda_i \in \Lambda$
-    * provide diagnoistic output (?)
+This module contains unittests for :mod:`~bet.util`
 """
+
+import unittest, os
+import numpy.testing as nptest
+import numpy as np
+import bet.sampling.basicSampling as bsam
+import scipy.io as sio
+
+def test_in_high_prob():
+    """
+    TODO :maybe move to test.test_postProcessing.test_postTools
+
+    Tests :meth:`bet.sampling.basicSampling.in_high_prob`
+    """
+    def rho_D(my_data):
+        return my_data/len(my_data)
+    data = np.array([0, 1, 0, 1, 1, 1])
+    maximum = np.max(rho_D(data))
+    assert 4 == bsam.in_high_prob(data, rho_D, maximum)
+    assert 3 == bsam.in_high_prob(data, rho_D, maximum, [3, 4, 5])
+    assert 2 == bsam.in_high_prob(data, rho_D, maximum, [0, 1, 2, 3])
+    assert 1 == bsam.in_high_prob(data, rho_D, maximum, [0, 2, 4])
+    assert 0 == bsam.in_high_prob(data, rho_D, maximum, [0, 2])
+
+def test_in_high_prob_multi():
+    """
+    TODO :maybe move to test.test_postProcessing.test_postTools
+
+    Tests :meth:`bet.sampling.basicSampling.in_high_prob_multi`
+    
+    """
+    def rho_D(my_data):
+        return my_data/len(my_data)
+    data1 = np.array([0, 1, 0, 1, 1, 0])
+    data2 = np.ones(data1.shape)-data1
+    maximum = np.max(rho_D(data1))
+
+    results_list = [[None, data1], [None, data2], [None, data1], [None, data2]]
+    sample_nos_list = [[3, 4, 5], [3, 4, 5], [0, 2, 4], [0, 2, 4]]
+
+    nptest.assert_array_equal(np.array([3, 1, 1, 2]),
+            bsam.in_high_prob_multi(results_list, rho_D, maximum,
+                sample_nos_list))
+    nptest.assert_array_equal(np.array([4, 2, 4, 2]),
+            bsam.in_high_prob_multi(results_list, rho_D, maximum))
+
+def test_loadmat():
+    """
+    Tests :meth:`bet.sampling.basicSampling.loadmat`
+    """
+    mdat1 = {'samples':range(5), 'data':range(5), 'num_samples':5}
+    mdat2 = {'samples':range(6), 'num_samples':6}
+    model = "this is not a model"
+
+    sio.savefile('testfile1', mdat1)
+    sio.savefile('testfile2', mdat2)
+
+    (loaded_sampler1, samples1, data1) = bsam.loadmat('testfile1')
+    nptest.assert_array_equal(samples1, range(5))
+    nptest.assert_array_equal(data1, range(5))
+    assert loaded_sampler1.num_samples == 5
+    assert loaded_sampler1.lb_model == None
+
+    (loaded_sampler2, samples2, data2) = bsam.loadmat('testfile2', model)
+    nptest.assert_array_equal(samples2, range(6))
+    nptest.assert_array_equal(data2, None)
+    assert loaded_sampler1.num_samples == 6
+    assert loaded_sampler1.lb_model == model
+    os.remove('testfile1.mat')
+    os.remove('testfile2.mat')
+
+
+class Test_basic_sampler(unittest.TestCase):
+    """
+    Test :class:`bet.sampling.basicSampling.sampler`.
+    """
+
+    def setUp(self):
+        # create 1-1 map
+        self.param_min1 = np.zeros((1, ))
+        self.param_max1 = np.zeros((1, ))
+        def map_1t1(x):
+            return np.sin(x)
+        # create 3-1 map
+        self.param_min3 = np.zeros((3, ))
+        self.param_max3 = np.ones((3, ))
+        def map_3t1(x):
+            return np.sum(x, 1)
+        # create 3-2 map
+        def map_3t2(x):
+            return np.vstack(([x[:, 0]+x[:, 1], x[:, 2]])).transpose()
+        # create 10-4 map
+        self.param_min10 = np.zeros((10, ))
+        self.param_min10 = np.ones((10, ))
+        def map_10t4(x):
+            x1 = x[:, 0] + x[:, 1]
+            x2 = x[:, 2] + x[:, 3]
+            x3 = x[:, 4] + x[:, 5]
+            x4 = np.sum(x[:, [6, 7, 8, 9]], 1)
+            return np.vstack([x1, x2, x3, x4]).transpose()
+        num_samples = 100
+        self.savefiles = ["11t11", "1t1", "3to1", "3to2", "10to4"]
+        self.models = [map_1t1, map_1t1, map_3t1, map_3t2, map_10t4]
+        self.samplers = []
+        for model in self.models:
+            self.samplers.append(bsam.sampler(model, num_samples))
+
+    def tearDown(self):
+        for f in self.savefiles:
+            if os.path.exists(f+".mat"):
+                os.remove(f+".mat")
+
+    def test_init(self):
+        """
+        Test initalization of :class:`bet.sampling.basicSampling.sampler`
+        """
+        assert self.samplers[0].num_samples == 100
+        assert self.samplers[0].model == self.models[0]
+        assert bsam.sampler(self.models[0], None).num_samples == None
+
+    @unittest.skip("Skipping testing saving")
+    def test_save(self):
+        """
+        """
+        pass
+
+    def test_update(self):
+        """
+        Test :meth:`bet.sampling.basicSampling.sampler.save`
+        """
+        mdict = {"frog":3, "moose":2}
+        self.samplers[0].update_mdict(mdict)
+        assert self.samplers[0].num_samples == mdict["num_samples"]
+
+    def test_user_samples(self):
+        """
+        Test :meth:`bet.sampling.basicSampling.sampler.random_samples` for
+        three different QoI maps (1 to 1, 3 to 1, 3 to 2, 10 to 4).
+        """
+        # create a list of different sets of samples
+        list_of_samples = [np.ones((4,)), np.ones((4,1)), np.ones((4,3)),
+                np.ones((4,3)), np.ones((4,10))]
+        
+        test_list = zip(self.models, self.samplers, list_of_samples, 
+                self.savefiles)
+        
+        for model, sampler, samples, savefile in test_list: 
+            for parallel in [False, True]:
+                yield verify_user_samples, model, sampler, samples, savefile,
+                parallel
+
+    def verify_user_samples(model, sampler, samples, savefile, parallel):
+        # evalulate the model at the samples directly
+        data = model(samples)
+
+        # evaluate the model at the samples
+        (my_samples, my_data) = sampler.user_samples(samples, savefile,
+                parallel)
+
+        if len(data.shape) == 1:
+            data = np.expand_dims(data, axis=1)
+        if len(samples.shape) == 1:
+            samples = np.expan_dims(samples, axis=1)
+        
+        # compare the samples
+        nptest.assert_array_equal(samples, my_samples)
+        # compare the data
+        nptest.assert_array_equal(data, my_data)
+        # did num_samples get updated?
+        assert samples.shape[0] == sampler.num_samples
+        # did the file get correctly saved?
+
+        mdat = sio.loadmat('savefile')
+        nptest.assert_array_equal(samples, mdat['samples'])
+        nptest.assert_array_equal(data, mdat['data'])
+        assert samples.shape[0] == sampler.num_samples
+
+
+
+
+
+        
+
diff --git a/test/test_util.py b/test/test_util.py
index 35bdfb16..8c27a6f1 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -7,7 +7,6 @@
 from bet.Comm import *
 import numpy.testing as nptest
 import numpy as np
-from pkgutil import iter_modules
 
 def get_binary_rep(i, dim):
     """
@@ -47,14 +46,35 @@ def test_meshgrid_ndim():
 
 def test_get_global_values():
     """
+    TODO: make sure the newer version of util matches this test
+
     Tests :meth:`bet.util.get_global_values`.
     """
-    original_array = np.array([range(size*2)])
-    if rank > 0:
-        my_index = range(0+rank, len(original_array), size)
+    for i in xrange(5):
+        yield compare_get_global_values, i
+
+def compare_get_global_values(i):
+    """
+    Compares the results of get global values for a vector of shape ``(size*2,
+    i)``.
+    
+    :param int i: Dimension of the vector of length ``size*2``
+
+    """
+    if rank == 0:
+        if i == 0:
+            original_array = np.array(np.random.random((size*2,)))
+        else:
+            original_array = np.array(np.random.random((size*2,i)))
+    else:
+        original_array = None
+    original_array = comm.bcast(original_array)
+    my_len = original_array.shape[0]/size
+    my_index = range(0+rank*my_len, (rank+1)*my_len)
+    if i == 0:
+        my_array = original_array[my_index]
     else:
-        my_index = range(0+rank, len(original_array), size)
-    my_array = original_array[my_index]
+        my_array = original_array[my_index,:]
     recomposed_array = util.get_global_values(my_array)
     nptest.assert_array_equal(original_array, recomposed_array)
 

From e731976a1ab90bd7c55f1d776d646980d02ce320 Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Tue, 7 Apr 2015 22:38:34 -0600
Subject: [PATCH 13/85] adds tests for plotP

---
 bet/postProcess/plotP.py                      |  15 +-
 .../test_plotDomains.py                       |   0
 test/test_postProcess/test_plotP.py           | 183 ++++++++++++++++++
 test/test_visualize/test_plotP.py             |   0
 4 files changed, 196 insertions(+), 2 deletions(-)
 rename test/{test_visualize => test_postProcess}/test_plotDomains.py (100%)
 create mode 100644 test/test_postProcess/test_plotP.py
 delete mode 100644 test/test_visualize/test_plotP.py

diff --git a/bet/postProcess/plotP.py b/bet/postProcess/plotP.py
index f4317420..9209e937 100644
--- a/bet/postProcess/plotP.py
+++ b/bet/postProcess/plotP.py
@@ -57,6 +57,8 @@ def calculate_1D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
     :returns: (bins, marginals)
 
     """
+    if len(samples.shape) == 1:
+        samples = np.expand_dims(samples, axis=1)
     num_samples = samples.shape[0]
     num_dim = samples.shape[1]
 
@@ -71,13 +73,17 @@ def calculate_1D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
     # Create bins
     bins = []
     for i in range(num_dim):
-        bins.append(np.linspace(lam_domain[i][0], lam_domain[i][1],
+        bins.append(np.linspace(lam_domain[i][0]-np.finfo(float).eps, lam_domain[i][1]+np.finfo(float).eps,
             nbins[i]+1))
     bin_ptr = np.zeros((num_samples, num_dim), dtype=np.int)
     # Bin samples
     for j in range(num_dim):
         bin_ptr[:, j] = np.searchsorted(bins[j], samples[:, j])
     bin_ptr -= 1
+    bins = []
+    for i in range(num_dim):
+        bins.append(np.linspace(lam_domain[i][0], lam_domain[i][1],
+                                nbins[i]+1))
          
     # Calculate marginal probabilities 
     marginals = {}
@@ -110,6 +116,7 @@ def calculate_2D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
     :returns: (bins, marginals)
 
     """
+
     num_samples = samples.shape[0]
     num_dim = samples.shape[1]
 
@@ -123,12 +130,16 @@ def calculate_2D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
     # Create bins
     bins = []
     for i in range(num_dim):
-        bins.append(np.linspace(lam_domain[i][0], lam_domain[i][1], nbins[i]+1))
+        bins.append(np.linspace(lam_domain[i][0]-np.finfo(float).eps, lam_domain[i][1]+np.finfo(float).eps, nbins[i]+1))
     bin_ptr = np.zeros((num_samples, num_dim), dtype=np.int)
     # Bin samples
     for j in range(num_dim):
         bin_ptr[:, j] = np.searchsorted(bins[j], samples[:, j])
     bin_ptr -= 1
+
+    bins = []
+    for i in range(num_dim):
+        bins.append(np.linspace(lam_domain[i][0], lam_domain[i][1], nbins[i]+1))
          
     # Calculate marginal probabilities 
     marginals = {}
diff --git a/test/test_visualize/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
similarity index 100%
rename from test/test_visualize/test_plotDomains.py
rename to test/test_postProcess/test_plotDomains.py
diff --git a/test/test_postProcess/test_plotP.py b/test/test_postProcess/test_plotP.py
new file mode 100644
index 00000000..1d3285a8
--- /dev/null
+++ b/test/test_postProcess/test_plotP.py
@@ -0,0 +1,183 @@
+# Steven Mattis 04/07/2015
+"""
+This module contains tests for :module:`bet.postProcess.plotP`.
+
+
+Tests for correct computation of marginals and plotting.
+"""
+
+import unittest
+import bet.calculateP.calculateP as calcP
+import bet.calculateP.simpleFunP as simpleFunP
+import bet.postProcess.plotP as plotP
+import numpy as np
+import scipy.spatial as spatial
+import numpy.testing as nptest
+import bet.util as util
+from bet.Comm import *
+
+class Test_calc_marg_1D(unittest.TestCase):
+    """
+    Test :meth:`bet.postProcess.plotP.calculate_1D_marginal_probs`
+    for a 1D parameter space.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.lam_domain=np.array([[0.0,1.0]])
+        num_samples=1000
+        self.samples = np.linspace(self.lam_domain[0][0], self.lam_domain[0][1], num_samples+1)
+        self.P_samples = (1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
+        
+    def test_1_bin(self):
+        """
+        Test that marginals sum to 1 and have correct shape.
+        """
+        (bins, marginals) = plotP.calculate_1D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 1)
+        nptest.assert_almost_equal(marginals[0][0], 1.0)
+        nptest.assert_equal(marginals[0].shape, (1,))
+
+    def test_10_bins(self):
+        """
+        Test that marginals sum to 1 and have correct shape.
+        """
+        (bins, marginals) = plotP.calculate_1D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 10)
+        nptest.assert_almost_equal(np.sum(marginals[0]), 1.0)
+        nptest.assert_equal(marginals[0].shape, (10,))
+
+class Test_calc_marg_2D(unittest.TestCase):
+    """
+    Test :meth:`bet.postProcess.plotP.calculate_1D_marginal_probs` and  :meth:`bet.postProcess.plotP.calculate_2D_marginal_probs` for a 2D
+    parameter space.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.lam_domain=np.array([[0.0,1.0],[0.0,1.0]])
+        self.samples=util.meshgrid_ndim((np.linspace(self.lam_domain[0][0], self.lam_domain[0][1], 10),np.linspace(self.lam_domain[1][0], self.lam_domain[1][1], 10)))
+        self.P_samples = (1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
+        
+    def test_1_bin_1D(self):
+        """ 
+        Test that 1D marginals sum to 1 and have right shape.
+        """
+        (bins, marginals) = plotP.calculate_1D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 1)
+        
+        nptest.assert_almost_equal(marginals[0][0], 1.0)
+        nptest.assert_almost_equal(marginals[1][0], 1.0)
+        nptest.assert_equal(marginals[0].shape, (1,))
+        nptest.assert_equal(marginals[1].shape, (1,))
+
+    def test_10_bins_1D(self):
+        """ 
+        Test that 1D marginals sum to 1 and have right shape.
+        """
+        (bins, marginals) = plotP.calculate_1D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 10)
+        nptest.assert_almost_equal(np.sum(marginals[0]), 1.0)
+        nptest.assert_almost_equal(np.sum(marginals[1]), 1.0)
+        nptest.assert_equal(marginals[0].shape, (10,))
+
+    def test_1_bin_2D(self):
+        """ 
+        Test that 2D marginals sum to 1 and have right shape.
+        """
+        (bins, marginals) = plotP.calculate_2D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 1)
+        
+        nptest.assert_almost_equal(marginals[(0,1)][0], 1.0)
+        nptest.assert_equal(marginals[(0,1)].shape, (1,1))
+
+    def test_10_bins_2D(self):
+        """ 
+        Test that 2D marginals sum to 1 and have right shape.
+        """
+        (bins, marginals) = plotP.calculate_2D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 10)
+        nptest.assert_almost_equal(np.sum(marginals[(0,1)]), 1.0)
+        nptest.assert_equal(marginals[(0,1)].shape, (10,10))
+
+    def test_5_10_bins_2D(self):
+        """ 
+        Test that 1D marginals sum to 1 and have right shape.
+        """
+        (bins, marginals) = plotP.calculate_2D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = [5,10])
+        nptest.assert_almost_equal(np.sum(marginals[(0,1)]), 1.0)
+        nptest.assert_equal(marginals[(0,1)].shape, (5,10))
+
+
+    def test_1D_smoothing(self):
+        """
+        Test :meth:`bet.postProcess.plotP.smooth_marginals_1D`.
+        """
+        (bins, marginals) = plotP.calculate_1D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 10)
+        marginals_smooth = plotP.smooth_marginals_1D(marginals, bins, sigma = 10.0)
+        nptest.assert_equal(marginals_smooth[0].shape,  marginals[0].shape)
+        nptest.assert_almost_equal(np.sum(marginals_smooth[0]), 1.0)
+
+    def test_2D_smoothing(self):
+        """
+        Test :meth:`bet.postProcess.plotP.smooth_marginals_2D`.
+        """
+        (bins, marginals) = plotP.calculate_2D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 10)
+        marginals_smooth = plotP.smooth_marginals_2D(marginals, bins, sigma = 10.0)
+        nptest.assert_equal(marginals_smooth[(0,1)].shape,  marginals[(0,1)].shape)
+        nptest.assert_almost_equal(np.sum(marginals_smooth[(0,1)]), 1.0)
+
+    def test_plot_marginals_1D(self):
+        """
+        Test :meth:`bet.postProcess.plotP.plot_1D_marginal_probs`.
+        """
+        (bins, marginals) = plotP.calculate_1D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 10)
+        try:
+            plotP.plot_1D_marginal_probs(marginals, bins,self.lam_domain, interactive=False)
+            go = True
+        except (RuntimeError, TypeError, NameError):
+            go = False
+            nptest.assert_equal(go, True)
+
+    def test_plot_marginals_2D(self):
+        """
+        Test :meth:`bet.postProcess.plotP.plot_2D_marginal_probs`.
+        """
+        (bins, marginals) = plotP.calculate_2D_marginal_probs(self.P_samples,
+                                                              self.samples,
+                                                              self.lam_domain,
+                                                              nbins = 10)
+        marginals[(0,1)][0][0]=0.0
+        marginals[(0,1)][0][1]*=2.0
+        try:
+            plotP.plot_2D_marginal_probs(marginals, bins,self.lam_domain, interactive=False)
+            go = True
+        except (RuntimeError, TypeError, NameError):
+            go = False
+            nptest.assert_equal(go, True)
diff --git a/test/test_visualize/test_plotP.py b/test/test_visualize/test_plotP.py
deleted file mode 100644
index e69de29b..00000000

From f4bae3f86a6d766f674603567536ed76af354e80 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Tue, 7 Apr 2015 23:01:24 -0600
Subject: [PATCH 14/85] finished basic sampling tests

---
 bet/sampling/basicSampling.py            |   5 +-
 test/test_Comm.py                        |   6 +-
 test/test_sampling/test_basicSampling.py | 161 ++++++++++++++++-------
 test/test_util.py                        |  10 +-
 4 files changed, 123 insertions(+), 59 deletions(-)

diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index 14ee91f7..68de1687 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -55,7 +55,10 @@ def in_high_prob(data, rho_D, maximum, sample_nos=None):
     """
     if sample_nos == None:
         sample_nos = range(data.shape[0])
-    rD = rho_D(data[sample_nos, :])
+    if len(data.shape) == 1:
+        rD = rho_D(data[sample_nos])
+    else:
+        rD = rho_D(data[sample_nos, :])
     adjusted_total_prob = int(sum(rD)/maximum)
     print "Samples in box "+str(adjusted_total_prob)
     return adjusted_total_prob
diff --git a/test/test_Comm.py b/test/test_Comm.py
index 982f1511..f57e9c73 100644
--- a/test/test_Comm.py
+++ b/test/test_Comm.py
@@ -13,15 +13,15 @@ class Test_comm_for_no_mpi4py(unittest.TestCase):
     def setUp(self):
         self.comm = Comm.comm_for_no_mpi4py()
     def test_Get_size(self):
-        self.assertEqual(self.comm.Get_size(),1)
+        self.assertEqual(self.comm.Get_size(), 1)
     def test_Get_rank(self):
-        self.assertEqual(self.comm.Get_rank(),0)
+        self.assertEqual(self.comm.Get_rank(), 0)
     def test_allgrather(self):
         thing = range(4)
         self.assertEqual(self.comm.allgather(thing), thing)
     def test_allreduce(self):
         thing = 4
-        self.assertEqual(self.comm.allreduce(thing,op=None), thing)
+        self.assertEqual(self.comm.allreduce(thing, op=None), thing)
     def test_bcast(self):
         thing = range(4)
         self.assertEqual(self.comm.bcast(thing, root=0), thing)
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index 1b42934f..bea8ef1d 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -15,9 +15,10 @@ def test_in_high_prob():
     Tests :meth:`bet.sampling.basicSampling.in_high_prob`
     """
     def rho_D(my_data):
-        return my_data/len(my_data)
+        return my_data/4.0
     data = np.array([0, 1, 0, 1, 1, 1])
     maximum = np.max(rho_D(data))
+    print "maximum", maximum
     assert 4 == bsam.in_high_prob(data, rho_D, maximum)
     assert 3 == bsam.in_high_prob(data, rho_D, maximum, [3, 4, 5])
     assert 2 == bsam.in_high_prob(data, rho_D, maximum, [0, 1, 2, 3])
@@ -32,45 +33,119 @@ def test_in_high_prob_multi():
     
     """
     def rho_D(my_data):
-        return my_data/len(my_data)
+        return my_data/4.0
     data1 = np.array([0, 1, 0, 1, 1, 0])
     data2 = np.ones(data1.shape)-data1
     maximum = np.max(rho_D(data1))
 
+    print "maximum", maximum
     results_list = [[None, data1], [None, data2], [None, data1], [None, data2]]
     sample_nos_list = [[3, 4, 5], [3, 4, 5], [0, 2, 4], [0, 2, 4]]
 
-    nptest.assert_array_equal(np.array([3, 1, 1, 2]),
+    nptest.assert_array_equal(np.array([2, 1, 1, 2]),
             bsam.in_high_prob_multi(results_list, rho_D, maximum,
                 sample_nos_list))
-    nptest.assert_array_equal(np.array([4, 2, 4, 2]),
+    nptest.assert_array_equal(np.array([3, 3, 3, 3]),
             bsam.in_high_prob_multi(results_list, rho_D, maximum))
 
 def test_loadmat():
     """
     Tests :meth:`bet.sampling.basicSampling.loadmat`
     """
-    mdat1 = {'samples':range(5), 'data':range(5), 'num_samples':5}
-    mdat2 = {'samples':range(6), 'num_samples':6}
+    mdat1 = {'samples':np.random.random((5,1)),
+            'data':np.random.random((5,1)), 'num_samples':5}
+    mdat2 = {'samples':np.random.random((6,1)), 'num_samples':6}
     model = "this is not a model"
 
-    sio.savefile('testfile1', mdat1)
-    sio.savefile('testfile2', mdat2)
+    sio.savemat('testfile1', mdat1)
+    sio.savemat('testfile2', mdat2)
 
     (loaded_sampler1, samples1, data1) = bsam.loadmat('testfile1')
-    nptest.assert_array_equal(samples1, range(5))
-    nptest.assert_array_equal(data1, range(5))
+    nptest.assert_array_equal(samples1, mdat1['samples'])
+    nptest.assert_array_equal(data1, mdat1['data'])
     assert loaded_sampler1.num_samples == 5
     assert loaded_sampler1.lb_model == None
 
     (loaded_sampler2, samples2, data2) = bsam.loadmat('testfile2', model)
-    nptest.assert_array_equal(samples2, range(6))
+    nptest.assert_array_equal(samples2, mdat2['samples'])
     nptest.assert_array_equal(data2, None)
-    assert loaded_sampler1.num_samples == 6
-    assert loaded_sampler1.lb_model == model
+    assert loaded_sampler2.num_samples == 6
+    assert loaded_sampler2.lb_model == model
     os.remove('testfile1.mat')
     os.remove('testfile2.mat')
 
+def verify_user_samples(model, sampler, samples, savefile, parallel):
+    # evalulate the model at the samples directly
+    data = model(samples)
+
+    # evaluate the model at the samples
+    (my_samples, my_data) = sampler.user_samples(samples, savefile,
+            parallel)
+
+    if len(data.shape) == 1:
+        data = np.expand_dims(data, axis=1)
+    if len(samples.shape) == 1:
+        samples = np.expan_dims(samples, axis=1)
+    
+    # compare the samples
+    nptest.assert_array_equal(samples, my_samples)
+    # compare the data
+    nptest.assert_array_equal(data, my_data)
+    # did num_samples get updated?
+    assert samples.shape[0] == sampler.num_samples
+    # did the file get correctly saved?
+
+    mdat = sio.loadmat('savefile')
+    nptest.assert_array_equal(samples, mdat['samples'])
+    nptest.assert_array_equal(data, mdat['data'])
+    assert samples.shape[0] == sampler.num_samples
+
+def verify_random_samples(model, sampler, sample_type, param_min, param_max,
+        num_samples, savefile, parallel):
+    # recreate the samples
+    if num_samples == None:
+        num_samples = self.num_samples
+    param_left = np.repeat([param_min], num_samples, 0)
+    param_right = np.repeat([param_max], num_samples, 0)
+    samples = (param_right-param_left)
+    if sample_type == "lhs":
+        samples = samples * lhs(param_min.shape[-1], num_samples, criterion)
+    elif sample_type == "random" or "r":
+        np.random.seed(1)
+        samples = samples * np.random(param_left.shape)
+    samples = samples + param_left
+    # evalulate the model at the samples directly
+    data = model(samples)
+
+    # evaluate the model at the samples
+    # reset the random seed
+    if sample_type == "random" or "r":
+        np.random.seed(1)
+    (my_samples, my_data) = sampler.user_samples(samples, savefile,
+            parallel)
+
+    # make sure that the samples are within the boundaries
+    assert np.all(my_samples <= param_right)
+    assert np.all(my_samples >= param_left)
+
+    if len(data.shape) == 1:
+        data = np.expand_dims(data, axis=1)
+    if len(samples.shape) == 1:
+        samples = np.expan_dims(samples, axis=1)
+    
+    # compare the samples
+    nptest.assert_array_equal(samples, my_samples)
+    # compare the data
+    nptest.assert_array_equal(data, my_data)
+    # did num_samples get updated?
+    assert samples.shape[0] == sampler.num_samples
+    # did the file get correctly saved?
+
+    mdat = sio.loadmat('savefile')
+    nptest.assert_array_equal(samples, mdat['samples'])
+    nptest.assert_array_equal(data, mdat['data'])
+    assert samples.shape[0] == sampler.num_samples
+
 
 class Test_basic_sampler(unittest.TestCase):
     """
@@ -117,13 +192,11 @@ def test_init(self):
         Test initalization of :class:`bet.sampling.basicSampling.sampler`
         """
         assert self.samplers[0].num_samples == 100
-        assert self.samplers[0].model == self.models[0]
+        assert self.samplers[0].lb_model == self.models[0]
         assert bsam.sampler(self.models[0], None).num_samples == None
 
     @unittest.skip("Skipping testing saving")
     def test_save(self):
-        """
-        """
         pass
 
     def test_update(self):
@@ -140,46 +213,34 @@ def test_user_samples(self):
         three different QoI maps (1 to 1, 3 to 1, 3 to 2, 10 to 4).
         """
         # create a list of different sets of samples
-        list_of_samples = [np.ones((4,)), np.ones((4,1)), np.ones((4,3)),
-                np.ones((4,3)), np.ones((4,10))]
+        list_of_samples = [np.ones((4, )), np.ones((4, 1)), np.ones((4, 3)),
+                np.ones((4, 3)), np.ones((4, 10))]
         
         test_list = zip(self.models, self.samplers, list_of_samples, 
                 self.savefiles)
         
         for model, sampler, samples, savefile in test_list: 
             for parallel in [False, True]:
-                yield verify_user_samples, model, sampler, samples, savefile,
-                parallel
-
-    def verify_user_samples(model, sampler, samples, savefile, parallel):
-        # evalulate the model at the samples directly
-        data = model(samples)
-
-        # evaluate the model at the samples
-        (my_samples, my_data) = sampler.user_samples(samples, savefile,
-                parallel)
-
-        if len(data.shape) == 1:
-            data = np.expand_dims(data, axis=1)
-        if len(samples.shape) == 1:
-            samples = np.expan_dims(samples, axis=1)
-        
-        # compare the samples
-        nptest.assert_array_equal(samples, my_samples)
-        # compare the data
-        nptest.assert_array_equal(data, my_data)
-        # did num_samples get updated?
-        assert samples.shape[0] == sampler.num_samples
-        # did the file get correctly saved?
-
-        mdat = sio.loadmat('savefile')
-        nptest.assert_array_equal(samples, mdat['samples'])
-        nptest.assert_array_equal(data, mdat['data'])
-        assert samples.shape[0] == sampler.num_samples
-
-
-
+                yield verify_user_samples, model, sampler, samples, savefile, parallel
 
+    def test_random_samples(self):
+        """
+        Test :met:`bet.sampling.basicSampling.sampler.random_samples` for three
+        different QoI maps (1 to 1, 3 to 1, 3 to 2, 10 to 4).
+        """
+        param_min_list = [self.param_min1, self.param_min1, self.param_min3,
+            self.param_min3, self.param_min10]
+        param_max_list = [self.param_max1, self.param_max1, self.param_max3,
+            self.param_max3, self.param_max10]
 
-        
+        test_list = zip(self.models, self.samplers, param_min_list,
+                param_max_list,
+                self.savefiles)
 
+        for model, sampler, param_min, param_max, savefile in test_list:
+            for sample_type in ["random", "r", "lhs"]:
+                for num_samples in [None, 25]:
+                    for parallel in [False, True]:
+                        yield verify_random_samples, model, sampler,
+                        param_min, param_max, savefile, num_samples,
+                        parallel
diff --git a/test/test_util.py b/test/test_util.py
index 8c27a6f1..9958eacc 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -2,7 +2,6 @@
 This module contains unittests for :mod:`~bet.util`
 """
 
-import unittest
 import bet.util as util
 from bet.Comm import *
 import numpy.testing as nptest
@@ -41,7 +40,7 @@ def test_meshgrid_ndim():
     equal to ``[0, 1]``.
     """
     for i in xrange(10):
-        x = [[0,1] for v in xrange(i+1)]
+        x = [[0, 1] for v in xrange(i+1)]
         yield compare_to_bin_rep, util.meshgrid_ndim(x)
 
 def test_get_global_values():
@@ -63,9 +62,9 @@ def compare_get_global_values(i):
     """
     if rank == 0:
         if i == 0:
-            original_array = np.array(np.random.random((size*2,)))
+            original_array = np.array(np.random.random((size*2, )))
         else:
-            original_array = np.array(np.random.random((size*2,i)))
+            original_array = np.array(np.random.random((size*2, i)))
     else:
         original_array = None
     original_array = comm.bcast(original_array)
@@ -74,7 +73,8 @@ def compare_get_global_values(i):
     if i == 0:
         my_array = original_array[my_index]
     else:
-        my_array = original_array[my_index,:]
+        my_array = original_array[my_index, :w
+                :]
     recomposed_array = util.get_global_values(my_array)
     nptest.assert_array_equal(original_array, recomposed_array)
 

From 83385e8eab099d69ba067f2a2a65c9bcb86ae50f Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Tue, 7 Apr 2015 23:07:47 -0600
Subject: [PATCH 15/85] added beginnings for testing adaptive sampling

---
 test/test_sampling/test_adaptiveSampling.py | 172 +++++++++++++++++++-
 1 file changed, 167 insertions(+), 5 deletions(-)

diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index cb417107..42b3fa39 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -1,8 +1,170 @@
 """
-This module contains tests for the :mod:`bet.sampling.basicSampling` module.
+This module contains unittests for :mod:`~bet.util`
+"""
+
+import unittest, os
+import numpy.testing as nptest
+import numpy as np
+import bet.sampling.basicSampling as bsam
+import scipy.io as sio
 
-    * check input/output dimensions and format
-    * make sure that $\lambda_i \in \Lambda$
-    * provide diagnoistic output (?)
-    * check that step sizes are within a certain tolerance
 """
+TODO: rewrite loadmat test, rewrite init test, rewrite update_mdict test, write
+tests for run_BLAHBLAH, write test for generalized_chains, write a test stub
+for kernels but do not implement
+"""
+
+def test_loadmat():
+    """
+    Tests :meth:`bet.sampling.basicSampling.loadmat`
+    """
+    mdat1 = {'samples':np.random.random((5,1)),
+            'data':np.random.random((5,1)), 'num_samples':5}
+    mdat2 = {'samples':np.random.random((6,1)), 'num_samples':6}
+    model = "this is not a model"
+
+    sio.savemat('testfile1', mdat1)
+    sio.savemat('testfile2', mdat2)
+
+    (loaded_sampler1, samples1, data1) = bsam.loadmat('testfile1')
+    nptest.assert_array_equal(samples1, mdat1['samples'])
+    nptest.assert_array_equal(data1, mdat1['data'])
+    assert loaded_sampler1.num_samples == 5
+    assert loaded_sampler1.lb_model == None
+
+    (loaded_sampler2, samples2, data2) = bsam.loadmat('testfile2', model)
+    nptest.assert_array_equal(samples2, mdat2['samples'])
+    nptest.assert_array_equal(data2, None)
+    assert loaded_sampler2.num_samples == 6
+    assert loaded_sampler2.lb_model == model
+    os.remove('testfile1.mat')
+    os.remove('testfile2.mat')
+
+def verify_random_samples(model, sampler, sample_type, param_min, param_max,
+        num_samples, savefile, parallel):
+    # recreate the samples
+    if num_samples == None:
+        num_samples = self.num_samples
+    param_left = np.repeat([param_min], num_samples, 0)
+    param_right = np.repeat([param_max], num_samples, 0)
+    samples = (param_right-param_left)
+    if sample_type == "lhs":
+        samples = samples * lhs(param_min.shape[-1], num_samples, criterion)
+    elif sample_type == "random" or "r":
+        np.random.seed(1)
+        samples = samples * np.random(param_left.shape)
+    samples = samples + param_left
+    # evalulate the model at the samples directly
+    data = model(samples)
+
+    # evaluate the model at the samples
+    # reset the random seed
+    if sample_type == "random" or "r":
+        np.random.seed(1)
+    (my_samples, my_data) = sampler.user_samples(samples, savefile,
+            parallel)
+
+    # make sure that the samples are within the boundaries
+    assert np.all(my_samples <= param_right)
+    assert np.all(my_samples >= param_left)
+
+    if len(data.shape) == 1:
+        data = np.expand_dims(data, axis=1)
+    if len(samples.shape) == 1:
+        samples = np.expan_dims(samples, axis=1)
+    
+    # compare the samples
+    nptest.assert_array_equal(samples, my_samples)
+    # compare the data
+    nptest.assert_array_equal(data, my_data)
+    # did num_samples get updated?
+    assert samples.shape[0] == sampler.num_samples
+    # did the file get correctly saved?
+
+    mdat = sio.loadmat('savefile')
+    nptest.assert_array_equal(samples, mdat['samples'])
+    nptest.assert_array_equal(data, mdat['data'])
+    assert samples.shape[0] == sampler.num_samples
+
+
+class Test_basic_sampler(unittest.TestCase):
+    """
+    Test :class:`bet.sampling.basicSampling.sampler`.
+    """
+
+    def setUp(self):
+        # create 1-1 map
+        self.param_min1 = np.zeros((1, ))
+        self.param_max1 = np.zeros((1, ))
+        def map_1t1(x):
+            return np.sin(x)
+        # create 3-1 map
+        self.param_min3 = np.zeros((3, ))
+        self.param_max3 = np.ones((3, ))
+        def map_3t1(x):
+            return np.sum(x, 1)
+        # create 3-2 map
+        def map_3t2(x):
+            return np.vstack(([x[:, 0]+x[:, 1], x[:, 2]])).transpose()
+        # create 10-4 map
+        self.param_min10 = np.zeros((10, ))
+        self.param_min10 = np.ones((10, ))
+        def map_10t4(x):
+            x1 = x[:, 0] + x[:, 1]
+            x2 = x[:, 2] + x[:, 3]
+            x3 = x[:, 4] + x[:, 5]
+            x4 = np.sum(x[:, [6, 7, 8, 9]], 1)
+            return np.vstack([x1, x2, x3, x4]).transpose()
+        num_samples = 100
+        self.savefiles = ["11t11", "1t1", "3to1", "3to2", "10to4"]
+        self.models = [map_1t1, map_1t1, map_3t1, map_3t2, map_10t4]
+        self.samplers = []
+        for model in self.models:
+            self.samplers.append(bsam.sampler(model, num_samples))
+
+    def tearDown(self):
+        for f in self.savefiles:
+            if os.path.exists(f+".mat"):
+                os.remove(f+".mat")
+
+    def test_init(self):
+        """
+        Test initalization of :class:`bet.sampling.basicSampling.sampler`
+        """
+        assert self.samplers[0].num_samples == 100
+        assert self.samplers[0].lb_model == self.models[0]
+        assert bsam.sampler(self.models[0], None).num_samples == None
+
+    @unittest.skip("Skipping testing saving")
+    def test_save(self):
+        pass
+
+    def test_update(self):
+        """
+        Test :meth:`bet.sampling.basicSampling.sampler.save`
+        """
+        mdict = {"frog":3, "moose":2}
+        self.samplers[0].update_mdict(mdict)
+        assert self.samplers[0].num_samples == mdict["num_samples"]
+
+    def test_random_samples(self):
+        """
+        Test :met:`bet.sampling.basicSampling.sampler.random_samples` for three
+        different QoI maps (1 to 1, 3 to 1, 3 to 2, 10 to 4).
+        """
+        param_min_list = [self.param_min1, self.param_min1, self.param_min3,
+            self.param_min3, self.param_min10]
+        param_max_list = [self.param_max1, self.param_max1, self.param_max3,
+            self.param_max3, self.param_max10]
+
+        test_list = zip(self.models, self.samplers, param_min_list,
+                param_max_list,
+                self.savefiles)
+
+        for model, sampler, param_min, param_max, savefile in test_list:
+            for sample_type in ["random", "r", "lhs"]:
+                for num_samples in [None, 25]:
+                    for parallel in [False, True]:
+                        yield verify_random_samples, model, sampler,
+                        param_min, param_max, savefile, num_samples,
+                        parallel

From 7a7f604a09cbcae830c5363f7cb01a0fa48883ed Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Tue, 7 Apr 2015 23:23:35 -0600
Subject: [PATCH 16/85] adds tests for postTools

---
 bet/postProcess/postTools.py            | 10 ++++-
 test/test_postProcess/test_postTools.py | 58 +++++++++++++++++++++++++
 2 files changed, 67 insertions(+), 1 deletion(-)
 create mode 100644 test/test_postProcess/test_postTools.py

diff --git a/bet/postProcess/postTools.py b/bet/postProcess/postTools.py
index db0ebf3a..e1e9c842 100644
--- a/bet/postProcess/postTools.py
+++ b/bet/postProcess/postTools.py
@@ -26,6 +26,8 @@ def sort_by_rho(P_samples, samples, lam_vol=None, data=None):
     :returns: (P_samples, samples, lam_vol, data)
 
     """
+    if len(samples.shape) == 1:
+        samples = np.expand_dims(samples, axis=1)
     nnz = np.sum(P_samples > 0)
     if lam_vol == None:
         indices = np.argsort(P_samples)[::-1][0:nnz]
@@ -36,7 +38,9 @@ def sort_by_rho(P_samples, samples, lam_vol=None, data=None):
     if lam_vol != None:
         lam_vol = lam_vol[indices]
     if data != None:
-        data = data[indices]
+        if len(data.shape) == 1:
+            data = np.expand_dims(data, axis=1)
+        data = data[indices,:]
 
     return (P_samples, samples, lam_vol, data)
 
@@ -64,6 +68,8 @@ def sample_highest_prob(top_percentile, P_samples, samples, lam_vol=None,
     :returns: ( num_samples, P_samples, samples, lam_vol, data)
 
     """
+    if len(samples.shape) == 1:
+        samples = np.expand_dims(samples, axis=1)
     # TODO: should we trust the user or should we do a quick check of part of
     # the array to see if it's already sorted?
     if sort:
@@ -77,6 +83,8 @@ def sample_highest_prob(top_percentile, P_samples, samples, lam_vol=None,
     if lam_vol != None:
         lam_vol = lam_vol[0:num_samples]
     if data != None:
+        if len(data.shape) == 1:
+            data = np.expand_dims(data, axis=1)
         data = data[0:num_samples, :]
         
     return  (num_samples, P_samples, samples, lam_vol, data)
diff --git a/test/test_postProcess/test_postTools.py b/test/test_postProcess/test_postTools.py
new file mode 100644
index 00000000..e29e42ef
--- /dev/null
+++ b/test/test_postProcess/test_postTools.py
@@ -0,0 +1,58 @@
+# Steven Mattis 04/07/2015
+"""
+This module contains tests for :module:`bet.postProcess.postTools`.
+
+
+Tests for correct post-processing.
+"""
+import unittest
+import bet.calculateP.simpleFunP as simpleFunP
+import bet.postProcess.postTools as postTools
+import numpy as np
+import scipy.spatial as spatial
+import numpy.testing as nptest
+import bet.util as util
+from bet.Comm import *
+
+class Test_PostTools(unittest.TestCase):
+    """
+    Test :meth:`bet.postProcess.postTools`.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.lam_domain=np.array([[0.0,1.0]])
+        num_samples=1000
+        self.samples = np.linspace(self.lam_domain[0][0], self.lam_domain[0][1], num_samples+1)
+        self.P_samples = (1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
+        self.P_samples[0] = 0.0
+        self.P_samples[-1] *= 2.0
+
+        self.data = self.samples[:]
+        
+    def test_sort_by_rho(self):
+        """
+        Test :meth:`bet.postProcess.postTools.sort_by_rho`.
+        """
+        (P_samples, samples, _ , data) = postTools.sort_by_rho(self.P_samples, self.samples,
+                                                               lam_vol=None, data=self.data)
+        self.assertGreater(np.min(P_samples),0.0)
+        nptest.assert_almost_equal(np.sum(P_samples),1.0)
+
+    def test_sample_highest_prob(self):
+        """
+        Test :meth:`bet.postProcess.postTools.sample_highest_prob`.
+        """
+        (num_samples,P_samples, samples, _ , data) = postTools.sample_highest_prob(1.0,
+                                                                                  self.P_samples, 
+                                                                                  self.samples,
+                                                                                  lam_vol=None, data=self.data, sort=True)
+        nptest.assert_almost_equal(np.sum(P_samples),1.0)
+        nptest.assert_equal(num_samples,1000)
+        
+        (num_samples,P_samples, samples, _ , data) = postTools.sample_highest_prob(0.8,
+                                                                                  self.P_samples, 
+                                                                                  self.samples,
+                                                                                  lam_vol=None, data=self.data, sort=True)
+        nptest.assert_allclose(np.sum(P_samples),0.8,0.001)

From 56cf20dbfa4bc556bed2f355a077dac0f359228e Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Wed, 8 Apr 2015 11:40:49 -0600
Subject: [PATCH 17/85] make changes for pull request

---
 bet/calculateP/calculateP.py                |   8 +-
 setup.py                                    |   8 +-
 test/test_calculateP/test_calculateP.py     | 316 ++++-------
 test/test_calculateP/test_calculateP_old.py | 558 --------------------
 test/test_postProcess/test_plotP.py         |   4 +-
 5 files changed, 104 insertions(+), 790 deletions(-)
 delete mode 100644 test/test_calculateP/test_calculateP_old.py

diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index d1ae5e93..0a24477a 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -137,7 +137,7 @@ def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None):
             rho_D_M, d_distr_samples, lam_domain, None, d_Tree)
     
     # Apply the standard MC approximation 
-    lam_vol = np.ones((samples.shape[0],1))
+    lam_vol = np.ones((samples.shape[0],))
     # Calculate Probabilities
     P = np.zeros((samples.shape[0],))
     # TODO Fix issue of unecessary mpi call
@@ -148,7 +148,7 @@ def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None):
         Itemp_sum = comm.allreduce(Itemp_sum, op=MPI.SUM)
         if Itemp_sum > 0:
             P[Itemp] = rho_D_M[i]*lam_vol[Itemp]/Itemp_sum 
-    lam_vol = (1.0/float(samples.shape[0]))*np.ones((samples.shape[0],1))
+    lam_vol = (1.0/float(samples.shape[0]))*np.ones((samples.shape[0],))
 
     return (P, lam_vol, io_ptr, emulate_ptr)
 
@@ -224,7 +224,7 @@ def prob_qhull(samples, data, rho_D_M, d_distr_samples,
     
     # Calculate the Voronoi diagram for samples. Calculate the volumes of 
     # the convex hulls of the corresponding Voronoi regions.
-    lam_vol = np.zeros((samples.shape[-1],1))
+    lam_vol = np.zeros((samples.shape[-1],))
     for i in range((samples.shape[0])):
         vornoi = spatial.Voronoi(lam_bound)
         lam_vol[i] = float(pyhull.qconvex('Qt FA', vornoi.vertices).split()[-1])
@@ -280,7 +280,7 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
     # Apply the standard MC approximation to determine the number of emulated
     # samples per model run sample. This is for approximating 
     # \mu_Lambda(A_i \intersect b_j)
-    lam_vol = np.zeros((samples.shape[0],1)) 
+    lam_vol = np.zeros((samples.shape[0],)) 
     for i in range(samples.shape[0]):
         lam_vol[i] = np.sum(np.equal(emulate_ptr, i))
     clam_vol = np.copy(lam_vol) 
diff --git a/setup.py b/setup.py
index c5eba602..5a4cedd8 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
 setup(name='bet',
       version='0.0',
       description='Butler, Estep, Tavener method',
-      author = 'Steven Mattis',
-      author_email ='steve.a.mattis@gmail.com',
-      url= 'https://github.com/smattis/BET',
-      packages =['bet', 'bet.sampling', 'bet.calculateP', 'bet.postProcess'])
+      author='Steven Mattis',
+      author_email='steve.a.mattis@gmail.com',
+      url='https://github.com/UT-CHG/BET',
+      packages=['bet', 'bet.sampling', 'bet.calculateP', 'bet.postProcess'])
diff --git a/test/test_calculateP/test_calculateP.py b/test/test_calculateP/test_calculateP.py
index 6e789bb3..79b3d439 100644
--- a/test/test_calculateP/test_calculateP.py
+++ b/test/test_calculateP/test_calculateP.py
@@ -56,6 +56,72 @@ def test_bounds(self):
         self.assertLessEqual(np.max(self.lambda_emulate[:,1]), 4.0)
         self.assertLessEqual(np.max(self.lambda_emulate[:,2]), 0.5)
 
+class prob:
+    def test_prob_sum_to_1(self):
+        """
+        Test to see if the prob. sums to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
+    @unittest.skipIf(size > 1, 'Only run in serial')
+    def test_P_matches_true(self):
+        """
+        Test against reference probs. (Only in serial)
+        """
+        nptest.assert_almost_equal(self.P_ref,self.P)
+    def test_vol_sum_to_1(self):
+        """
+        Test that volume ratios sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
+    def test_prob_pos(self):
+        """
+        Test that all probs are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)
+
+class prob_emulated:
+    def test_P_sum_to_1(self):
+        """
+        Test that prob. sums to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P_emulate),1.0)
+    def test_P_matches_true(self):
+        """
+        Test that probabilites match reference values.
+        """
+        if size == 1:
+            nptest.assert_almost_equal(self.P_emulate_ref,self.P_emulate)
+    def test_prob_pos(self):
+        """
+        Test that all probabilites are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P_emulate,0)),0)
+
+class prob_mc:
+    def test_P_sum_to_1(self):
+        """
+        Test that probs sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.P),1.0)
+    def test_P_matches_true(self):
+        """
+        Test the probs. match reference values.
+        """
+        if size==1:
+            nptest.assert_almost_equal(self.P_ref,self.P)
+    def test_vol_sum_to_1(self):
+        """
+        Test that volume ratios sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
+    def test_prob_pos(self):
+        """
+        Test that all probs are non-negative.
+        """
+        self.assertEqual(np.sum(np.less(self.P,0)),0)
+        
+    
+
 class TestProbMethod_3to2(unittest.TestCase):
     """
     Sets up 3 to 2 map problem.
@@ -72,7 +138,10 @@ def setUp(self):
         rnd.seed(1)
         self.lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain=self.lam_domain, 
                                                   num_l_emulate = 1000)
-class Test_prob_3to2(TestProbMethod_3to2):
+
+
+    
+class Test_prob_3to2(TestProbMethod_3to2,prob):
     """
     Test :meth:`bet.calculateP.calculateP.prob` on 3 to 2 map.
     """
@@ -89,29 +158,9 @@ def setUp(self):
                                                      d_Tree = self.d_Tree)
         self.P_ref = np.loadtxt("datafiles/3to2_prob.txt.gz")
 
-    def test_prob_sum_to_1(self):
-        """
-        Test to see if the prob. sums to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P),1.0)
-    def test_P_matches_true(self):
-        """
-        Test against reference probs. (Only in serial)
-        """
-        if size == 1:
-            nptest.assert_almost_equal(self.P_ref,self.P)
-    def test_vol_sum_to_1(self):
-        """
-        Test that volume ratios sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
-    def test_prob_pos(self):
-        """
-        Test that all probs are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P,0)),0)
 
-class Test_prob_emulated_3to2(TestProbMethod_3to2):
+
+class Test_prob_emulated_3to2(TestProbMethod_3to2, prob_emulated):
     """
     Test :meth:`bet.calculateP.calculateP.prob_emulated` on a 3 to 2 map.
     """
@@ -130,24 +179,9 @@ def setUp(self):
         self.P_emulate_ref=np.loadtxt("datafiles/3to2_prob_emulated.txt.gz")
         self.P_emulate = util.get_global_values(self.P_emulate)
 
-    def test_P_sum_to_1(self):
-        """
-        Test that prob. sums to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P_emulate),1.0)
-    def test_P_matches_true(self):
-        """
-        Test that probabilites match reference values.
-        """
-        if size == 1:
-            nptest.assert_almost_equal(self.P_emulate_ref,self.P_emulate)
-    def test_prob_pos(self):
-        """
-        Test that all probabilites are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P_emulate,0)),0)
 
-class Test_prob_mc_3to2(TestProbMethod_3to2):
+
+class Test_prob_mc_3to2(TestProbMethod_3to2, prob_mc):
     """
     Test :meth:`bet.calculateP.calculateP.prob_mc` on a 3 to 2 map.
     """
@@ -165,27 +199,7 @@ def setUp(self):
                                                            d_Tree = self.d_Tree)
         self.P_ref = np.loadtxt("datafiles/3to2_prob_mc.txt.gz")
 
-    def test_P_sum_to_1(self):
-        """
-        Test that probs sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P),1.0)
-    def test_P_matches_true(self):
-        """
-        Test the probs. match reference values.
-        """
-        if size==1:
-            nptest.assert_almost_equal(self.P_ref,self.P)
-    def test_vol_sum_to_1(self):
-        """
-        Test that volume ratios sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
-    def test_prob_pos(self):
-        """
-        Test that all probs are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P,0)),0)
+ 
 
 class TestProbMethod_3to1(unittest.TestCase):
     """
@@ -206,7 +220,7 @@ def setUp(self):
         rnd.seed(1)
         self.lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain=self.lam_domain, 
                                                   num_l_emulate = 1000)
-class Test_prob_3to1(TestProbMethod_3to1):
+class Test_prob_3to1(TestProbMethod_3to1, prob):
     """
     Test :meth:`bet.calculateP.calculateP.prob` on a 3 to 1 map.
     """
@@ -223,30 +237,8 @@ def setUp(self):
                                                      d_Tree = self.d_Tree)
         self.P_ref = np.loadtxt("datafiles/3to1_prob.txt.gz")
 
-    def test_prob_sum_to_1(self):
-        """
-        Test that probs. sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P),1.0)
-    def test_P_matches_true(self):
-        """
-        Test that probs match reference data.
-        """
-        if size==1:
-            nptest.assert_almost_equal(self.P_ref,self.P)
-    def test_vol_sum_to_1(self):
-        """
-        Test that volume ratios sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
-    def test_prob_pos(self):
-        """
-        Test that probs. are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P,0)),0)
-
 
-class Test_prob_emulated_3to1(TestProbMethod_3to1):
+class Test_prob_emulated_3to1(TestProbMethod_3to1, prob_emulated):
     """
     Test :meth:`bet.calculateP.calculateP.prob_emulated` on a 3 to 1 map.
     """
@@ -265,26 +257,9 @@ def setUp(self):
         self.P_emulate_ref=np.loadtxt("datafiles/3to1_prob_emulated.txt.gz")
         self.P_emulate = util.get_global_values(self.P_emulate)
 
-    def test_P_sum_to_1(self):
-        """
-        Test the probs sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P_emulate),1.0)
-
-    def test_P_matches_true(self):
-        """
-        Test the probs. match reference values.
-        """
-        if size==1:
-            nptest.assert_almost_equal(self.P_emulate_ref,self.P_emulate)
 
-    def test_prob_pos(self):
-        """
-        Test that all probs are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P_emulate,0)),0)
 
-class Test_prob_mc_3to1(TestProbMethod_3to1):
+class Test_prob_mc_3to1(TestProbMethod_3to1, prob_mc):
     """
     Test :meth:`bet.calculateP.calculateP.prob_mc` on a 3 to 1 map.
     """
@@ -302,31 +277,7 @@ def setUp(self):
                                                            d_Tree = self.d_Tree)
         self.P_ref = np.loadtxt("datafiles/3to1_prob_mc.txt.gz")
 
-    def test_P_sum_to_1(self):
-        """
-        Test that probs sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P),1.0)
-
-    def test_P_matches_true(self):
-        """
-        Test that probs match reference vals.
-        """
-        if size==1:
-            nptest.assert_almost_equal(self.P_ref,self.P)
-
-    def test_vol_sum_to_1(self):
-        """
-        Test that volume ratios sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
-
-    def test_prob_pos(self):
-        """
-        Test that all probs are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P,0)),0)
-
+  
 class TestProbMethod_10to4(unittest.TestCase):
     """
     Sets up 10 to 4 map problem.
@@ -347,7 +298,11 @@ def setUp(self):
         Q_ref =  np.mean(self.data, axis=0)
         (self.d_distr_prob, self.d_distr_samples, self.d_Tree) = simpleFunP.uniform_hyperrectangle(data=self.data,Q_ref=Q_ref, bin_ratio=0.2, center_pts_per_edge = 1)
 
-class Test_prob_10to4(TestProbMethod_10to4):
+    @unittest.skip("No reference data")
+    def test_P_matches_true(self):
+        pass
+
+class Test_prob_10to4(TestProbMethod_10to4, prob):
     """
     Test :meth:`bet.calculateP.calculateP.prob` on a 10 to 4 map.
     """
@@ -363,25 +318,9 @@ def setUp(self):
                                                      lam_domain = self.lam_domain,
                                                      d_Tree = self.d_Tree)
 
-    def test_prob_sum_to_1(self):
-        """
-        Test that probs sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P),1.0)
-
-    def test_vol_sum_to_1(self):
-        """
-        Test that volume ratios sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
-
-    def test_prob_pos(self):
-        """
-        Test that all probs are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P,0)),0)
+    
 
-class Test_prob_emulated_10to4(TestProbMethod_10to4):
+class Test_prob_emulated_10to4(TestProbMethod_10to4, prob_emulated):
     """
     Test :meth:`bet.calculateP.calculateP.prob_emulated` on a 10 to 4 map.
     """
@@ -400,19 +339,10 @@ def setUp(self):
                                                               d_Tree = self.d_Tree)
         self.P_emulate = util.get_global_values(self.P_emulate)
 
-    def test_P_sum_to_1(self):
-        """
-        Test that probs. sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P_emulate),1.0)
 
-    def test_prob_pos(self):
-        """
-        Test that all probs are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P_emulate,0)),0)
 
-class Test_prob_mc_10to4(TestProbMethod_10to4):
+
+class Test_prob_mc_10to4(TestProbMethod_10to4, prob_mc):
     """
     Test :meth:`bet.calculateP.calculateP.prob_mc` on a 10 to 4 map.
     """
@@ -429,23 +359,6 @@ def setUp(self):
                                                            lambda_emulate = self.lambda_emulate,
                                                            d_Tree = self.d_Tree)
 
-    def test_P_sum_to_1(self):
-        """
-        Test the probs. sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P),1.0)
-
-    def test_vol_sum_to_1(self):
-        """
-        Test that volume ratios sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
-
-    def test_prob_pos(self):
-        """
-        Test that probs. are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P,0)),0)
 
 class TestProbMethod_1to1(unittest.TestCase):
     """
@@ -466,8 +379,11 @@ def setUp(self):
         self.data = 2.0*self.samples
         Q_ref =  np.mean(self.data, axis=0)
         (self.d_distr_prob, self.d_distr_samples, self.d_Tree) = simpleFunP.uniform_hyperrectangle(data=self.data,Q_ref=Q_ref, bin_ratio=0.2, center_pts_per_edge = 1)
+    @unittest.skip("No reference data")
+    def test_P_matches_true(self):
+        pass
 
-class Test_prob_1to1(TestProbMethod_1to1):
+class Test_prob_1to1(TestProbMethod_1to1, prob):
     """
     Test :meth:`bet.calculateP.calculateP.prob` on a 1 to 1 map.
     """
@@ -483,25 +399,8 @@ def setUp(self):
                                                      lam_domain = self.lam_domain,
                                                      d_Tree = self.d_Tree)
 
-    def test_prob_sum_to_1(self):
-        """
-        Test that probs sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P),1.0)
 
-    def test_vol_sum_to_1(self):
-        """
-        Test that volume ratios sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
-
-    def test_prob_pos(self):
-        """
-        Test that all probs are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P,0)),0)
-
-class Test_prob_emulated_1to1(TestProbMethod_1to1):
+class Test_prob_emulated_1to1(TestProbMethod_1to1, prob_emulated):
     """
     Test :meth:`bet.calculateP.calculateP.prob_emulated` on a 1 to 1 map.
     """
@@ -520,19 +419,8 @@ def setUp(self):
                                                               d_Tree = self.d_Tree)
         self.P_emulate = util.get_global_values(self.P_emulate)
 
-    def test_P_sum_to_1(self):
-        """
-        Test that probs. sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P_emulate),1.0)
 
-    def test_prob_pos(self):
-        """
-        Test that all probs are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P_emulate,0)),0)
-
-class Test_prob_mc_1to1(TestProbMethod_1to1):
+class Test_prob_mc_1to1(TestProbMethod_1to1, prob_mc):
     """
     Test :meth:`bet.calculateP.calculateP.prob_mc` on a 1 to 1 map.
     """
@@ -549,20 +437,4 @@ def setUp(self):
                                                            lambda_emulate = self.lambda_emulate,
                                                            d_Tree = self.d_Tree)
 
-    def test_P_sum_to_1(self):
-        """
-        Test the probs. sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.P),1.0)
-
-    def test_vol_sum_to_1(self):
-        """
-        Test that volume ratios sum to 1.
-        """
-        nptest.assert_almost_equal(np.sum(self.lam_vol), 1.0)
 
-    def test_prob_pos(self):
-        """
-        Test that probs. are non-negative.
-        """
-        self.assertEqual(np.sum(np.less(self.P,0)),0)
diff --git a/test/test_calculateP/test_calculateP_old.py b/test/test_calculateP/test_calculateP_old.py
deleted file mode 100644
index 721b18e6..00000000
--- a/test/test_calculateP/test_calculateP_old.py
+++ /dev/null
@@ -1,558 +0,0 @@
-# Lindley Graham 05/22/2014
-
-"""
-This module contains tests for :module:`bet.caculcateP.calculateP`.
-
-    * compare using same lambda_emulated (iid and regular grid)
-    * compare using different lambda_emulated
-
-Most of these tests should make sure certain values are within a tolerance
-rather than exact due to the stocastic nature of the algorithms being tested.
-"""
-
-import unittest
-import bet.calculateP.calculateP as calcP
-import numpy sa np
-import scipy.spatial as spatial
-import numpy.testing as nptest
-
-class TestEmulateIIDLebesgue(unittest.TestCase):
-    """
-    Test :meth:`bet.calculateP.calculateP.emulate_iid_lebesgue`.
-    """
-    
-    def runTest(self):
-        """
-        Test dimension, number of samples, and that all the samples are within
-        lambda_domain.
-
-        """
-        lam_left = np.array([0.0, .25, .4])
-        lam_right = np.array([1.0, 4.0, .5])
-
-        lam_domain = np.zeros((3,3))
-        lam_domain[:,0] = lam_left
-        lam_domain[:,1] = lam_right
-
-        num_l_emulate = 1e6
-
-        lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
-
-        # check the dimension
-        np.assert_array_equal(lambda_emulate.shape, (3, num_l_emulate))
-
-        # check that the samples are all within the correct bounds
-        np.assertGreaterEqual(0.0, np.min(lambda_emulate[0, :]))
-        np.assertGreaterEqual(.25, np.min(lambda_emulate[1, :]))
-        np.assertGreaterEqual(.4, np.min(lambda_emulate[2, :]))
-        np.assertLessEqual(1.0, np.max(lambda_emulate[0, :]))
-        np.assertLessEqual(4.0, np.max(lambda_emulate[1, :]))
-        np.assertLessEqual(.5, np.max(lambda_emulate[2, :]))
-
-# make sure probabilties and lam_vol follow the MC assumption (i.e. for uniform
-# over the entire space they should all be the same, the hyperrectangle case
-# will be different)
-
-# compare the P calculated by the different methods on the same samples 
-
-# add a skip thing to the tests involving qhull so that it only runs if that
-# package is installed
-
-# test where P is uniform over the entire domain
-# test where P is uniform over a hyperrectangle subdomain
-# test with and without optional arguments, together and separatly
-# compare using same lambda_emulated (iid and regular grid)
-# compare using different lambda_emulated
-# some will need to be within a tolerance and some will need to be exact
-# also tolerances will need to be depend on if the lambda_emulated are the same
-# and if there voronoi cells are the same, etc.
-# test on a linear model and maybe a non-linear, read from file model
-
-class TestProb(object):
-    """
-    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP` with a linear
-    model.
-    """
-    @classmethod
-    def setUpClass(cls):
-        """
-        Create inputs for ``prob*`` methods. This should run only once per
-        grouping of tests from this class. But, for isolated tests it doesn't
-        make any sense to do it this way. Since many of these tests are
-        comparision tests it should be fine to structure things in this manner
-        rather using inheritance everywhere.
-        """
-        # Create model associated inputs (samples, data, lam_domain)
-        data_domain = np.array([[0.0, 1.0], [-1.0, 1.0], [-1.0, 0.0]])
-        self.lam_domain = np.array([[.1, .2], [3, 4], [50, 60]])
-        # iid samples
-        self.u_samples = None #calcP.emulate_iid_lebesgue(lam_domain,
-                #20**lam_domain.shape[0])
-        # regular grid samples
-        lam1 = np.linspace(lam_domain[0, 0], lam_domain[0, 1], 20)
-        lam2 = np.linspace(lam_domain[1, 0], lam_domain[1, 1], 20)
-        lam3 = np.linspace(lam_domain[2, 0], lam_domain[2, 1], 20)
-        lam1, lam2, lam3 = np.meshgrid(lam1, lam2, lam3)
-        self.r_samples = np.column_stack((lam1.ravel(), lam2.ravel(), lam3.ravel()))
-    
-        # Create lambda_emulate
-        # iid
-        self.u_lambda_emulate = None #calcP.emulate_iid_lebesgue(lam_domain,
-            1e6)
-        # regular grid
-        lam1 = np.linspace(lam_domain[0, 0], lam_domain[0, 1], 100)
-        lam2 = np.linspace(lam_domain[1, 0], lam_domain[1, 1], 100)
-        lam3 = np.linspace(lam_domain[2, 0], lam_domain[2, 1], 100)
-        lam1, lam2, lam3 = np.meshgrid(lam1, lam2, lam3)
-        self.r_lambda_emulate = np.column_stack((lam1.ravel(), lam2.ravel(),
-            lam3.ravel()))
-
-    def compare_to_vol(result_vol, lambda_domain):
-        """
-        Compare lambda_vol from the algorithm to an analytic solution.
-
-        :param result_vol: lambda_vol from any of the methods in
-            :mod:`~bet.calculatevol.calculatevol`
-        :type result_vol: :class:`numpy.ndarray`
-
-        """
-        lambda_vol = np.product(lambda_domain[:,1]-lambda_domain[:,0])
-        lambda_vol = lambda_vol / float(len(result_vol))
-        nptest.assert_array_equal(result_vol,
-                np.ones(result_vol.shape)*lambda_vol)
-        
-    def compare_to_vol_ae(result_vol, lambda_domain):
-        """
-        Compare ``lambda_vol`` from the algorithm to an analytic solution.
-
-        :param result_vol: lambda_vol from any of the methods in
-            :mod:`~bet.calculatevol.calculatevol`
-        :type result_vol: :class:`numpy.ndarray`
-
-        """
-        lambda_vol = np.product(lambda_domain[:,1]-lambda_domain[:,0])
-        lambda_vol = lambda_vol / float(len(result_vol))
-        nptest.assert_array_almost_equal_nulp(result_vol,
-                np.ones(result_vol.shape)*lambda_vol)
-
-    def compare_prob_dtree(result_wtree, result_wotree):
-        """
-
-        Make sure the output from
-        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
-        option arguments.
-
-        """
-        # calculate with d_tree
-        (P, lem, io_ptr, emulate_ptr) = result_wtree
-        # calculate without d_tree
-        (Pt, lemt, io_ptrt, emulate_ptrt) = result_wotree
-        # Compare results
-        nptest.assert_array_equal(P,Pt)
-        nptest.assert_array_equal(lem, lemt)
-        nptest.assert_array_equal(lem, self.r_lambda_emulate)
-        nptest.assert_array_equal(io_ptrt, ioptr)
-        nptest.assert_array_equal(emulate_ptr, emulate_ptrt)
-
-    def compare_prob_emulate(result_wsamples, result_wosamples):
-        """
-
-        Make sure the output from
-        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
-        ``lambda_emulate`` when ``lambda_emulate == samples``.
-
-        """
-        # calculate with samples
-        (P, lem, io_ptr, emulate_ptr) = result_wsamples
-        # calculate without samples
-        (Pt, lemt, io_ptrt, emulate_ptrt) = result_wosamples
-        # Compare results
-        nptest.assert_array_equal(P,Pt)
-        nptest.assert_array_equal(lem, lemt)
-        nptest.assert_array_equal(lem, self.r_lambda_emulate)
-        nptest.assert_array_equal(io_ptrt,ioptr)
-        nptest.assert_array_equal(emulate_ptr, emulate_ptrt)
-
-    def compare_prob(result_emulated, result_prob, result_mc):
-        """
-
-        Make sure that the output from 
-        :meth:`~bet.calculateP.calculateP.prob_emulated`,
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when ``lambda_emulate == samples``.
-
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-
-        """
-        # Calculate prob 
-        (P, lem, io_ptr, emulate_ptr) = result_emulated
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_mc
-
-        # Compare results
-        nptest.assert_array_equal(P,P1)
-        nptest.assert_array_equal(P,P3)
-        nptest.assert_array_equal(P1,P3)
-
-        nptest.assert_array_equal(lam_vol1,lam_vol3)
-
-        nptest.assert_array_equal(lem,lem1)
-        nptest.assert_array_equal(lem,lem3)
-        nptest.assert_array_equal(lem1,lem3)
-
-        nptest.assert_array_equal(io_ptr,io_ptr1)
-        nptest.assert_array_equal(io_ptr,io_ptr3)
-        nptest.assert_array_equal(io_ptr1,io_ptr3)
-       
-        nptest.assert_array_equal(emulate_ptr,emulate_ptr1)
-        nptest.assert_array_equal(emulate_ptr,emulate_ptr3)
-        nptest.assert_array_equal(emulate_ptr1,emulate_ptr3)
-
-    def compare_volume_rg(result_prob, result_mc):
-        """
-
-        Make sure that the voronoi cell volumes from 
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
-        all on a regular grid and ``lambda_emulate == samples``.
-
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-
-        """
-        # Calculate prob 
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_mc
-
-        nptest.assert_array_equal(lam_vol1,lam_vol3)
-        self.compare_to_mean(lam_vol1)
-        self.compare_to_vol_linear(lam_vol1)
-
-    def compare_volume_ae(result_prob, result_mc):
-        """
-
-        Make sure that the voronoi cell volumes from 
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
-        i.i.d and ``lambda_emulate == samples``.
-
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-
-        """
-        # Calculate prob 
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_mc
-
-        nptest.assert_array_equal(lam_vol1, lam_vol3)
-        self.compare_to_mean_ae(lam_vol1)
-        self.compare_to_vol_linear_ae(lam_vol1)
-
-    def compare_lambda_emulate(result_prob, result_rg, result_iid):
-        """
-        Compare results when lambda_emulate != samples
-            
-            * lambda_emulate is i.i.d. or on a regular grid
-       
-       .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-       
-        """
-        # Calculate prob (has no lambda_emulate)
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = result_prob
-        # Calculate prob_mc (has lambda_emulate), regular grid
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = result_rg
-        # Calculate prob_mc (has lambda_emulate), iid samples
-        (P4, lam_vol4, lem4, io_ptr4, emulate_ptr4) = result_iid
-
-        # Compare results
-        nptest.assert_array_almost_equal_nulp(P1,P3)
-        nptest.assert_array_almost_equal_nulp(lam_vol1, lam_vol3)
-        
-        nptest.assert_array_almost_equal_nulp(P4,P3)
-        nptest.assert_array_almost_equal_nulp(lam_vol4, lam_vol3)
-        
-        nptest.assert_array_almost_equal_nulp(P1,P4)
-        nptest.assert_array_almost_equal_nulp(lam_vol1, lam_vol4)
-
-    def generate_results(self):
-        """
-        Generate the mix of results from
-        :meth:~`bet.calculateP.calculateP.prob_emulated`,
-        :meth:~`bet.calculateP.calculateP.prob`, and
-        :meth:~`bet.calculateP.calculateP.prob_mc` to be used in test
-        subclasses
-        """
-
-        # RESULTS WHERE SAMPLES = LAMBDA_EMULATE
-        # samples are on a regular grid
-        # result_wtree, result_wsamples, result_emulated_rg
-        self.result_emulated_rg = calcP.prob_emulated(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_samples, self.d_Tree)
-        self.result_wtree = result_emulated_rg
-        self.result_wsamples = result_emulated_rg
-        self.result_wotree = calcP.prob_emulated(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_samples)
-        self.result_wosamples = calcP.prob_emulated(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain)
-
-        self.result_prob_rg = calcP.prob(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.d_Tree)
-        self.result_mc_rg = calcP.prob_mc(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_samples, self.d_Tree)
-
-        # samples are iid
-        self.result_emulated_iid = calcP.prob_emulated(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.u_samples, self.d_Tree)
-        self.result_prob_iid = calcP.prob(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.d_Tree)
-        self.result_mc_iid = calcP.prob_mc(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.u_samples, self.d_Tree)
-
-        # RESULTS WHERE SAMPLES != LAMBDA_EMULATE
-        # result_emu_samples_emulatedsamples
-        self.result_emu_rg_rg = calcP.prob_mc(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_lambda_emulate, self.d_Tree)
-        self.result_emu_rg_iid = calcP.prob_mc(self.r_samples, self.r_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.u_lambda_emulate, self.d_Tree)
-        self.result_emu_iid_rg = calcP.prob_mc(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.r_lambda_emulate, self.d_Tree)
-        self.result_emu_iid_iid = calcP.prob_mc(self.u_samples, self.u_data,
-                self.rho_D_M, self.d_distr_samples, self.lam_domain,
-                self.u_lambda_emulate, self.d_Tree)
-
-
-class LinearModel(CompareProb):
-    """
-    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP` with a linear
-    model.
-    """
-    @classmethod
-    def setUpClass(cls):
-        """
-        Create inputs for ``prob*`` methods. This should run only once per
-        grouping of tests from this class. But, for isolated tests it doesn't
-        make any sense to do it this way. Since many of these tests are
-        comparision tests it should be fine to structure things in this manner
-        rather using inheritance everywhere.
-        """
-        # This might not be the right invocation.
-        super(TestLinearModel, self).setUpClass(TestLinearModel)
-        # linear model
-        self.rl_data = np.dot(r_samples, data_domain)
-        self.ul_data = np.dot(u_samples, data_domain)
-
-class NonLinearModel(CompareProb):
-    """
-    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP` with a linear
-    model.
-    """
-    @classmethod
-    def setUpClass(cls):
-        """
-        Create inputs for ``prob*`` methods. This should run only once per
-        grouping of tests from this class. But, for isolated tests it doesn't
-        make any sense to do it this way. Since many of these tests are
-        comparision tests it should be fine to structure things in this manner
-        rather using inheritance everywhere.
-        """
-        # This might not be the right invocation.
-        super(NonLinearModel, self).setUpClass(NonLinearModel)
-        # non-linear model
-        def nonlinear_model(l_data):
-            n_data = l_data
-            n_data[:,0] = l_data[:,0] + l_data[:,2]
-            n_data[:,1] = np.square(l_data[:,1])
-            n_data[:,2] = l_data[:,0] - n_data[:,1]
-            return n_data
-        self.rn_data = nonlinear_model(rl_data)
-        self.un_data = nonlinear_model(ul_data)
-
-class TestProbUnifLinear(LinearModel, unittest.TestCase):
-    """
-    Tests ``prob*`` methods in :mod:`bet.calculateP.calculateP`.
-    """
-    @classmethod
-    def setUpClass(cls):
-        """
-        Create inputs for ``prob*`` methods. This should run only once per
-        grouping of tests from this class. But, for isolated tests it doesn't
-        make any sense to do it this way. Since many of these tests are
-        comparision tests it should be fine to structure things in this manner
-        rather using inheritance everywhere.
-        """
-        # This might not be the right invocation.
-        super(TestProbUnifLinear, self).setUpClass(TestProbUnifLinear)
-    
-        # Create rho_D_M associated inputs (rho_D_M, d_distr_samples, d_tree)
-        # UNIFORM
-        self.d_distr_dsamples = np.mean(self.data_domain, 1)
-        self.rho_D_M = 1
-        self.d_Tree = spatial.KDTree(self.d_distr_samples)
-
-        # Generate results
-        super(TestProbUnifLinear, self).generate_results(self)
-
-    def compare_to_unif_linear(result_P):
-        """
-        Compare P from the algorithm to an analytic solution where $P_\mathcal{D}$ is uniform
-        over $\mathbf{D}$ (linear model only).
-
-        :param result_P: P from any of the methods in
-            :mod:`~bet.calculateP.calculateP`
-        :type result_P: :class:`numpy.ndarray`
-
-        """
-        nptest.assert_array_equal(result_P,
-                np.ones(result_P.shape)/float(len(result_P)))
-        
-    def compare_to_unif_linear_ae(result_P):
-        """
-        Compare P from the algorithm to an analytic solution where $P_\mathcal{D}$ is uniform
-        over $\mathbf{D}$ (linear model only).
-
-        :param result_P: P from any of the methods in
-            :mod:`~bet.calculateP.calculateP`
-        :type result_P: :class:`numpy.ndarray`
-
-        """
-        nptest.assert_array_almost_equal_nulp(result_P,
-                np.ones(result_P.shape)/float(len(result_P)))
-
-    def test_prob_dtree(self):
-        """
-
-        Make sure the output from
-        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
-        option arguments.
-
-        """  
-        compare_prob_dtree(self.result_wtree, self.result_wotree)
-
-    def test_prob_emulate(self):
-        """
-
-        Make sure the output from
-        :meth:`bet.calcuateP.calculateP.prob_emulated` matches with and without
-        ``lambda_emulate`` when ``lambda_emulate == samples``.
-
-        """
-        compare_prob_emulate(self.result_wsamples, self.result_wosamples)
-
-    def test_prob_rg(self):
-        """
-
-        Make sure that the output from 
-        :meth:`~bet.calculateP.calculateP.prob_emulated`,
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
-        all on a regular grid when ``lambda_emulate == samples``.
-
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-
-        """
-        compare_prob(self.result_emulated_rg, self.result_prob_rg,self.result_mc_rg)
-        compare_volume_rg(self.results_prob_rg, self.result_mc_rg)
-
-    def test_prob_iid(self):
-        """
-
-        Make sure that the output from 
-        :meth:`~bet.calculateP.calculateP.prob_emulated`,
-        :meth:`~bet.calculateP.calculateP.prob`,
-        :meth:`~bet.calculateP.calculateP.prob_mc` matches when the samples are
-        i.i.d. with respect to the Lebesgue measure when ``lambda_emulate == samples``.
-
-        .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-
-        """
-        compare_prob(self.result_emulated_iid, self.result_prob_iid,
-                self.result_mc_iid)
-        compare_volume_ae(self.results_prob_iid, self.result_mc_iid)
- 
-    def test_l_emulate_rg(self):
-        """
-        Compare results when lambda_emulate != samples
-            
-            * samples are on a regular grid
-            * lambda_emulate is i.i.d. or on a regular grid
-       
-       .. note::
-            This method also needs to include
-            :meth:`~bet.calculateP.calculateP.prob_qhull` if and only if the
-            user has the Python `pyhull <http://pythonhosted.org/pyhull>`_
-            package installed.
-       
-        """
-        compare_lambda_emulate(self.result_prob_rg, self.result_emu_rg_rg,
-                self.result_emu_rg_iid)
-        compare_volume_ae(
-        compare_volume_ae(
-        compare_volume_ae(
-
-        # Calculate prob (has no lambda_emulate)
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = calc.prob(self.u_samples,
-                self.ul_data, self.u_rho, self.u_dsamples, self.lam_domain,
-                self.ud_tree)
-
-        # Calculate prob_mc (has lambda_emulate)
-        (P3, lam_vol3, lem3, io_ptr3, emulate_ptr3) = calc.prob_mc(self.u_samples,
-                self.ul_data, self.u_rho, self.u_dsamples, self.lam_domain,
-                self.u_lambda_emulate, self.ud_tree)
-
-        # Compare to mean
-        self.compare_to_mean_ae(P3)
-        self.compare_to_mean_ae(lam_vol3)
-
-        (P4, lam_vol4, lem4, io_ptr4, emulate_ptr4) = calc.prob_mc(self.u_samples,
-                self.ul_data, self.u_rho, self.u_dsamples, self.lam_domain,
-                self.u_lambda_emulate, self.ud_tree)
-
-        # Compare to mean
-        self.compare_to_mean_ae(P4)
-        self.compare_to_mean_ae(lam_vol4)
-
-        # Compare results
-        nptest.assert_array_almost_equal_nulp(P1,P3)
-        nptest.assert_array_almost_equal_nulp(lam_vol1,lam_vol3)
-        
-        nptest.assert_array_almost_equal_nulp(P4,P3)
-        nptest.assert_array_almost_equal_nulp(lam_vol4,lam_vol3)
-        
-        nptest.assert_array_almost_equal_nulp(P1,P4)
-        nptest.assert_array_almost_equal_nulp(lam_vol1,lam_vol4
-                )
-
-    def test_compare_to_analytic_solution(self):
-        pass
diff --git a/test/test_postProcess/test_plotP.py b/test/test_postProcess/test_plotP.py
index 1d3285a8..d60cf871 100644
--- a/test/test_postProcess/test_plotP.py
+++ b/test/test_postProcess/test_plotP.py
@@ -163,7 +163,7 @@ def test_plot_marginals_1D(self):
             go = True
         except (RuntimeError, TypeError, NameError):
             go = False
-            nptest.assert_equal(go, True)
+        nptest.assert_equal(go, True)
 
     def test_plot_marginals_2D(self):
         """
@@ -180,4 +180,4 @@ def test_plot_marginals_2D(self):
             go = True
         except (RuntimeError, TypeError, NameError):
             go = False
-            nptest.assert_equal(go, True)
+        nptest.assert_equal(go, True)

From 488f46ddc48a5d58e6ce8aba284193472949a867 Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Wed, 8 Apr 2015 11:58:24 -0600
Subject: [PATCH 18/85] changes to util

---
 bet/util.py | 57 ++++++++++++++++++++++++++++++++---------------------
 1 file changed, 34 insertions(+), 23 deletions(-)

diff --git a/bet/util.py b/bet/util.py
index 378c3130..ec904609 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -14,7 +14,7 @@ def meshgrid_ndim(X):
     Make N-D coordinate arrays for vectorized evaluations of
     N-D scalar/vector fields over N-D grids, given
     one-dimensional coordinate arrays (x1, x2,..., xn).
-    
+
 
     :param X: A tuple containing the 1d coordinate arrays
     :type X: tuple
@@ -22,28 +22,39 @@ def meshgrid_ndim(X):
     :returns: X_new
     """
     n = len(X)
-    alist=[]
+    alist = []
     for i in range(n):
         alist.append(X[i])
-    for i in range(n,10):
+    for i in range(n, 10):
         alist.append(np.array([0]))
-    a,b,c,d,e,f,g,h,i,j = np.meshgrid(alist[0],
-                                      alist[1],
-                                      alist[2],
-                                      alist[3],
-                                      alist[4],
-                                      alist[5],
-                                      alist[6],
-                                      alist[7],
-                                      alist[8],
-                                      alist[9],
-                                      indexing='ij')
+    a, b, c, d, e, f, g, h, i, j = np.meshgrid(alist[0],
+                                               alist[1],
+                                               alist[2],
+                                               alist[3],
+                                               alist[4],
+                                               alist[5],
+                                               alist[6],
+                                               alist[7],
+                                               alist[8],
+                                               alist[9],
+                                               indexing='ij')
+
+    X_new = np.vstack(
+        (a.flat[:],
+         b.flat[:],
+         c.flat[:],
+         d.flat[:],
+         e.flat[:],
+         f.flat[:],
+         g.flat[:],
+         h.flat[:],
+         i.flat[:],
+         j.flat[:])).transpose()
+    X_new = X_new[:, 0:n]
 
-    X_new = np.vstack((a.flat[:],b.flat[:],c.flat[:],d.flat[:],e.flat[:],f.flat[:],g.flat[:],h.flat[:],i.flat[:],j.flat[:])).transpose()
-    X_new  = X_new[:,0:n]
-    
     return X_new
-    
+
+
 def get_global_values(array):
     """
     TODO: write a version that also works with captial mpi4py method calls
@@ -52,13 +63,13 @@ def get_global_values(array):
     Concatenates local arrays into global array.
 
     :param array: Array.
-    :type P_samples: :class:'~numpy.ndarray' 
-    :rtype: :class:'~numpy.ndarray' 
+    :type P_samples: :class:'~numpy.ndarray'
+    :rtype: :class:'~numpy.ndarray'
     :returns: array
     """
-    dir=len(array.shape)
-    array = comm.allgather(array, array)   
-    if dir==1:
+    a_shape = len(array.shape)
+    array = comm.allgather(array, array)
+    if a_shape == 1:
         return np.hstack(array)
     else:
         return np.vstack(array)

From f9569d92b8da204f4a68459c02e95aff0b91a187 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Wed, 8 Apr 2015 12:36:40 -0600
Subject: [PATCH 19/85] small changes to adaptive sampling, finished draft of
 adaptiveSampling.sampler tests

---
 bet/sampling/adaptiveSampling.py            |  72 +++---
 test/test_sampling/test_adaptiveSampling.py | 257 ++++++++++++++------
 2 files changed, 214 insertions(+), 115 deletions(-)

diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 58be8d7d..04901f10 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -88,12 +88,13 @@ def update_mdict(self, mdict):
         mdict['sample_batch_no'] = self.sample_batch_no
         
     def run_gen(self, kern_list, rho_D, maximum, param_min, param_max,
-            t_kernel, savefile, initial_sample_type="lhs", criterion='center'):
+            t_set, savefile, initial_sample_type="lhs", criterion='center'):
         """
         Generates samples using generalized chains and a list of different
         kernels.
 
-        :param list() kern_list: List of kernels.
+        :param list() kern_list: List of
+            :class:~`bet.sampling.adaptiveSampling.kernel` objects.
         :param rho_D: probability density on D
         :type rho_D: callable function that takes a :class:`np.array` and
             returns a :class:`numpy.ndarray`
@@ -102,11 +103,9 @@ def run_gen(self, kern_list, rho_D, maximum, param_min, param_max,
         :type param_min: np.array (ndim,)
         :param param_max: maximum value for each parameter dimension
         :type param_max: np.array (ndim,)
-        :param t_kernel: method for creating new parameter steps using
+        :param t_set: method for creating new parameter steps using
             given a step size based on the paramter domain size
-        :type t_kernel: :class:~`t_kernel`
-        :param function kernel: functional that acts on the data used to
-            determine the proposed change to the ``step_size``
+        :type t_set: :class:~`bet.sampling.adaptiveSampling.transition_set`
         :param string savefile: filename to save samples and data
         :param string initial_sample_type: type of initial sample random (or r),
             latin hypercube(lhs), or space-filling curve(TBD)
@@ -124,7 +123,7 @@ def run_gen(self, kern_list, rho_D, maximum, param_min, param_max,
         mean_ss = list()
         for kern in kern_list:
             (samples, data, step_sizes) = self.generalized_chains(
-                    param_min, param_max, t_kernel, kern, savefile,
+                    param_min, param_max, t_set, kern, savefile,
                     initial_sample_type, criterion)
             results.append((samples, data))
             r_step_size.append(step_sizes)
@@ -135,7 +134,7 @@ def run_gen(self, kern_list, rho_D, maximum, param_min, param_max,
 
     # TODO This appears to be an unused function
     def run_reseed(self, kern_list, rho_D, maximum, param_min, param_max,
-            t_kernel, savefile, initial_sample_type="lhs", criterion='center',
+            t_set, savefile, initial_sample_type="lhs", criterion='center',
             reseed=3):
         """
         Generates samples using reseeded chains and a list of different
@@ -143,7 +142,8 @@ def run_reseed(self, kern_list, rho_D, maximum, param_min, param_max,
 
         THIS IS NOT OPERATIONAL DO NOT USE.
 
-        :param list() kern_list: List of kernels.
+        :param list() kern_list: List of
+            :class:~`bet.sampling.adaptiveSampling.kernel` objects.
         :param rho_D: probability density on D
         :type rho_D: callable function that takes a :class:`np.array` and
             returns a :class:`numpy.ndarray`
@@ -152,11 +152,9 @@ def run_reseed(self, kern_list, rho_D, maximum, param_min, param_max,
         :type param_min: np.array (ndim,)
         :param param_max: maximum value for each parameter dimension
         :type param_max: np.array (ndim,)
-        :param t_kernel: method for creating new parameter steps using
+        :param t_set: method for creating new parameter steps using
             given a step size based on the paramter domain size
-        :type t_kernel: :class:~`t_kernel`
-        :param function kernel: functional that acts on the data used to
-            determine the proposed change to the ``step_size``
+        :type t_set: :class:~`bet.sampling.adaptiveSampling.transition_set`
         :param string savefile: filename to save samples and data
         :param string initial_sample_type: type of initial sample random (or r),
             latin hypercube(lhs), or space-filling curve(TBD)
@@ -175,7 +173,7 @@ def run_reseed(self, kern_list, rho_D, maximum, param_min, param_max,
         mean_ss = list()
         for kern in kern_list:
             (samples, data, step_sizes) = self.reseed_chains(
-                    param_min, param_max, t_kernel, kern, savefile,
+                    param_min, param_max, t_set, kern, savefile,
                     initial_sample_type, criterion, reseed)
             results.append((samples, data))
             r_step_size.append(step_sizes)
@@ -206,11 +204,9 @@ def run_tk(self, init_ratio, min_ratio, max_ratio, rho_D, maximum,
         :type param_min: np.array (ndim,)
         :param param_max: maximum value for each parameter dimension
         :type param_max: np.array (ndim,)
-        :param t_kernel: method for creating new parameter steps using
-            given a step size based on the paramter domain size
-        :type t_kernel: :class:~`t_kernel`
-        :param function kernel: functional that acts on the data used to
+        :param kernel: functional that acts on the data used to
             determine the proposed change to the ``step_size``
+        :type kernel: :class:~`bet.sampling.adaptiveSampling.kernel` object.
         :param string savefile: filename to save samples and data
         :param string initial_sample_type: type of initial sample random (or r),
             latin hypercube(lhs), or space-filling curve(TBD)
@@ -226,9 +222,9 @@ def run_tk(self, init_ratio, min_ratio, max_ratio, rho_D, maximum,
         results_rD = list()
         mean_ss = list()
         for i, j, k  in zip(init_ratio, min_ratio, max_ratio):
-            tk = transition_set(i, j, k)
+            ts = transition_set(i, j, k)
             (samples, data, step_sizes) = self.generalized_chains(
-                    param_min, param_max, tk, kernel, savefile,
+                    param_min, param_max, ts, kernel, savefile,
                     initial_sample_type, criterion)
             results.append((samples, data))
             r_step_size.append(step_sizes)
@@ -238,7 +234,7 @@ def run_tk(self, init_ratio, min_ratio, max_ratio, rho_D, maximum,
         return (results, r_step_size, results_rD, sort_ind, mean_ss)
 
     def run_inc_dec(self, increase, decrease, tolerance, rho_D, maximum,
-            param_min, param_max, t_kernel, savefile,
+            param_min, param_max, t_set, savefile,
             initial_sample_type="lhs", criterion='center'):
         """
         Generates samples using generalized chains and
@@ -257,11 +253,9 @@ def run_inc_dec(self, increase, decrease, tolerance, rho_D, maximum,
         :type param_min: np.array (ndim,)
         :param param_max: maximum value for each parameter dimension
         :type param_max: np.array (ndim,)
-        :param t_kernel: method for creating new parameter steps using
+        :param t_set: method for creating new parameter steps using
             given a step size based on the paramter domain size
-        :type t_kernel: :class:~`t_kernel`
-        :param function kernel: functional that acts on the data used to
-            determine the proposed change to the ``step_size``
+        :type t_set: :class:~`bet.sampling.adaptiveSampling.transition_set`
         :param string savefile: filename to save samples and data
         :param string initial_sample_type: type of initial sample random (or r),
             latin hypercube(lhs), or space-filling curve(TBD)
@@ -276,9 +270,9 @@ def run_inc_dec(self, increase, decrease, tolerance, rho_D, maximum,
         for i, j, z in zip(increase, decrease, tolerance):
             kern_list.append(rhoD_kernel(maximum, rho_D, i, j, z)) 
         return self.run_gen(kern_list, rho_D, maximum, param_min, param_max,
-                t_kernel, savefile, initial_sample_type, criterion)
+                t_set, savefile, initial_sample_type, criterion)
 
-    def generalized_chains(self, param_min, param_max, t_kernel, kern,
+    def generalized_chains(self, param_min, param_max, t_set, kern,
             savefile, initial_sample_type="lhs", criterion='center'):
         """
         Basic adaptive sampling algorithm using generalized chains.
@@ -289,11 +283,12 @@ def generalized_chains(self, param_min, param_max, t_kernel, kern,
         :type param_min: np.array (ndim,)
         :param param_max: maximum value for each parameter dimension
         :type param_max: np.array (ndim,)
-        :param t_kernel: method for creating new parameter steps using
+        :param t_set: method for creating new parameter steps using
             given a step size based on the paramter domain size
-        :type t_kernel: :class:~`t_kernel`
+        :type t_set: :class:~`bet.sampling.adaptiveSampling.transition_set`
         :param function kern: functional that acts on the data used to
             determine the proposed change to the ``step_size``
+        :type kernel: :class:~`bet.sampling.adaptiveSampling.kernel` object.
         :param string savefile: filename to save samples and data
         :param string criterion: latin hypercube criterion see 
             `PyDOE <http://pythonhosted.org/pyDOE/randomized.html>`_
@@ -317,9 +312,9 @@ def generalized_chains(self, param_min, param_max, t_kernel, kern,
         param_right = np.repeat([param_max], self.num_chains_pproc, 0)
         param_width = param_right - param_left
         # Calculate step_size
-        max_ratio = t_kernel.max_ratio
-        min_ratio = t_kernel.min_ratio
-        step_ratio = t_kernel.init_ratio*np.ones(self.num_chains_pproc)
+        max_ratio = t_set.max_ratio
+        min_ratio = t_set.min_ratio
+        step_ratio = t_set.init_ratio*np.ones(self.num_chains_pproc)
        
         # Initiative first batch of N samples (maybe taken from latin
         # hypercube/space-filling curve to fully explore parameter space - not
@@ -347,7 +342,7 @@ def generalized_chains(self, param_min, param_max, t_kernel, kern,
         for batch in xrange(1, self.chain_length):
             # For each of N samples_old, create N new parameter samples using
             # transition set and step_ratio. Call these samples samples_new.
-            samples_new = t_kernel.step(step_ratio, param_width,
+            samples_new = t_set.step(step_ratio, param_width,
                     param_left, param_right, MYsamples_old)
             
             # Solve the model for the samples_new.
@@ -401,27 +396,30 @@ def generalized_chains(self, param_min, param_max, t_kernel, kern,
         return (samples, data, all_step_ratios)
         
     #TODO MOve this function to a dev branch since it is not implemented.
-    def reseed_chains(self, param_min, param_max, t_kernel, kern,
+    def reseed_chains(self, param_min, param_max, t_set, kern,
             savefile, initial_sample_type="lhs", criterion='center', reseed=1):
         """
         Basic adaptive sampling algorithm.
 
         NOT YET IMPLEMENTED.
-       
+
         :param string initial_sample_type: type of initial sample random (or r),
             latin hypercube(lhs), or space-filling curve(TBD)
         :param param_min: minimum value for each parameter dimension
         :type param_min: np.array (ndim,)
         :param param_max: maximum value for each parameter dimension
         :type param_max: np.array (ndim,)
-        :param t_kernel: method for creating new parameter steps using
+        :param t_set: method for creating new parameter steps using
             given a step size based on the paramter domain size
-        :type t_kernel: :class:~`t_kernel`
+        :type t_set: :class:~`bet.sampling.adaptiveSampling.transition_set`
         :param function kern: functional that acts on the data used to
             determine the proposed change to the ``step_size``
+        :type kernel: :class:~`bet.sampling.adaptiveSampling.kernel` object.
         :param string savefile: filename to save samples and data
         :param string criterion: latin hypercube criterion see 
             `PyDOE <http://pythonhosted.org/pyDOE/randomized.html>`_
+
+
         :param int reseed: number of times to reseed the chains
         :rtype: tuple
         :returns: (``parameter_samples``, ``data_samples``) where
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 42b3fa39..8555bfca 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -5,7 +5,7 @@
 import unittest, os
 import numpy.testing as nptest
 import numpy as np
-import bet.sampling.basicSampling as bsam
+import bet.sampling.adaptiveSampling as asam
 import scipy.io as sio
 
 """
@@ -14,82 +14,113 @@
 for kernels but do not implement
 """
 
-def test_loadmat():
+def test_loadmat_init():
     """
-    Tests :meth:`bet.sampling.basicSampling.loadmat`
+    Tests :meth:`bet.sampling.adaptiveSampling.loadmat` and
+    :meth:`bet.sampling.adaptiveSampling.sampler.init`.
     """
+    chain_length = 10
     mdat1 = {'samples':np.random.random((5,1)),
-            'data':np.random.random((5,1)), 'num_samples':5}
-    mdat2 = {'samples':np.random.random((6,1)), 'num_samples':6}
+            'data':np.random.random((5,1)), 'num_samples':50,
+            'chain_length':chain_length} 
+    mdat2 = {'samples':np.random.random((6,1)),
+                    'num_samples':60, 'chain_length':chain_length}
     model = "this is not a model"
+    
+    num_samples = np.array([50, 60])
+    num_chains_pproc1, num_chains_pproc2 = int(np.ceil(num_samples/float(chain_length*size)))
+    num_chains1, num_chains2 = size * num_chains_pproc
+    num_samples1, num_samples2 = chain_length * np.array([num_chains1,
+        num_chains2])
 
     sio.savemat('testfile1', mdat1)
     sio.savemat('testfile2', mdat2)
 
-    (loaded_sampler1, samples1, data1) = bsam.loadmat('testfile1')
+    (loaded_sampler1, samples1, data1) = asam.loadmat('testfile1')
     nptest.assert_array_equal(samples1, mdat1['samples'])
     nptest.assert_array_equal(data1, mdat1['data'])
-    assert loaded_sampler1.num_samples == 5
+    assert loaded_sampler1.num_samples == num_samples1
+    assert loaded_sampler1.chain_length == chain_length
+    assert loaded_sampler1.num_chains_pproc == num_chains_pproc1
+    assert loaded_sampler1.num_chains = num_chains1
+    nptest.assert_array_equal(np.repeat(range(num_chains1), chain_length, 0),
+            loaded_sampler1.sample_batch_no)
     assert loaded_sampler1.lb_model == None
 
-    (loaded_sampler2, samples2, data2) = bsam.loadmat('testfile2', model)
+    (loaded_sampler2, samples2, data2) = asam.loadmat('testfile2', model)
     nptest.assert_array_equal(samples2, mdat2['samples'])
     nptest.assert_array_equal(data2, None)
-    assert loaded_sampler2.num_samples == 6
-    assert loaded_sampler2.lb_model == model
+    assert loaded_sampler2.num_samples == num_samples2
+    assert loaded_sampler2.chain_length == chain_length
+    assert loaded_sampler2.num_chains_pproc == num_chains_pproc2
+    assert loaded_sampler2.num_chains = num_chains2
+    nptest.assert_array_equal(np.repeat(range(num_chains2), chain_length, 0),
+            loaded_sampler2.sample_batch_no)
     os.remove('testfile1.mat')
     os.remove('testfile2.mat')
 
-def verify_random_samples(model, sampler, sample_type, param_min, param_max,
-        num_samples, savefile, parallel):
-    # recreate the samples
-    if num_samples == None:
-        num_samples = self.num_samples
+def verify_random_samples(model, QoI_range, sampler, param_min, param_max,
+        t_set, savefile, initial_sample_type):
+
+    # create indicator function
+    Q_ref = QoI_range*0.5
+    bin_size = 0.15*QoI_range
+    maxiumum = 1/np.product(bin_size)
+    def ifun(outputs):
+        left = np.repeat([Q_ref-.5*bin_size], outputs.shape[0], 0)
+        right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0)
+        left = np.all(np.greater_equal(outputs, left), axis=1)
+        right = np.all(np.less_equal(outputs, right), axis=1)
+        inside = np.logial_and(left, right)
+        max_values = np.repeate(maxium, outputs.shape[0], 0)
+        return inside.astype('float64')*max_values
+        
+    # create rhoD_kernel
+    kernel_rD = asam.rhoD_kernel(maxium, ifun)
+
+    # run generalized chains
+    (samples, data, all_step_ratios) = sampler.generalized_chains(param_min,
+            param_max, t_set, kernel_rD, savefile, inital_sample_type)
+
+    # check dimensions of samples
+    assert samples.shape == (sampler.num_samples, len(param_min))
+
+    # are the samples in bounds?
     param_left = np.repeat([param_min], num_samples, 0)
     param_right = np.repeat([param_max], num_samples, 0)
-    samples = (param_right-param_left)
-    if sample_type == "lhs":
-        samples = samples * lhs(param_min.shape[-1], num_samples, criterion)
-    elif sample_type == "random" or "r":
-        np.random.seed(1)
-        samples = samples * np.random(param_left.shape)
-    samples = samples + param_left
-    # evalulate the model at the samples directly
-    data = model(samples)
-
-    # evaluate the model at the samples
-    # reset the random seed
-    if sample_type == "random" or "r":
-        np.random.seed(1)
-    (my_samples, my_data) = sampler.user_samples(samples, savefile,
-            parallel)
-
-    # make sure that the samples are within the boundaries
     assert np.all(my_samples <= param_right)
     assert np.all(my_samples >= param_left)
 
-    if len(data.shape) == 1:
-        data = np.expand_dims(data, axis=1)
-    if len(samples.shape) == 1:
-        samples = np.expan_dims(samples, axis=1)
+    # check dimensions of data
+    assert data.shape == (sampler.num_samples, len(QoI_range))
+
+    # check dimensions of all_step_ratios
+    assert all_step_ratios.shape == (sampler.num_chains, sampler.chain_length)
+
+    # are all the step ratios of an appropriate size?
+    assert np.all(all_step_ratios >= t_set.min_ratio)
+    assert np.all(all_step_ratios <= t_set.max_ratio)
     
-    # compare the samples
-    nptest.assert_array_equal(samples, my_samples)
-    # compare the data
-    nptest.assert_array_equal(data, my_data)
-    # did num_samples get updated?
-    assert samples.shape[0] == sampler.num_samples
-    # did the file get correctly saved?
-
-    mdat = sio.loadmat('savefile')
+    # did the savefiles get created? (proper number, contain proper keys)
+    mdat = {}
+    if size > 1:
+        mdat =
+        sio.loadmat(os.path.join(os.path.dirname(savefile),"proc{}{}".format(rank,
+            os.path.basename(savefile))))
+    else:
+        mdat = sio.loadmat(savefile)
     nptest.assert_array_equal(samples, mdat['samples'])
     nptest.assert_array_equal(data, mdat['data'])
-    assert samples.shape[0] == sampler.num_samples
-
+    nptest.assert_array_equal(all_step_ratios, mdat['step_ratios'])
+    assert sampler.chain_length == mdat['chain_length']
+    assert sampler.num_samples == mdat['num_samples']
+    assert sampler.num_chains == mdat['num_chains']
+    nptest.assert_array_equal(sampler.sample_batch_no,
+            mdat['sampler_batch_no'])
 
 class Test_basic_sampler(unittest.TestCase):
     """
-    Test :class:`bet.sampling.basicSampling.sampler`.
+    Test :class:`bet.sampling.adaptiveSampling.sampler`.
     """
 
     def setUp(self):
@@ -97,7 +128,7 @@ def setUp(self):
         self.param_min1 = np.zeros((1, ))
         self.param_max1 = np.zeros((1, ))
         def map_1t1(x):
-            return np.sin(x)
+            return x*2.0
         # create 3-1 map
         self.param_min3 = np.zeros((3, ))
         self.param_max3 = np.ones((3, ))
@@ -115,29 +146,34 @@ def map_10t4(x):
             x3 = x[:, 4] + x[:, 5]
             x4 = np.sum(x[:, [6, 7, 8, 9]], 1)
             return np.vstack([x1, x2, x3, x4]).transpose()
-        num_samples = 100
         self.savefiles = ["11t11", "1t1", "3to1", "3to2", "10to4"]
         self.models = [map_1t1, map_1t1, map_3t1, map_3t2, map_10t4]
+        self.QoI_range = [np.array([2.0]), np.array([2.0]), np.array([3.0]),
+                np.array([2.0, 1.0]), np.array([2.0, 2.0, 2.0, 4.0])
+
+        # define parameters for the adaptive sampler
+
+        num_samples = 1000
+        chain_length = 100
+        num_chains_pproc = int(np.ceil(num_samples/float(chain_length*size)))
+        num_chains = size * num_chains_pproc
+        num_samples = chain_length * np.array(num_chains)
+
         self.samplers = []
         for model in self.models:
-            self.samplers.append(bsam.sampler(model, num_samples))
+            self.samplers.append(asam.sampler(num_samples, chain_length,
+                model))
 
     def tearDown(self):
         for f in self.savefiles:
             if os.path.exists(f+".mat"):
                 os.remove(f+".mat")
-
-    def test_init(self):
-        """
-        Test initalization of :class:`bet.sampling.basicSampling.sampler`
-        """
-        assert self.samplers[0].num_samples == 100
-        assert self.samplers[0].lb_model == self.models[0]
-        assert bsam.sampler(self.models[0], None).num_samples == None
-
-    @unittest.skip("Skipping testing saving")
-    def test_save(self):
-        pass
+        if size > 1:
+            for f in self.savefiles:
+                for proc in range(size):
+                    proc_savefile = os.path.join(os.path.dirname(f),
+                            "proc{}{}".format(rank,
+                                os.path.basename(savefile)))
 
     def test_update(self):
         """
@@ -146,25 +182,90 @@ def test_update(self):
         mdict = {"frog":3, "moose":2}
         self.samplers[0].update_mdict(mdict)
         assert self.samplers[0].num_samples == mdict["num_samples"]
+        assert self.samplers[0].chain_length == mdict["chain_length"]
+        assert self.samplers[0].num_chains == mdict["num_chains"]
+        nptest.assert_array_equal(self.samplers[0].sample_batch_no,
+                np.repeat(range(num_chains), chain_length, 0))
+    
+    @unittest.skip("Implement me")
+    def test_run_gen(self):
+        # sampler.run_gen(kern_list, rho_D, maximum, param_min, param_max,
+        # t_set, savefile, initial_sample_type)
+        # returns list where each member is a tuple ((samples, data),
+        # all_step_ratios, num_high_prob_samples,
+        # sorted_indices_of_num_high_prob_samples, average_step_ratio)
+        pass
+    @unittest.skip("Implement me")
+    def test_run_tk(self):
+        # sampler.run_tk(init_ratio, min_raio, max_ratio, rho_D, maximum,
+        # param_min, param_max, kernel, savefile, intial_sample_type)
+        # returns list where each member is a tuple ((samples, data),
+        # all_step_ratios, num_high_prob_samples,
+        # sorted_indices_of_num_high_prob_samples, average_step_ratio)
+        pass
+    @unittest.skip("Implement me")
+    def test_run_inc_dec(self):
+        # sampler.run_inc_dec(increase, decrease, tolerance, rho_D, maximum,
+        # param_min, param_max, t_set, savefile, initial_sample_type)
+        # returns list where each member is a tuple ((samples, data),
+        # all_step_ratios, num_high_prob_samples,
+        # sorted_indices_of_num_high_prob_samples, average_step_ratio)
+        pass
 
-    def test_random_samples(self):
+    def test_generalized_chains(self):
         """
-        Test :met:`bet.sampling.basicSampling.sampler.random_samples` for three
-        different QoI maps (1 to 1, 3 to 1, 3 to 2, 10 to 4).
+        Test :met:`bet.sampling.adaptiveSampling.sampler.generalized_chains`
+        for three different QoI maps (1 to 1, 3 to 1, 3 to 2, 10 to 4).
         """
         param_min_list = [self.param_min1, self.param_min1, self.param_min3,
             self.param_min3, self.param_min10]
         param_max_list = [self.param_max1, self.param_max1, self.param_max3,
             self.param_max3, self.param_max10]
+        # create a transition set
+        t_set = asam.transition_set(.5, .5**5, 1.0) 
+
+        test_list = zip(self.models, self.QoI_range, self.samplers,
+                param_min_list, param_max_list, self.savefiles)
+
+        for model, sampler, param_min, param_max, savefile, rho_D in test_list:
+            for initial_sample_type in ["random", "r", "lhs"]:
+                        yield verify_samples, model, QoI_range, sampler,
+                        param_min, param_max, t_set, savefile, initial_sample_type
+
+@unittest.skip("Implement me")
+def test_kernels():
+        pass
+
+@unittest.skip("Implement me")
+class test_transition_set(unittest):
+    @unittest.skip("Implement me")
+    def test_init():
+        pass
+    @unittest.skip("Implement me")
+    def test_step():
+        pass
+
+@unittest.skip("Implement me")
+class kernel(unittest):
+    @unittest.skip("Implement me")
+    def test_init():
+        pass
+    def test_delta_step():
+        pass
+
+@unittest.skip("Implement me")
+class test_rhoD_kernsl(kernel):
+    pass
+
+@unittest.skip("Implement me")
+class test_maxima_kernsl(kernel):
+    pass
+
+@unittest.skip("Implement me")
+class test_maxima_mean_kernsl(kernel):
+    pass
+
+@unittest.skip("Implement me")
+class test_multi_dist_kernsl(kernel):
+    pass
 
-        test_list = zip(self.models, self.samplers, param_min_list,
-                param_max_list,
-                self.savefiles)
-
-        for model, sampler, param_min, param_max, savefile in test_list:
-            for sample_type in ["random", "r", "lhs"]:
-                for num_samples in [None, 25]:
-                    for parallel in [False, True]:
-                        yield verify_random_samples, model, sampler,
-                        param_min, param_max, savefile, num_samples,
-                        parallel

From 4692282896b0321f31326f91cd68c75dc0d66acb Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Wed, 8 Apr 2015 14:18:40 -0600
Subject: [PATCH 20/85] pylinted partial draft of tests for adaptiveSampling

---
 test/test_sampling/test_adaptiveSampling.py | 48 +++++++++++----------
 1 file changed, 25 insertions(+), 23 deletions(-)

diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 8555bfca..a4145517 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -7,6 +7,7 @@
 import numpy as np
 import bet.sampling.adaptiveSampling as asam
 import scipy.io as sio
+from bet.Comm import *
 
 """
 TODO: rewrite loadmat test, rewrite init test, rewrite update_mdict test, write
@@ -29,7 +30,8 @@ def test_loadmat_init():
     
     num_samples = np.array([50, 60])
     num_chains_pproc1, num_chains_pproc2 = int(np.ceil(num_samples/float(chain_length*size)))
-    num_chains1, num_chains2 = size * num_chains_pproc
+    num_chains1, num_chains2 = size * np.array([num_chains_pproc1,
+        num_chains_pproc2])
     num_samples1, num_samples2 = chain_length * np.array([num_chains1,
         num_chains2])
 
@@ -42,7 +44,7 @@ def test_loadmat_init():
     assert loaded_sampler1.num_samples == num_samples1
     assert loaded_sampler1.chain_length == chain_length
     assert loaded_sampler1.num_chains_pproc == num_chains_pproc1
-    assert loaded_sampler1.num_chains = num_chains1
+    assert loaded_sampler1.num_chains == num_chains1
     nptest.assert_array_equal(np.repeat(range(num_chains1), chain_length, 0),
             loaded_sampler1.sample_batch_no)
     assert loaded_sampler1.lb_model == None
@@ -53,43 +55,43 @@ def test_loadmat_init():
     assert loaded_sampler2.num_samples == num_samples2
     assert loaded_sampler2.chain_length == chain_length
     assert loaded_sampler2.num_chains_pproc == num_chains_pproc2
-    assert loaded_sampler2.num_chains = num_chains2
+    assert loaded_sampler2.num_chains == num_chains2
     nptest.assert_array_equal(np.repeat(range(num_chains2), chain_length, 0),
             loaded_sampler2.sample_batch_no)
     os.remove('testfile1.mat')
     os.remove('testfile2.mat')
 
-def verify_random_samples(model, QoI_range, sampler, param_min, param_max,
+def verify_samples(model, QoI_range, sampler, param_min, param_max,
         t_set, savefile, initial_sample_type):
 
     # create indicator function
     Q_ref = QoI_range*0.5
     bin_size = 0.15*QoI_range
-    maxiumum = 1/np.product(bin_size)
+    maximum = 1/np.product(bin_size)
     def ifun(outputs):
         left = np.repeat([Q_ref-.5*bin_size], outputs.shape[0], 0)
         right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0)
         left = np.all(np.greater_equal(outputs, left), axis=1)
         right = np.all(np.less_equal(outputs, right), axis=1)
         inside = np.logial_and(left, right)
-        max_values = np.repeate(maxium, outputs.shape[0], 0)
+        max_values = np.repeate(maximum, outputs.shape[0], 0)
         return inside.astype('float64')*max_values
         
     # create rhoD_kernel
-    kernel_rD = asam.rhoD_kernel(maxium, ifun)
+    kernel_rD = asam.rhoD_kernel(maximum, ifun)
 
     # run generalized chains
     (samples, data, all_step_ratios) = sampler.generalized_chains(param_min,
-            param_max, t_set, kernel_rD, savefile, inital_sample_type)
+            param_max, t_set, kernel_rD, savefile, initial_sample_type)
 
     # check dimensions of samples
     assert samples.shape == (sampler.num_samples, len(param_min))
 
     # are the samples in bounds?
-    param_left = np.repeat([param_min], num_samples, 0)
-    param_right = np.repeat([param_max], num_samples, 0)
-    assert np.all(my_samples <= param_right)
-    assert np.all(my_samples >= param_left)
+    param_left = np.repeat([param_min], sampler.num_samples, 0)
+    param_right = np.repeat([param_max], sampler.num_samples, 0)
+    assert np.all(samples <= param_right)
+    assert np.all(samples >= param_left)
 
     # check dimensions of data
     assert data.shape == (sampler.num_samples, len(QoI_range))
@@ -104,9 +106,8 @@ def ifun(outputs):
     # did the savefiles get created? (proper number, contain proper keys)
     mdat = {}
     if size > 1:
-        mdat =
-        sio.loadmat(os.path.join(os.path.dirname(savefile),"proc{}{}".format(rank,
-            os.path.basename(savefile))))
+        mdat = sio.loadmat(os.path.join(os.path.dirname(savefile),
+            "proc{}{}".format(rank, os.path.basename(savefile))))
     else:
         mdat = sio.loadmat(savefile)
     nptest.assert_array_equal(samples, mdat['samples'])
@@ -149,7 +150,7 @@ def map_10t4(x):
         self.savefiles = ["11t11", "1t1", "3to1", "3to2", "10to4"]
         self.models = [map_1t1, map_1t1, map_3t1, map_3t2, map_10t4]
         self.QoI_range = [np.array([2.0]), np.array([2.0]), np.array([3.0]),
-                np.array([2.0, 1.0]), np.array([2.0, 2.0, 2.0, 4.0])
+                np.array([2.0, 1.0]), np.array([2.0, 2.0, 2.0, 4.0])]
 
         # define parameters for the adaptive sampler
 
@@ -173,7 +174,7 @@ def tearDown(self):
                 for proc in range(size):
                     proc_savefile = os.path.join(os.path.dirname(f),
                             "proc{}{}".format(rank,
-                                os.path.basename(savefile)))
+                                os.path.basename(f)))
 
     def test_update(self):
         """
@@ -185,7 +186,8 @@ def test_update(self):
         assert self.samplers[0].chain_length == mdict["chain_length"]
         assert self.samplers[0].num_chains == mdict["num_chains"]
         nptest.assert_array_equal(self.samplers[0].sample_batch_no,
-                np.repeat(range(num_chains), chain_length, 0))
+                np.repeat(range(self.smaplers[0].num_chains),
+                    self.samplers[0].chain_length, 0))
     
     @unittest.skip("Implement me")
     def test_run_gen(self):
@@ -227,7 +229,7 @@ def test_generalized_chains(self):
         test_list = zip(self.models, self.QoI_range, self.samplers,
                 param_min_list, param_max_list, self.savefiles)
 
-        for model, sampler, param_min, param_max, savefile, rho_D in test_list:
+        for model, QoI_range, sampler, param_min, param_max, savefile, rho_D in test_list:
             for initial_sample_type in ["random", "r", "lhs"]:
                         yield verify_samples, model, QoI_range, sampler,
                         param_min, param_max, t_set, savefile, initial_sample_type
@@ -239,18 +241,18 @@ def test_kernels():
 @unittest.skip("Implement me")
 class test_transition_set(unittest):
     @unittest.skip("Implement me")
-    def test_init():
+    def test_init(self):
         pass
     @unittest.skip("Implement me")
-    def test_step():
+    def test_step(self):
         pass
 
 @unittest.skip("Implement me")
 class kernel(unittest):
     @unittest.skip("Implement me")
-    def test_init():
+    def test_init(self):
         pass
-    def test_delta_step():
+    def test_delta_step(self):
         pass
 
 @unittest.skip("Implement me")

From 3ac817928c390880ab53d001f55cb4d98af9717f Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Wed, 8 Apr 2015 14:41:06 -0600
Subject: [PATCH 21/85] first half of adaptive sampling testing completed

---
 bet/sampling/adaptiveSampling.py            |  4 ++--
 test/test_sampling/test_adaptiveSampling.py | 18 +++++++++---------
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 04901f10..8568c93a 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -45,8 +45,8 @@ def loadmat(save_file, lb_model=None):
     else:
         data = None
     # recreate the sampler
-    new_sampler = sampler(mdat['num_samples'], mdat['chain_length'],
-            lb_model)
+    new_sampler = sampler(mdat['num_samples'],
+            np.squeeze(mdat['chain_length']), lb_model)
     
     return (new_sampler, samples, data)
 
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index a4145517..001f8eaa 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -29,7 +29,7 @@ def test_loadmat_init():
     model = "this is not a model"
     
     num_samples = np.array([50, 60])
-    num_chains_pproc1, num_chains_pproc2 = int(np.ceil(num_samples/float(chain_length*size)))
+    num_chains_pproc1, num_chains_pproc2 = np.ceil(num_samples/float(chain_length*size)).astype('int')
     num_chains1, num_chains2 = size * np.array([num_chains_pproc1,
         num_chains_pproc2])
     num_samples1, num_samples2 = chain_length * np.array([num_chains1,
@@ -119,7 +119,7 @@ def ifun(outputs):
     nptest.assert_array_equal(sampler.sample_batch_no,
             mdat['sampler_batch_no'])
 
-class Test_basic_sampler(unittest.TestCase):
+class Test_adaptive_sampler(unittest.TestCase):
     """
     Test :class:`bet.sampling.adaptiveSampling.sampler`.
     """
@@ -186,7 +186,7 @@ def test_update(self):
         assert self.samplers[0].chain_length == mdict["chain_length"]
         assert self.samplers[0].num_chains == mdict["num_chains"]
         nptest.assert_array_equal(self.samplers[0].sample_batch_no,
-                np.repeat(range(self.smaplers[0].num_chains),
+                np.repeat(range(self.samplers[0].num_chains),
                     self.samplers[0].chain_length, 0))
     
     @unittest.skip("Implement me")
@@ -239,7 +239,7 @@ def test_kernels():
         pass
 
 @unittest.skip("Implement me")
-class test_transition_set(unittest):
+class test_transition_set(unittest.TestCase):
     @unittest.skip("Implement me")
     def test_init(self):
         pass
@@ -248,7 +248,7 @@ def test_step(self):
         pass
 
 @unittest.skip("Implement me")
-class kernel(unittest):
+class kernel(unittest.TestCase):
     @unittest.skip("Implement me")
     def test_init(self):
         pass
@@ -256,18 +256,18 @@ def test_delta_step(self):
         pass
 
 @unittest.skip("Implement me")
-class test_rhoD_kernsl(kernel):
+class test_rhoD_kernel(kernel):
     pass
 
 @unittest.skip("Implement me")
-class test_maxima_kernsl(kernel):
+class test_maxima_kernel(kernel):
     pass
 
 @unittest.skip("Implement me")
-class test_maxima_mean_kernsl(kernel):
+class test_maxima_mean_kernel(kernel):
     pass
 
 @unittest.skip("Implement me")
-class test_multi_dist_kernsl(kernel):
+class test_multi_dist_kernel(kernel):
     pass
 

From 1e65923cab7c961b02e6968c747e8f6e7868edb4 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Wed, 8 Apr 2015 15:14:58 -0600
Subject: [PATCH 22/85] added example input files to repo

---
 examples/fromFileMap/Q_2D.mat | Bin 0 -> 41481 bytes
 examples/fromFileMap/Q_3D.mat | Bin 0 -> 767744 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 examples/fromFileMap/Q_2D.mat
 create mode 100644 examples/fromFileMap/Q_3D.mat

diff --git a/examples/fromFileMap/Q_2D.mat b/examples/fromFileMap/Q_2D.mat
new file mode 100644
index 0000000000000000000000000000000000000000..575f361579badfa60fea085bbcb12cfb269b7945
GIT binary patch
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HcmV?d00001


From a6e6de1788fc4a952d8580e67b4cfc06b1d66e4d Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Wed, 8 Apr 2015 17:11:27 -0600
Subject: [PATCH 23/85] working on tests for plotDomains

---
 bet/postProcess/plotDomains.py              |   8 +-
 test/test_Comm.py                           |   1 +
 test/test_postProcess/test_plotDomains.py   | 153 ++++++++++++++++++++
 test/test_sampling/test_adaptiveSampling.py |   4 +-
 test/test_sampling/test_basicSampling.py    |   3 +-
 test/test_util.py                           |   1 +
 6 files changed, 165 insertions(+), 5 deletions(-)

diff --git a/bet/postProcess/plotDomains.py b/bet/postProcess/plotDomains.py
index 5d02154d..efad0e65 100644
--- a/bet/postProcess/plotDomains.py
+++ b/bet/postProcess/plotDomains.py
@@ -126,20 +126,22 @@ def show_param(samples, data, rho_D=None, p_ref=None, sample_nos=None,
     :type p_ref: :class:`np.ndarray`
     :param boolean save: flag whether or not to save the figure
     :param boolean show: flag whether or not to show the figure
+    :param list lnums: integers representing parameter domain coordinate
+        numbers
 
     """
    
     if rho_D != None:
         rD = rho_D(data)
     if lnums == None:
-        lnums = 1+np.array(range(data.shape[1]))
+        lnums = 1+np.array(range(samples.shape[1]))
     xlabel = r'$\lambda_{'+str(lnums[0])+'}$'
     ylabel = r'$\lambda_{'+str(lnums[1])+'}$'
     savename = 'param_samples_cs.eps'
-    if data.shape[1] == 2:
+    if samples.shape[1] == 2:
         scatter_2D(samples, sample_nos, rD, p_ref, save, show, xlabel, ylabel,
                 savename)
-    elif data.shape[1] == 3:
+    elif samples.shape[1] == 3:
         zlabel = r'$\lambda_{'+str(lnums[2])+'}$'
         scatter_3D(samples, sample_nos, rD, p_ref, save, show, xlabel, ylabel,
                 zlabel, savename)
diff --git a/test/test_Comm.py b/test/test_Comm.py
index f57e9c73..3cb98a7e 100644
--- a/test/test_Comm.py
+++ b/test/test_Comm.py
@@ -1,3 +1,4 @@
+# Lindley Graham 04/07/2015
 """
 This module contains unittests for :mod:`~bet.Comm`
 """
diff --git a/test/test_postProcess/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
index e69de29b..fa2792a3 100644
--- a/test/test_postProcess/test_plotDomains.py
+++ b/test/test_postProcess/test_plotDomains.py
@@ -0,0 +1,153 @@
+# Lindley Graham 04/07/2015
+"""
+This module contains tests for :module:`bet.postProcess.plotDomains`.
+
+
+Tests for the execution of plotting parameter and data domains.
+"""
+
+import unittest, os, glob
+import bet.postProcess.plotD as plotDomains
+import numpy as np
+import numpy.testing as nptest
+from bet.Comm import *
+
+class Test_calc_marg_2D(unittest.TestCase):
+    """
+    Test :meth:`bet.postProcess.plotP.calculate_1D_marginal_probs` and  :meth:`bet.postProcess.plotP.calculate_2D_marginal_probs` for a 2D
+    parameter space.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.lam_domain=np.array([[0.0,1.0],[0.0,1.0], [0.0, 1.0]])
+        self.samples=util.meshgrid_ndim((np.linspace(self.lam_domain[0][0],
+            self.lam_domain[0][1], 10),np.linspace(self.lam_domain[1][0],
+                self.lam_domain[1][1], 10), np.linspace(self.lam_domain[1][0],
+                    self.lam_domain[1][1], 10)))
+        self.data = self.samples*3.0
+        self.P_samples = (1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
+        self.filename("testfigure")
+        
+        QoI_range = np.array([3.0, 3.0, 3.0])
+        Q_ref = QoI_range*0.5
+	bin_size = 0.15*QoI_range
+	maximum = 1/np.product(bin_size)
+	def ifun(outputs):
+	    left = np.repeat([Q_ref-.5*bin_size], outputs.shape[0], 0)
+	    right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0)
+	    left = np.all(np.greater_equal(outputs, left), axis=1)
+	    right = np.all(np.less_equal(outputs, right), axis=1)
+	    inside = np.logial_and(left, right)
+	    max_values = np.repeate(maximum, outputs.shape[0], 0)
+	    return inside.astype('float64')*max_values
+	self.rho_D = ifun
+        self.lnums = [1,2,3]
+
+    def tearDown(self):
+        """
+        Tear Down problem
+        """
+        # remove any files the we create
+        filenames = glob.glob(self.filename+".*")
+        filenames.append('param_samples_cs.eps')
+        for f in filenames:
+            os.remove(f)
+
+    def test_scatter_2D():
+        """
+        Test :meth:`bet.postProcess.plotDomains.scatter_2D`
+        """
+        sample_nos = [None, 25]
+        p_ref = [None, self.samples[4,[0, 1]]]
+        save = [True, False]
+        for sn in sample_nos:
+            for pr in p_ref:
+                for s in save:
+                    yield self.check_scatter_2D, sn, pr, s
+
+    def check_scatter_2D(self, sample_nos, p_ref, save):
+        try:
+            plotDomains.scatter_2D(self.samples[:,[0, 1]], sample_nos,
+                self.P_samples, p_ref, save, False, 'XLABEL', 'YLABEL',
+                self.filename)
+            go = True
+        except (RuntimeError, TypeError, NameError):
+            go = False
+        nptest.assert_equal(go, True)
+ 
+    def test_scatter_3D():
+        """
+        Test :meth:`bet.postProcess.plotDomains.scatter_3D`
+        """
+        sample_nos = [None, 25]
+        p_ref = [None, self.samples[4,:]]
+        save = [True, False]
+        for sn in sample_nos:
+            for pr in p_ref:
+                for s in save:
+                    yield self.check_scatter_3D, sn, pr, s
+
+    def check_scatter_3D(self, sample_nos, p_ref, save):
+        try:
+            plotDomains.scatter_3D(self.samples, sample_nos, self.P_samples,
+                    p_ref, save, False, 'XLABEL', 'YLABEL', 'ZLABEL',
+                    self.filename) 
+            go = True
+        except (RuntimeError, TypeError, NameError):
+            go = False
+        nptest.assert_equal(go, True)      
+
+    def test_show_param():
+        """
+        Test :meth:`bet.postProcess.plotDomains.show_param`
+        """
+        sample_nos = [None, 25]
+        save = [True, False]
+        samples = [self.samples, self.samples[:,[0, 1]]]
+        lnums = [None, self.lnums]
+
+        for sample in samples:
+            for ln in lnums:
+                for sn in sample_nos:
+                    p_ref = [None, samples[4,:]]
+                    for pr in p_ref:
+                        for s in save:
+                            yield self.check_show_param, samples, pr, sn, s, ln
+
+    def check_show_param(self, samples, sample_nos, p_ref, save, lnums):
+        try:
+            plotDomains.show_param(samples, self.data, self.rho_D, p_ref,
+                    sample_nos, save, False, lnums) 
+            go = True
+        except (RuntimeError, TypeError, NameError):
+            go = False
+        nptest.assert_equal(go, True) 
+
+    def test_show_data():
+        """
+        Test :meth:`bet.postProcess.plotDomains.show_data`
+        """
+        sample_nos = [None, 25]
+        save = [True, False]
+        data_sets = [self.data, self.data[:,[0, 1]]]
+        qnums = [None, self.lnums]
+
+        for data in data_sets:
+            for qn in qnums:
+                for sn in sample_nos:
+                    Q_ref = [None, data[4,:]]
+                    for qr in Q_ref:
+                        for s in save:
+                            yield self.check_show_data, data, sn, qr, s, qn
+
+    def check_show_data(self, data, sample_nos, q_ref, save, qnums):
+        try:
+            plotDomains.show_data(data, self.rho_D, q_ref,
+                    sample_nos, save, False, qnums) 
+            go = True
+        except (RuntimeError, TypeError, NameError):
+            go = False
+        nptest.assert_equal(go, True) 
+
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 001f8eaa..2726f517 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -1,5 +1,7 @@
+# Lindley Graham 04/07/2015
+
 """
-This module contains unittests for :mod:`~bet.util`
+This module contains unittests for :mod:`~bet.sampling.basicSampling`
 """
 
 import unittest, os
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index bea8ef1d..528e985f 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -1,5 +1,6 @@
+# Lindley Graham 04/07/2015
 """
-This module contains unittests for :mod:`~bet.util`
+This module contains unittests for :mod:`~bet.sampling.basicSampling:`
 """
 
 import unittest, os
diff --git a/test/test_util.py b/test/test_util.py
index 9958eacc..4e19b3a2 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -1,3 +1,4 @@
+# Lindley Graham 04/07/2015
 """
 This module contains unittests for :mod:`~bet.util`
 """

From 4572d1eac02f94a3a6c93fe413da49116d29e64b Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Wed, 8 Apr 2015 17:44:10 -0600
Subject: [PATCH 24/85] makes file paths absolute

---
 test/test_calculateP/test_calculateP.py | 24 +++++++++++++-----------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/test/test_calculateP/test_calculateP.py b/test/test_calculateP/test_calculateP.py
index 79b3d439..77195231 100644
--- a/test/test_calculateP/test_calculateP.py
+++ b/test/test_calculateP/test_calculateP.py
@@ -6,8 +6,9 @@
 Most of these tests should make sure certain values are within a tolerance
 rather than exact due to the stocastic nature of the algorithms being tested.
 """
-
+import os
 import unittest
+import bet
 import bet.calculateP.calculateP as calcP
 import bet.calculateP.simpleFunP as simpleFunP
 import numpy as np
@@ -16,6 +17,7 @@
 import bet.util as util
 from bet.Comm import *
 
+data_path = os.path.dirname(bet.__file__) + "/../test/test_calculateP/datafiles"
 
 class TestEmulateIIDLebesgue(unittest.TestCase):
     """
@@ -127,8 +129,8 @@ class TestProbMethod_3to2(unittest.TestCase):
     Sets up 3 to 2 map problem.
     """
     def setUp(self):
-        self.samples = np.loadtxt("datafiles/3to2_samples.txt.gz")
-        self.data = np.loadtxt("datafiles/3to2_data.txt.gz")
+        self.samples = np.loadtxt(data_path + "/3to2_samples.txt.gz")
+        self.data = np.loadtxt(data_path + "/3to2_data.txt.gz")
         Q_ref =  np.array([0.422, 0.9385])
         (self.d_distr_prob, self.d_distr_samples, self.d_Tree) = simpleFunP.uniform_hyperrectangle(data=self.data,Q_ref=Q_ref, bin_ratio=0.2, center_pts_per_edge = 1)
         self.lam_domain= np.array([[0.0, 1.0],
@@ -156,7 +158,7 @@ def setUp(self):
                                                      d_distr_samples = self.d_distr_samples,
                                                      lam_domain = self.lam_domain,
                                                      d_Tree = self.d_Tree)
-        self.P_ref = np.loadtxt("datafiles/3to2_prob.txt.gz")
+        self.P_ref = np.loadtxt(data_path + "/3to2_prob.txt.gz")
 
 
 
@@ -176,7 +178,7 @@ def setUp(self):
                                                               lam_domain = self.lam_domain,
                                                               lambda_emulate = self.lambda_emulate,
                                                               d_Tree = self.d_Tree)
-        self.P_emulate_ref=np.loadtxt("datafiles/3to2_prob_emulated.txt.gz")
+        self.P_emulate_ref=np.loadtxt(data_path + "/3to2_prob_emulated.txt.gz")
         self.P_emulate = util.get_global_values(self.P_emulate)
 
 
@@ -197,7 +199,7 @@ def setUp(self):
                                                            lam_domain = self.lam_domain,
                                                            lambda_emulate = self.lambda_emulate,
                                                            d_Tree = self.d_Tree)
-        self.P_ref = np.loadtxt("datafiles/3to2_prob_mc.txt.gz")
+        self.P_ref = np.loadtxt(data_path + "/3to2_prob_mc.txt.gz")
 
  
 
@@ -209,8 +211,8 @@ def setUp(self):
         """
         Set up problem.
         """
-        self.samples = np.loadtxt("datafiles/3to2_samples.txt.gz")
-        self.data = np.loadtxt("datafiles/3to2_data.txt.gz")[:,0]
+        self.samples = np.loadtxt(data_path + "/3to2_samples.txt.gz")
+        self.data = np.loadtxt(data_path + "/3to2_data.txt.gz")[:,0]
         Q_ref =  np.array([0.422])
         (self.d_distr_prob, self.d_distr_samples, self.d_Tree) = simpleFunP.uniform_hyperrectangle(data=self.data,Q_ref=Q_ref, bin_ratio=0.2, center_pts_per_edge = 1)
         self.lam_domain= np.array([[0.0, 1.0],
@@ -235,7 +237,7 @@ def setUp(self):
                                                      d_distr_samples = self.d_distr_samples,
                                                      lam_domain = self.lam_domain,
                                                      d_Tree = self.d_Tree)
-        self.P_ref = np.loadtxt("datafiles/3to1_prob.txt.gz")
+        self.P_ref = np.loadtxt(data_path + "/3to1_prob.txt.gz")
 
 
 class Test_prob_emulated_3to1(TestProbMethod_3to1, prob_emulated):
@@ -254,7 +256,7 @@ def setUp(self):
                                                               lam_domain = self.lam_domain,
                                                               lambda_emulate = self.lambda_emulate,
                                                               d_Tree = self.d_Tree)
-        self.P_emulate_ref=np.loadtxt("datafiles/3to1_prob_emulated.txt.gz")
+        self.P_emulate_ref=np.loadtxt(data_path + "/3to1_prob_emulated.txt.gz")
         self.P_emulate = util.get_global_values(self.P_emulate)
 
 
@@ -275,7 +277,7 @@ def setUp(self):
                                                            lam_domain = self.lam_domain,
                                                            lambda_emulate = self.lambda_emulate,
                                                            d_Tree = self.d_Tree)
-        self.P_ref = np.loadtxt("datafiles/3to1_prob_mc.txt.gz")
+        self.P_ref = np.loadtxt(data_path + "/3to1_prob_mc.txt.gz")
 
   
 class TestProbMethod_10to4(unittest.TestCase):

From 16891686b239eff4786e1b2aa7484ba8908ad7be Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Wed, 8 Apr 2015 18:10:11 -0600
Subject: [PATCH 25/85] fixed comments regarding pull request, added
 plotDomains, made minor changes to plotDomains

---
 bet/postProcess/plotDomains.py              |  6 +-
 test/test_postProcess/test_plotDomains.py   | 88 +++++++++++++++++++--
 test/test_sampling/test_adaptiveSampling.py |  8 +-
 test/test_util.py                           |  3 +-
 4 files changed, 85 insertions(+), 20 deletions(-)

diff --git a/bet/postProcess/plotDomains.py b/bet/postProcess/plotDomains.py
index efad0e65..ab91a3a6 100644
--- a/bet/postProcess/plotDomains.py
+++ b/bet/postProcess/plotDomains.py
@@ -194,7 +194,7 @@ def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
     various :math:`Q_{ref}`. 
 
     :param samples: Samples to plot
-    :type samples: :class:`~numpy.ndarray` of shape (ndim, num_samples)
+    :type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim)
     :param data: Data associated with ``samples``
     :type data: :class:`np.ndarray`
     :param Q_ref: reference data value
@@ -212,7 +212,7 @@ def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
     if  Q_nums == None:
         Q_nums = range(data.shape[1])
 
-    triangulation = tri.Triangulation(samples[0, :], samples[1, :])
+    triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
     triangles = triangulation.triangles
 
     for i in Q_nums:
@@ -239,7 +239,7 @@ def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
     dimension of data is :math:`q_1`.
 
     :param samples: Samples to plot
-    :type samples: :class:`~numpy.ndarray` of shape (ndim, num_samples)
+    :type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim)
     :param data: Data associated with ``samples``
     :type data: :class:`np.ndarray`
     :param Q_ref: reference data value
diff --git a/test/test_postProcess/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
index fa2792a3..a78d3948 100644
--- a/test/test_postProcess/test_plotDomains.py
+++ b/test/test_postProcess/test_plotDomains.py
@@ -7,12 +7,16 @@
 """
 
 import unittest, os, glob
-import bet.postProcess.plotD as plotDomains
+import bet.postProcess.plotDomains as plotDomains
+import bet.util as util
+import matplotlib.tri as tri
+from matplotlib.lines import Line2D
+
 import numpy as np
 import numpy.testing as nptest
 from bet.Comm import *
 
-class Test_calc_marg_2D(unittest.TestCase):
+class test_plotDomains(unittest.TestCase):
     """
     Test :meth:`bet.postProcess.plotP.calculate_1D_marginal_probs` and  :meth:`bet.postProcess.plotP.calculate_2D_marginal_probs` for a 2D
     parameter space.
@@ -28,7 +32,7 @@ def setUp(self):
                     self.lam_domain[1][1], 10)))
         self.data = self.samples*3.0
         self.P_samples = (1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
-        self.filename("testfigure")
+        self.filename = "testfigure"
         
         QoI_range = np.array([3.0, 3.0, 3.0])
         Q_ref = QoI_range*0.5
@@ -44,6 +48,14 @@ def ifun(outputs):
 	    return inside.astype('float64')*max_values
 	self.rho_D = ifun
         self.lnums = [1,2,3]
+        self.markers = []
+        for m in Line2D.markers:
+            try:
+                if len(m) == 1 and m != ' ':
+                    self.markers.append(m)
+            except TypeError:
+                pass
+        self.colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k')
 
     def tearDown(self):
         """
@@ -52,10 +64,15 @@ def tearDown(self):
         # remove any files the we create
         filenames = glob.glob(self.filename+".*")
         filenames.append('param_samples_cs.eps')
+        filenames.append('domain_q1_q1_cs.eps')
+        filenames.append('q1_q2_domain_Q_cs.eps')
+        figfiles = glob.glob('figs/*')
+        filenames.extend(figfiles)
         for f in filenames:
-            os.remove(f)
+            if os.path.exists(f):
+                os.remove(f)
 
-    def test_scatter_2D():
+    def test_scatter_2D(self):
         """
         Test :meth:`bet.postProcess.plotDomains.scatter_2D`
         """
@@ -77,7 +94,7 @@ def check_scatter_2D(self, sample_nos, p_ref, save):
             go = False
         nptest.assert_equal(go, True)
  
-    def test_scatter_3D():
+    def test_scatter_3D(self):
         """
         Test :meth:`bet.postProcess.plotDomains.scatter_3D`
         """
@@ -99,7 +116,7 @@ def check_scatter_3D(self, sample_nos, p_ref, save):
             go = False
         nptest.assert_equal(go, True)      
 
-    def test_show_param():
+    def test_show_param(self):
         """
         Test :meth:`bet.postProcess.plotDomains.show_param`
         """
@@ -125,7 +142,7 @@ def check_show_param(self, samples, sample_nos, p_ref, save, lnums):
             go = False
         nptest.assert_equal(go, True) 
 
-    def test_show_data():
+    def test_show_data(self):
         """
         Test :meth:`bet.postProcess.plotDomains.show_data`
         """
@@ -151,3 +168,58 @@ def check_show_data(self, data, sample_nos, q_ref, save, qnums):
             go = False
         nptest.assert_equal(go, True) 
 
+    def test_show_data_domain_2D(self):
+        """
+        Test :meth:`bet.postProces.plotDomains.show_data_domain_2D`
+        """
+        ref_markers = [None, self.markers]
+        ref_colors = [None, self.colors]
+        triangulation = tri.Triangulation(self.samples[:, 0], self.samples[:, 1])
+        triangles = [None, triangulation.triangles]
+        filenames = [None, ['domain_q1_q1_cs.eps', 'q1_q2_domain_Q_cs.eps']]
+        save = [None, False]
+
+        for rm in ref_markers:
+            for rc in ref_colors:
+                    for t in triangles:
+                        for s in save:
+                            for fn in filenames:
+                                yield self.check_show_data_domain_2D, rm, rc, t, s,
+                                fn
+
+    def check_show_data_domain_2D(self, ref_markers, ref_colors, triangles,
+            save, filenames):
+        Q_ref = self.data[4, [0, 1]]
+        data = self.data[:, [0, 1]]
+        try:
+            plotDomains.show_data_domain_2D(self.samples, data, Q_ref,
+                    ref_markers, ref_colors, triangles=triangles, save=save,
+                    filenames=filenames)
+            go = True
+        except (RuntimeError, TypeError, NameError):
+            go = False
+        nptest.assert_equal(go, True) 
+        
+    def test_show_data_domain_multi(self):
+        """
+        Test :meth:`bet.postProcess.plotDomains.show_data_domain_multi`
+        """
+        os.mkdir('figs/')
+        Q_nums = [None, [1, 2]]
+        ref_markers = [None, self.markers]
+        ref_colors = [None, self.colors]
+        for rm in ref_markers:
+            for rc in ref_colors:
+                for qn in Q_nums:
+                    yield self.check_show_data_domain_multi, rm, rc, qn
+
+    def check_show_data_domain_multi(self, ref_markers, ref_colors, Q_nums):
+        Q_ref = self.data[4,:]
+        try:
+            plotDomains.show_data_domain_multi(self.samples, self.data,
+                    Q_ref, Q_nums, ref_markers=ref_markers,
+                    ref_colors=ref_colors)
+            go = True
+        except (RuntimeError, TypeError, NameError):
+            go = False
+
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 2726f517..cfcac4c0 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -1,7 +1,7 @@
 # Lindley Graham 04/07/2015
 
 """
-This module contains unittests for :mod:`~bet.sampling.basicSampling`
+This module contains unittests for :mod:`~bet.sampling.adaptiveSampling`
 """
 
 import unittest, os
@@ -11,12 +11,6 @@
 import scipy.io as sio
 from bet.Comm import *
 
-"""
-TODO: rewrite loadmat test, rewrite init test, rewrite update_mdict test, write
-tests for run_BLAHBLAH, write test for generalized_chains, write a test stub
-for kernels but do not implement
-"""
-
 def test_loadmat_init():
     """
     Tests :meth:`bet.sampling.adaptiveSampling.loadmat` and
diff --git a/test/test_util.py b/test/test_util.py
index 4e19b3a2..67d2b656 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -74,8 +74,7 @@ def compare_get_global_values(i):
     if i == 0:
         my_array = original_array[my_index]
     else:
-        my_array = original_array[my_index, :w
-                :]
+        my_array = original_array[my_index, :]
     recomposed_array = util.get_global_values(my_array)
     nptest.assert_array_equal(original_array, recomposed_array)
 

From b8123a47f28e7543b8b60789f4aeb5d446b8bebc Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Wed, 8 Apr 2015 18:27:04 -0600
Subject: [PATCH 26/85] making path names relative

---
 test/test_sampling/test_adaptiveSampling.py | 12 +++++++-----
 test/test_sampling/test_basicSampling.py    | 11 +++++++----
 2 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index cfcac4c0..462eb498 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -11,6 +11,8 @@
 import scipy.io as sio
 from bet.Comm import *
 
+local_path = os.path.join(os.path.dirname(bet.__file__), "/../test/test_sampling")
+
 def test_loadmat_init():
     """
     Tests :meth:`bet.sampling.adaptiveSampling.loadmat` and
@@ -54,8 +56,8 @@ def test_loadmat_init():
     assert loaded_sampler2.num_chains == num_chains2
     nptest.assert_array_equal(np.repeat(range(num_chains2), chain_length, 0),
             loaded_sampler2.sample_batch_no)
-    os.remove('testfile1.mat')
-    os.remove('testfile2.mat')
+    os.remove(os.path.join(local_path, 'testfile1.mat'))
+    os.remove(os.path.join(local_path, 'testfile2.mat'))
 
 def verify_samples(model, QoI_range, sampler, param_min, param_max,
         t_set, savefile, initial_sample_type):
@@ -102,7 +104,7 @@ def ifun(outputs):
     # did the savefiles get created? (proper number, contain proper keys)
     mdat = {}
     if size > 1:
-        mdat = sio.loadmat(os.path.join(os.path.dirname(savefile),
+        mdat = sio.loadmat(os.path.join(local_path, os.path.dirname(savefile),
             "proc{}{}".format(rank, os.path.basename(savefile))))
     else:
         mdat = sio.loadmat(savefile)
@@ -168,8 +170,8 @@ def tearDown(self):
         if size > 1:
             for f in self.savefiles:
                 for proc in range(size):
-                    proc_savefile = os.path.join(os.path.dirname(f),
-                            "proc{}{}".format(rank,
+                    proc_savefile = os.path.join(local_path,
+                            os.path.dirname(f), "proc{}{}".format(rank,
                                 os.path.basename(f)))
 
     def test_update(self):
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index 528e985f..a9e61427 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -8,6 +8,9 @@
 import numpy as np
 import bet.sampling.basicSampling as bsam
 import scipy.io as sio
+import bet
+
+local_path = os.path.join(os.path.dirname(bet.__file__), "../test/test_sampling")
 
 def test_in_high_prob():
     """
@@ -72,8 +75,8 @@ def test_loadmat():
     nptest.assert_array_equal(data2, None)
     assert loaded_sampler2.num_samples == 6
     assert loaded_sampler2.lb_model == model
-    os.remove('testfile1.mat')
-    os.remove('testfile2.mat')
+    os.remove(os.join(local_path, 'testfile1.mat'))
+    os.remove(os.join(loca_path, 'testfile2.mat'))
 
 def verify_user_samples(model, sampler, samples, savefile, parallel):
     # evalulate the model at the samples directly
@@ -185,8 +188,8 @@ def map_10t4(x):
 
     def tearDown(self):
         for f in self.savefiles:
-            if os.path.exists(f+".mat"):
-                os.remove(f+".mat")
+            if os.path.exists(os.path.join(local_path, f, ".mat")):
+                os.remove(os.path.join(local_path, f, ".mat"))
 
     def test_init(self):
         """

From 89cb1485702271c06f7bfb0c6089952f57f7dcb3 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Wed, 8 Apr 2015 18:34:18 -0600
Subject: [PATCH 27/85] make paths relative in test_sampling

---
 test/test_sampling/test_adaptiveSampling.py | 9 ++++++---
 test/test_sampling/test_basicSampling.py    | 6 ++++--
 2 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 462eb498..f01f5fb9 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -10,8 +10,9 @@
 import bet.sampling.adaptiveSampling as asam
 import scipy.io as sio
 from bet.Comm import *
+import bet
 
-local_path = os.path.join(os.path.dirname(bet.__file__), "/../test/test_sampling")
+local_path = os.path.join(os.path.dirname(bet.__file__), "../test/test_sampling")
 
 def test_loadmat_init():
     """
@@ -56,8 +57,10 @@ def test_loadmat_init():
     assert loaded_sampler2.num_chains == num_chains2
     nptest.assert_array_equal(np.repeat(range(num_chains2), chain_length, 0),
             loaded_sampler2.sample_batch_no)
-    os.remove(os.path.join(local_path, 'testfile1.mat'))
-    os.remove(os.path.join(local_path, 'testfile2.mat'))
+    if os.path.exists(os.path.join(local_path, 'testfile1.mat')):
+        os.remove(os.path.join(local_path, 'testfile1.mat'))
+    if os.path.exists(os.path.join(local_path, 'testfile2.mat')):
+        os.remove(os.path.join(loca_path, 'testfile2.mat'))
 
 def verify_samples(model, QoI_range, sampler, param_min, param_max,
         t_set, savefile, initial_sample_type):
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index a9e61427..109c9874 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -75,8 +75,10 @@ def test_loadmat():
     nptest.assert_array_equal(data2, None)
     assert loaded_sampler2.num_samples == 6
     assert loaded_sampler2.lb_model == model
-    os.remove(os.join(local_path, 'testfile1.mat'))
-    os.remove(os.join(loca_path, 'testfile2.mat'))
+    if os.path.exists(os.path.join(local_path, 'testfile1.mat')):
+        os.remove(os.path.join(local_path, 'testfile1.mat'))
+    if os.path.exists(os.path.join(local_path, 'testfile2.mat')):
+        os.remove(os.path.join(loca_path, 'testfile2.mat'))
 
 def verify_user_samples(model, sampler, samples, savefile, parallel):
     # evalulate the model at the samples directly

From 6d05a8e4a81acb1925e152878d615856cd81b464 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Wed, 8 Apr 2015 18:36:03 -0600
Subject: [PATCH 28/85] made path names relative in test_plotDomains

---
 test/test_postProcess/test_plotDomains.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/test/test_postProcess/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
index a78d3948..f0ca4f72 100644
--- a/test/test_postProcess/test_plotDomains.py
+++ b/test/test_postProcess/test_plotDomains.py
@@ -6,16 +6,17 @@
 Tests for the execution of plotting parameter and data domains.
 """
 
-import unittest, os, glob
+import unittest, os, glob, bet
 import bet.postProcess.plotDomains as plotDomains
 import bet.util as util
 import matplotlib.tri as tri
 from matplotlib.lines import Line2D
-
 import numpy as np
 import numpy.testing as nptest
 from bet.Comm import *
 
+local_path = os.path.join(os.path.dirname(bet.__file__), "../test/test_sampling")
+
 class test_plotDomains(unittest.TestCase):
     """
     Test :meth:`bet.postProcess.plotP.calculate_1D_marginal_probs` and  :meth:`bet.postProcess.plotP.calculate_2D_marginal_probs` for a 2D
@@ -69,8 +70,8 @@ def tearDown(self):
         figfiles = glob.glob('figs/*')
         filenames.extend(figfiles)
         for f in filenames:
-            if os.path.exists(f):
-                os.remove(f)
+            if os.path.exists(os.path.join(local_path, f)):
+                os.remove(os.path.join(local_path, f))
 
     def test_scatter_2D(self):
         """

From ce6640ccfd953df0248f3bf5abb4d0b1130fd8fa Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Thu, 9 Apr 2015 10:39:09 -0600
Subject: [PATCH 29/85] deletes created .eps files during tests

---
 test/test_postProcess/test_plotP.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/test/test_postProcess/test_plotP.py b/test/test_postProcess/test_plotP.py
index d60cf871..ff0c85ce 100644
--- a/test/test_postProcess/test_plotP.py
+++ b/test/test_postProcess/test_plotP.py
@@ -15,6 +15,8 @@
 import numpy.testing as nptest
 import bet.util as util
 from bet.Comm import *
+import os
+
 
 class Test_calc_marg_1D(unittest.TestCase):
     """
@@ -159,8 +161,10 @@ def test_plot_marginals_1D(self):
                                                               self.lam_domain,
                                                               nbins = 10)
         try:
-            plotP.plot_1D_marginal_probs(marginals, bins,self.lam_domain, interactive=False)
+            plotP.plot_1D_marginal_probs(marginals, bins,self.lam_domain, filename = "file", interactive=False)
             go = True
+            os.remove("file_1D_0.eps")
+            os.remove("file_1D_1.eps")
         except (RuntimeError, TypeError, NameError):
             go = False
         nptest.assert_equal(go, True)
@@ -176,8 +180,9 @@ def test_plot_marginals_2D(self):
         marginals[(0,1)][0][0]=0.0
         marginals[(0,1)][0][1]*=2.0
         try:
-            plotP.plot_2D_marginal_probs(marginals, bins,self.lam_domain, interactive=False)
+            plotP.plot_2D_marginal_probs(marginals, bins,self.lam_domain, filename = "file", interactive=False)
             go = True
+            os.remove("file_2D_0_1.eps")
         except (RuntimeError, TypeError, NameError):
             go = False
         nptest.assert_equal(go, True)

From 6ae19a4267e87534f261efa17c5ffc1076367694 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Thu, 9 Apr 2015 10:51:12 -0600
Subject: [PATCH 30/85] fix spelling error, documentation error, started on
 simpleFunP tests

---
 bet/calculateP/calculateP.py            |  8 ++++----
 bet/calculateP/simpleFunP.py            | 14 +++++++-------
 test/test_calculateP/test_calculateP.py |  2 +-
 test/test_calculateP/test_simpleFunP.py | 20 ++++++++++++++++++++
 4 files changed, 32 insertions(+), 12 deletions(-)

diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index 0a24477a..b32e603a 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -51,7 +51,7 @@ def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
     :param data: The data from running the model given the samples.
     :type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
     :param rho_D_M: The simple function approximation of rho_D
-    :type rho_D_M: :class:`~numpy.ndarray` of shape  (M,mdim) 
+    :type rho_D_M: :class:`~numpy.ndarray` of shape  (M,) 
     :param d_distr_samples: The samples in the data space that define a
         parition of D to for the simple function approximation
     :type d_distr_samples: :class:`~numpy.ndarray` of shape  (M, mdim) 
@@ -112,7 +112,7 @@ def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None):
     :param data: The data from running the model given the samples.
     :type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
     :param rho_D_M: The simple function approximation of rho_D
-    :type rho_D_M: :class:`~numpy.ndarray` of shape  (M,mdim) 
+    :type rho_D_M: :class:`~numpy.ndarray` of shape  (M,) 
     :param d_distr_samples: The samples in the data space that define a
         parition of D to for the simple function approximation
     :type d_distr_samples: :class:`~numpy.ndarray` of shape  (M, mdim) 
@@ -169,7 +169,7 @@ def prob_qhull(samples, data, rho_D_M, d_distr_samples,
     :param data: The data from running the model given the samples.
     :type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
     :param rho_D_M: The simple function approximation of rho_D
-    :type rho_D_M: :class:`~numpy.ndarray` of shape  (M,mdim) 
+    :type rho_D_M: :class:`~numpy.ndarray` of shape  (M,) 
     :param d_distr_samples: The samples in the data space that define a
         parition of D to for the simple function approximation
     :type d_distr_samples: :class:`~numpy.ndarray` of shape  (M,mdim) 
@@ -251,7 +251,7 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
     :param data: The data from running the model given the samples.
     :type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
     :param rho_D_M: The simple function approximation of rho_D
-    :type rho_D_M: :class:`~numpy.ndarray` of shape  (M, mdim) 
+    :type rho_D_M: :class:`~numpy.ndarray` of shape  (M,) 
     :param d_distr_samples: The samples in the data space that define a
         parition of D to for the simple function approximation
     :type d_distr_samples: :class:`~numpy.ndarray` of shape  (M, mdim) 
diff --git a/bet/calculateP/simpleFunP.py b/bet/calculateP/simpleFunP.py
index ae7c80b3..fcf8641b 100644
--- a/bet/calculateP/simpleFunP.py
+++ b/bet/calculateP/simpleFunP.py
@@ -43,7 +43,7 @@ def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     :param Q_ref: :math:`Q(`\lambda_{reference})`
     :type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
     :rtype: tuple
-    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` and
+    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
     ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree` is
     the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
@@ -182,7 +182,7 @@ def normal_normal(Q_ref, M, std, num_d_emulate=1E6):
     :param std: The standard deviation of each QoI
     :type std: :class:`~numpy.ndarray` of size (mdim,)
     :rtype: tuple
-    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` and
+    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M``  is (M,) and
     ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree` is
     the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
@@ -259,7 +259,7 @@ def unif_normal(Q_ref, M, std, num_d_emulate=1E6):
     :param std: The standard deviation of each QoI
     :type std: :class:`~numpy.ndarray` of size (mdim,)
     :rtype: tuple
-    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` and
+    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
     ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree` is
     the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
@@ -336,7 +336,7 @@ def uniform_hyperrectangle_user(data, domain, center_pts_per_edge=1):
         additional two points will be added to create the bounding layer
 
     :rtype: tuple
-    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` and
+    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
         ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
@@ -385,7 +385,7 @@ def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1)
         additional two points will be added to create the bounding layer
 
     :rtype: tuple
-    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` and
+    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
         ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
@@ -429,7 +429,7 @@ def uniform_hyperrectangle(data, Q_ref, bin_ratio, center_pts_per_edge=1):
         additional two points will be added to create the bounding layer
 
     :rtype: tuple
-    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` and
+    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
         ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
@@ -477,7 +477,7 @@ def uniform_data(data):
         additional two points will be added to create the bounding layer
 
     :rtype: tuple
-    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` and
+    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
         ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
diff --git a/test/test_calculateP/test_calculateP.py b/test/test_calculateP/test_calculateP.py
index 77195231..165de28c 100644
--- a/test/test_calculateP/test_calculateP.py
+++ b/test/test_calculateP/test_calculateP.py
@@ -1,6 +1,6 @@
 # Steven Mattis and Lindley Graham 04/06/2015
 """
-This module contains tests for :module:`bet.caculcateP.calculateP`.
+This module contains tests for :module:`bet.calculateP.calculateP`.
 
 
 Most of these tests should make sure certain values are within a tolerance
diff --git a/test/test_calculateP/test_simpleFunP.py b/test/test_calculateP/test_simpleFunP.py
index e69de29b..5f992941 100644
--- a/test/test_calculateP/test_simpleFunP.py
+++ b/test/test_calculateP/test_simpleFunP.py
@@ -0,0 +1,20 @@
+# Lindley Graham 04/09/2015
+
+"""
+This module contains tests for :module:`bet.calculateP.simpleFunP`
+
+Some of these tests make sure certain values are within a tolerance rather than
+exact due to the stochastic nature of the algorithms being tested
+"""
+
+import os, bet, unittest
+import bet.calculateP.simpleFunP as simpleFunP
+import numpy as np
+
+local_path = os.path.join(os.path.dirname(bet.__file__),
+'../test/test_calulateP')
+
+class prob_exact:
+    def test_prob_sum_to_1(self):
+        """
+        Test to see if the 

From d14e4f8d36ccf017ba6e20eb7cfb9f7a2bdc95d6 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Thu, 9 Apr 2015 11:13:22 -0600
Subject: [PATCH 31/85] working on simplefun tests

---
 test/test_calculateP/test_simpleFunP.py | 41 ++++++++++++++++++++++---
 1 file changed, 37 insertions(+), 4 deletions(-)

diff --git a/test/test_calculateP/test_simpleFunP.py b/test/test_calculateP/test_simpleFunP.py
index 5f992941..82c94925 100644
--- a/test/test_calculateP/test_simpleFunP.py
+++ b/test/test_calculateP/test_simpleFunP.py
@@ -4,17 +4,50 @@
 This module contains tests for :module:`bet.calculateP.simpleFunP`
 
 Some of these tests make sure certain values are within a tolerance rather than
-exact due to the stochastic nature of the algorithms being tested
+exact due to the stochastic nature of the algorithms being tested. 
+
+The ouput of all the methods being tested is of the form (rho_D_M,
+d_distr_samples, d_Tree) where ``rho_D_M`` and ``d_distr_samples`` are (mdim,
+M) :class:`~numpy.ndarray` and `d_Tree` is the :class:`~scipy.spatial.KDTree`
+for d_distr_samples.
+
 """
 
 import os, bet, unittest
 import bet.calculateP.simpleFunP as simpleFunP
 import numpy as np
+import numpy.test as nptest
 
 local_path = os.path.join(os.path.dirname(bet.__file__),
 '../test/test_calulateP')
 
-class prob_exact:
-    def test_prob_sum_to_1(self):
+class prob:
+    def test_rho_D_M_sum_to_1(self):
+        """
+        Test that probabilities sum to 1.
+        """
+        nptest.assert_almost_equal(np.sum(self.rho_D_M), 1.0)
+    def test_rho_D_M_pos(self):
+        """
+        Test that probabilities are non-negative.
+        """
+        assert True == np.all(self.rho_D_M >= 0.0)
+    def test_dimensions(self):
+        """
+        Test that the dimensions of the outputs are correct.
+        """
+        assert self.rho_D_M.shape[0] == self.d_distr_samples.shape[1]
+        assert self.data.shape[1] == self.d_distr_samples.shape[0]
+        assert (self.d_tree.n, self.d_tree.m) == d_distr_samples.shape
+
+class prob_uniform:
+    def test_domain(self):
         """
-        Test to see if the 
+        Test that the probabilities within the prescribed domain are non-zero
+        and that the probabilities outside of the prescribed domain are zero.
+        """
+        # d_distr_samples are (mdim, M)
+
+
+
+

From 8e594d005529257922526c2ab566bac39265b64f Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Thu, 9 Apr 2015 11:20:58 -0600
Subject: [PATCH 32/85] fixes test_simpleFunP

---
 test/test_calculateP/test_simpleFunP.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/test_calculateP/test_simpleFunP.py b/test/test_calculateP/test_simpleFunP.py
index 82c94925..f46f3217 100644
--- a/test/test_calculateP/test_simpleFunP.py
+++ b/test/test_calculateP/test_simpleFunP.py
@@ -16,7 +16,7 @@
 import os, bet, unittest
 import bet.calculateP.simpleFunP as simpleFunP
 import numpy as np
-import numpy.test as nptest
+import numpy.testing as nptest
 
 local_path = os.path.join(os.path.dirname(bet.__file__),
 '../test/test_calulateP')

From 5f9c49d96ab6f0c2a2c4ee38eacfc3d453fb492a Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Fri, 10 Apr 2015 17:48:38 -0600
Subject: [PATCH 33/85] working on simplefun minor changes and tests to
 simplefun

---
 bet/calculateP/simpleFunP.py            |  40 +++-
 test/test_calculateP/test_simpleFunP.py | 299 +++++++++++++++++++++++-
 2 files changed, 319 insertions(+), 20 deletions(-)

diff --git a/bet/calculateP/simpleFunP.py b/bet/calculateP/simpleFunP.py
index fcf8641b..147a271d 100644
--- a/bet/calculateP/simpleFunP.py
+++ b/bet/calculateP/simpleFunP.py
@@ -6,9 +6,12 @@
 import numpy as np
 import scipy.spatial as spatial
 import bet.calculateP.voronoiHistogram as vHist
+import collections
 
 def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     r"""
+    TODO: THIS OCCASIONALLY FAILS test.test_calculateP.test_domain FIX ME
+
     Creates a simple function approximation of :math:`\rho_{\mathcal{D}}`
     where :math:`\rho_{\mathcal{D}}` is a uniform probability density on
     a generalized rectangle centered at Q_ref.
@@ -44,7 +47,7 @@ def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     :type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
     :rtype: tuple
     :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
-    ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree` is
+    ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree` is
     the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
     if len(data.shape) == 1:
@@ -175,7 +178,8 @@ def normal_normal(Q_ref, M, std, num_d_emulate=1E6):
         :math:`\rho_{\mathcal{D},M}` The choice of M is something of an "art" -
         play around with it and you can get reasonable results with a
         relatively small number here like 50. 
-    :param int num_d_emulate: Number of samples used to emulate using an MC
+    :param:w
+    int num_d_emulate: Number of samples used to emulate using an MC
         assumption 
     :param Q_ref: :math:`Q(\lambda_{reference})`
     :type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
@@ -183,7 +187,7 @@ def normal_normal(Q_ref, M, std, num_d_emulate=1E6):
     :type std: :class:`~numpy.ndarray` of size (mdim,)
     :rtype: tuple
     :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M``  is (M,) and
-    ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree` is
+    ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree` is
     the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
     """
@@ -191,10 +195,18 @@ def normal_normal(Q_ref, M, std, num_d_emulate=1E6):
     r'''Create M smaples defining M bins in D used to define
     :math:`\rho_{\mathcal{D},M}` rho_D is assumed to be a multi-variate normal
     distribution with mean Q_ref and standard deviation std.'''
+    if not isinstance(Q_ref, collections.Iterable):
+        Q_ref = np.array([Q_ref])
+    if not isinstance(std, collections.Iterable):
+        std = np.array([std])
 
-    covariance = np.diag(std*std)
+    covariance = std**2
 
     d_distr_samples = np.zeros((M, len(Q_ref)))
+    print "d_distr_samples.shape", d_distr_samples.shape
+    print "Q_ref.shape", Q_ref.shape
+    print "std.shape", std.shape
+
     if rank == 0:
         for i in range(len(Q_ref)):
             d_distr_samples[:, i] = np.random.normal(Q_ref[i], std[i], M) 
@@ -260,7 +272,7 @@ def unif_normal(Q_ref, M, std, num_d_emulate=1E6):
     :type std: :class:`~numpy.ndarray` of size (mdim,)
     :rtype: tuple
     :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
-    ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree` is
+    ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree` is
     the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
     """
@@ -337,7 +349,7 @@ def uniform_hyperrectangle_user(data, domain, center_pts_per_edge=1):
 
     :rtype: tuple
     :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
-        ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree`
+        ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
     # TODO following code could probably be reduced to a few lines
@@ -386,7 +398,7 @@ def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1)
 
     :rtype: tuple
     :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
-        ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree`
+        ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
     """
@@ -395,6 +407,14 @@ def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1)
     data_max = np.max(data, 0)
     data_min = np.min(data, 0)
 
+    if not isinstance(center_pts_per_edge, collections.Iterable):
+        center_pts_per_edge = np.ones((data.shape[1])) * center_pts_per_edge
+    else:
+        if not len(center_pts_per_edge) == data.shape[1]:
+            center_pts_per_edge = np.ones((data.shape[1]))
+            print 'Warning: center_pts_per_edge dimension mismatch.'
+            print 'Using 1 in each dimension.'
+
     sur_domain = np.zeros((data.shape[1], 2))
     sur_domain[:, 0] = data_min
     sur_domain[:, 1] = data_max
@@ -430,7 +450,7 @@ def uniform_hyperrectangle(data, Q_ref, bin_ratio, center_pts_per_edge=1):
 
     :rtype: tuple
     :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
-        ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree`
+        ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
     """
@@ -443,7 +463,7 @@ def uniform_hyperrectangle(data, Q_ref, bin_ratio, center_pts_per_edge=1):
     # or as numpy array to see if dimensions match data space dimensions and
     # that positive integer values are being used. Also, create this change
     # elsewhere since center_pts_per_edge is only a scalar if dim(D)=1.
-    if not isinstance(center_pts_per_edge, np.ndarray):
+    if not isinstance(center_pts_per_edge, collections.Iterable):
         center_pts_per_edge = np.ones((data.shape[1])) * center_pts_per_edge
     else:
         if not len(center_pts_per_edge) == data.shape[1]:
@@ -478,7 +498,7 @@ def uniform_data(data):
 
     :rtype: tuple
     :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
-        ``d_distr_samples`` are (mdim, M) :class:`~numpy.ndarray` and `d_Tree`
+        ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
     d_distr_prob = np.ones((data.shape[1],))
diff --git a/test/test_calculateP/test_simpleFunP.py b/test/test_calculateP/test_simpleFunP.py
index 82c94925..b770798a 100644
--- a/test/test_calculateP/test_simpleFunP.py
+++ b/test/test_calculateP/test_simpleFunP.py
@@ -7,21 +7,25 @@
 exact due to the stochastic nature of the algorithms being tested. 
 
 The ouput of all the methods being tested is of the form (rho_D_M,
-d_distr_samples, d_Tree) where ``rho_D_M`` and ``d_distr_samples`` are (mdim,
-M) :class:`~numpy.ndarray` and `d_Tree` is the :class:`~scipy.spatial.KDTree`
-for d_distr_samples.
+d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and ``d_distr_samples`` are
+(M, mdim) :class:`~numpy.ndarray` and `d_Tree` is the
+:class:`~scipy.spatial.KDTree` for d_distr_samples.
 
 """
 
 import os, bet, unittest
-import bet.calculateP.simpleFunP as simpleFunP
+import bet.calculateP.simpleFunP as sFun
 import numpy as np
-import numpy.test as nptest
+import numpy.testing as nptest
 
 local_path = os.path.join(os.path.dirname(bet.__file__),
 '../test/test_calulateP')
 
-class prob:
+class prob(object):
+    """
+    Test that the probabilties sum to 1, are non-negative, and have the correct
+    dimensions.
+    """
     def test_rho_D_M_sum_to_1(self):
         """
         Test that probabilities sum to 1.
@@ -36,18 +40,293 @@ def test_dimensions(self):
         """
         Test that the dimensions of the outputs are correct.
         """
-        assert self.rho_D_M.shape[0] == self.d_distr_samples.shape[1]
-        assert self.data.shape[1] == self.d_distr_samples.shape[0]
-        assert (self.d_tree.n, self.d_tree.m) == d_distr_samples.shape
+        assert self.rho_D_M.shape[0] == self.d_distr_samples.shape[0]
+        assert self.mdim == self.d_distr_samples.shape[1]
+        assert (self.d_Tree.n, self.d_Tree.m) == self.d_distr_samples.shape
+
+
+class prob_uniform(prob):
+    """
+    Test that the probabilities within the prescribed domain are non-zero and
+    that the probabilities outside of the prescribed domain are zero.
+    """
 
-class prob_uniform:
     def test_domain(self):
         """
         Test that the probabilities within the prescribed domain are non-zero
         and that the probabilities outside of the prescribed domain are zero.
         """
         # d_distr_samples are (mdim, M)
+        inside = np.logical_and(np.all(np.greater_equal(self.d_distr_samples,
+            self.rect_domain[:, 0]),axis=1),
+            np.all(np.less_equal(self.d_distr_samples,
+            self.rect_domain[:, 1]),axis=1)) 
+        assert np.all(self.rho_D_M[inside] >= 0.0)
+        #print self.rect_domain
+        #print "ind, inside", inside
+        #print "ind, outside", np.logical_not(inside)
+        #print "inside", self.d_distr_samples[inside]
+        #print "outside", self.d_distr_samples[np.logical_not(inside)]
+        #print "inside", self.rho_D_M[inside]
+        #print "outside", self.rho_D_M[np.logical_not(inside)]
+        assert np.all(self.rho_D_M[np.logical_not(inside)] == 0.0)
+
+
+class data_01D(object):
+    """
+    Sets up 01D data domain problem.
+    """
+    def createData(self):
+        """
+        Set up data.
+        """
+        self.data = np.random.random((100,))*10.0
+        self.Q_ref = 5.0
+        self.data_domain = np.array([0.0, 10.0])
+        self.mdim = 1
+
+
+class data_1D(object):
+    """
+    Sets up 1D data domain problem.
+    """
+    def createData(self):
+        """
+        Set up data.
+        """
+        self.data = np.random.random((100, 1))*10.0
+        self.Q_ref = np.array([5.0])
+        self.data_domain = np.expand_dims(np.array([0.0, 10.0]), axis=0)
+        self.mdim = 1
 
 
+class data_2D(object):
+    """
+    Sets up 2D data domain problem.
+    """
+    def createData(self):
+        """
+        Set up data.
+        """
+        self.data = np.random.random((100, 2))*10.0
+        self.Q_ref = np.array([5.0, 5.0])
+        self.data_domain = np.array([[0.0, 10.0], [0.0, 10.0]])
+        self.mdim = 2
+
+
+class data_3D(object):
+    """
+    Sets up 3D data domain problem.
+    """
+    def createData(self):
+        """
+        Set up data.
+        """
+        self.data = np.random.random((100, 3))*10.0
+        self.Q_ref = np.array([5.0, 5.0, 5.0])
+        self.data_domain = np.array([[0.0, 10.0], [0.0, 10.0] , [0.0, 10.0]])
+        self.mdim = 3
+
+class unif_unif(prob_uniform):
+    """
+    Set up :meth:`bet.calculateP.simpleFunP.unif_unif` on data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.unif_unif(self.data, 
+                self.Q_ref, M=67, bin_ratio=0.1, num_d_emulate=1E3)
+
+        if type(self.Q_ref) != np.array:
+            self.Q_ref = np.array([self.Q_ref])
+        if len(self.data_domain.shape) == 1:
+            self.data_domain = np.expand_dims(self.data_domain, axis=0)
+
+        self.rect_domain = np.zeros((self.data_domain.shape[0], 2))
+        r_width = 0.1*self.data_domain[:,1]
+
+        self.rect_domain[:, 0] = self.Q_ref - .5*r_width
+        self.rect_domain[:, 1] = self.Q_ref + .5*r_width
+         
+    def test_M(self):
+        """
+        Test that the right number of d_distr_samples are used to create
+        rho_D_M.
+        """
+        assert len(self.rho_D_M) == 67
+
+class test_unif_unif_01D(data_01D, unif_unif):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.unif_unif` on 01D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_unif_unif_01D, self).createData()
+        super(test_unif_unif_01D, self).setUp()
+
+class test_unif_unif_1D(data_1D, unif_unif):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.unif_unif` on 1D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_unif_unif_1D, self).createData()
+        super(test_unif_unif_1D, self).setUp()
+
+
+class test_unif_unif_2D(data_2D, unif_unif):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.unif_unif` on 2D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_unif_unif_2D, self).createData()
+        super(test_unif_unif_2D, self).setUp()
+
+
+class test_unif_unif_3D(data_3D, unif_unif):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.unif_unif` on 3D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_unif_unif_3D, self).createData()
+        super(test_unif_unif_3D, self).setUp()
+
+@unittest.skip("Implement or remove multivariate_gaussian method from module")        
+class test_multivariate_gaussian(unittest.TestCase):
+    """
+    TODO: Write a test for multivariate_gaussian or remove from module
+    """
+    pass
+
+class normal_normal(prob):
+    """
+    Set up :meth:`bet.calculateP.simpleFunP.normal_normal` on data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        if type(self.Q_ref) != np.array and type(self.Q_ref) != np.ndarray:
+            std = 1.0
+        else:
+	    std = np.ones(self.Q_ref.shape)
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.normal_normal(self.Q_ref, 
+		M=67, std=std, num_d_emulate=1E3)
+         
+    def test_M(self):
+        """
+        Test that the right number of d_distr_samples are used to create
+        rho_D_M.
+        """
+        assert len(self.rho_D_M) == 67
+
+class test_normal_normal_01D(data_01D, normal_normal):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.normal_normal` on 01D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_normal_normal_01D, self).createData()
+        super(test_normal_normal_01D, self).setUp()
+
+class test_normal_normal_1D(data_1D, normal_normal):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.normal_normal` on 1D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_normal_normal_1D, self).createData()
+        super(test_normal_normal_1D, self).setUp()
+
+
+class test_normal_normal_2D(data_2D, normal_normal):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.normal_normal` on 2D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_normal_normal_2D, self).createData()
+        super(test_normal_normal_2D, self).setUp()
+
+
+class test_normal_normal_3D(data_3D, normal_normal):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.normal_normal` on 3D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_normal_normal_3D, self).createData()
+        super(test_normal_normal_3D, self).setUp()
+
+
+class uniform_hyperrectangle_base(prob_uniform):
+    """
+    Provides set up and a test to check the number of ``d_distr_samples`` for
+    an exact simple function approximation of a hyperrectangle.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        if type(self.Q_ref) != np.array:
+            self.Q_ref = np.array([self.Q_ref])
+        if len(self.data_domain.shape) == 1:
+            self.data_domain = np.expand_dims(self.data_domain, axis=0)
+
+        self.rect_domain = np.zeros((self.data_domain.shape[0], 2))
+         
+    def test_M(self):
+        """
+        Test that the right number of d_distr_samples are used to create
+        rho_D_M.
+        """
+        if not isinstance(self.center_pts_per_edge, collections.Iterable):
+            assert len(self.rho_D_M) == (self.center_pts_per_edge+2)**self.mdim
+        else:
+            assert len(self.rho_D_M) == np.sum(self.center_pts_per_edge+2)**self.mdim
+
+
+class uniform_hyperrectangle_int(uniform_hyperrectanlge_base):
+    """
+    Set up :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user` on data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.center_pts_per_edge = 2
+        super(uniform_hyperrectangle_base, self).setUp()        
+
+
+class uniform_hyperrectangle_list(uniform_hyperrectangle_base):
+    """
+    Set up :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user` on data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.center_pts_per_edge = 2*np.ones((self.mdim,), dtype=np.int)
+        super(uniform_hyperrectangle_base, self).setUp()   
 
+class uniform_hyperrectangle_user_int(uniform_hyperrectangle_int):
 

From 7ad66feae823b10d2ebe93e6b1d1067ff727d228 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Sun, 12 Apr 2015 19:06:33 -0500
Subject: [PATCH 34/85] finished simpleFunP tests

---
 bet/calculateP/simpleFunP.py            |  39 +-
 bet/util.py                             |  19 +-
 test/test_calculateP/test_simpleFunP.py | 563 +++++++++++++++++++++++-
 3 files changed, 591 insertions(+), 30 deletions(-)

diff --git a/bet/calculateP/simpleFunP.py b/bet/calculateP/simpleFunP.py
index 147a271d..f00eab98 100644
--- a/bet/calculateP/simpleFunP.py
+++ b/bet/calculateP/simpleFunP.py
@@ -8,6 +8,8 @@
 import bet.calculateP.voronoiHistogram as vHist
 import collections
 
+# TODO change bins_size to more informative name like bound_size?
+
 def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     r"""
     TODO: THIS OCCASIONALLY FAILS test.test_calculateP.test_domain FIX ME
@@ -356,11 +358,12 @@ def uniform_hyperrectangle_user(data, domain, center_pts_per_edge=1):
     # determine the center of the domain
     if len(domain.shape) == 1:
         domain = np.expand_dims(domain, axis=1)
+    # TODO make sure the shape of the domain is correct, if not transpose it
     domain_center = np.mean(domain, 0)
     domain_min = np.min(domain, 0)
     domain_max = np.max(domain, 0)
     domain_lengths = domain_max - domain_min
-   
+ 
     # determine the ratio of the lengths of the domain to the lengths of the
     # hyperrectangle containing the data
     if len(data.shape) == 1:
@@ -368,7 +371,19 @@ def uniform_hyperrectangle_user(data, domain, center_pts_per_edge=1):
     data_max = np.max(data, 0)
     data_min = np.min(data, 0)
     data_lengths = data_max - data_min
-    bin_ratios = domain_lengths/data_lengths
+    bin_ratios = domain_lengths/data_lengths  
+
+    # TODO: Check for inputted center_pts_per_edge in case given as list
+    # or as numpy array to see if dimensions match data space dimensions and
+    # that positive integer values are being used. Also, create this change
+    # elsewhere since center_pts_per_edge is only a scalar if dim(D)=1.
+    if not isinstance(center_pts_per_edge, collections.Iterable):
+        center_pts_per_edge = np.ones((data.shape[1],)) * center_pts_per_edge
+    else:
+        if not len(center_pts_per_edge) == data.shape[1]:
+            center_pts_per_edge = np.ones((data.shape[1],))
+            print 'Warning: center_pts_per_edge dimension mismatch.'
+            print 'Using 1 in each dimension.'
 
     return uniform_hyperrectangle(data, domain_center, bin_ratios,
             center_pts_per_edge)
@@ -407,13 +422,20 @@ def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1)
     data_max = np.max(data, 0)
     data_min = np.min(data, 0)
 
+    # TODO: Check for inputted center_pts_per_edge in case given as list
+    # or as numpy array to see if dimensions match data space dimensions and
+    # that positive integer values are being used. Also, create this change
+    # elsewhere since center_pts_per_edge is only a scalar if dim(D)=1.
     if not isinstance(center_pts_per_edge, collections.Iterable):
-        center_pts_per_edge = np.ones((data.shape[1])) * center_pts_per_edge
+        center_pts_per_edge = np.ones((data.shape[1],)) * center_pts_per_edge
     else:
         if not len(center_pts_per_edge) == data.shape[1]:
-            center_pts_per_edge = np.ones((data.shape[1]))
+            center_pts_per_edge = np.ones((data.shape[1],))
             print 'Warning: center_pts_per_edge dimension mismatch.'
             print 'Using 1 in each dimension.'
+    # TODO: see note above
+    if not isinstance(bin_size, collections.Iterable):
+        bin_size = bin_size*np.ones((data.shape[1],))
 
     sur_domain = np.zeros((data.shape[1], 2))
     sur_domain[:, 0] = data_min
@@ -459,7 +481,7 @@ def uniform_hyperrectangle(data, Q_ref, bin_ratio, center_pts_per_edge=1):
     data_max = np.max(data, 0)
     data_min = np.min(data, 0)
 
-    # TO-DO: Check for inputted center_pts_per_edge in case given as list
+    # TODO: Check for inputted center_pts_per_edge in case given as list
     # or as numpy array to see if dimensions match data space dimensions and
     # that positive integer values are being used. Also, create this change
     # elsewhere since center_pts_per_edge is only a scalar if dim(D)=1.
@@ -467,9 +489,11 @@ def uniform_hyperrectangle(data, Q_ref, bin_ratio, center_pts_per_edge=1):
         center_pts_per_edge = np.ones((data.shape[1])) * center_pts_per_edge
     else:
         if not len(center_pts_per_edge) == data.shape[1]:
-            center_pts_per_edge = np.ones((data.shape[1]))
+            center_pts_per_edge = np.ones((data.shape[1], ))
             print 'Warning: center_pts_per_edge dimension mismatch.'
             print 'Using 1 in each dimension.'
+    if not isinstance(bin_ratio, collections.Iterable):
+        bin_ratio = bin_ratio*np.ones((data.shape[1], ))
 
     sur_domain = np.zeros((data.shape[1], 2))
     sur_domain[:, 0] = data_min
@@ -501,8 +525,9 @@ def uniform_data(data):
         ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
-    d_distr_prob = np.ones((data.shape[1],))
     if len(data.shape) == 1:
         data = np.expand_dims(data, axis=1)
+
+    d_distr_prob = np.ones((data.shape[0],), dtype=np.float)/data.shape[0]
     d_Tree = spatial.KDTree(data)
     return (d_distr_prob, data, d_Tree)
diff --git a/bet/util.py b/bet/util.py
index 3602d9af..4be3b105 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -57,7 +57,6 @@ def meshgrid_ndim(X):
 
     return X_new
 
-
 def get_global_values(array):
     """
     TODO: write a version that also works with captial mpi4py method calls
@@ -76,3 +75,21 @@ def get_global_values(array):
         return np.hstack(array)
     else:
         return np.vstack(array)
+
+def fix_dimensions_vector(vector):
+    """
+    TODO: write and document me
+    """
+    pass
+def fix_dimensions_domain(domain):
+    """
+    TODO: write and document me
+    """
+    pass
+def fix_dimensions_data(data):
+    """
+    TODO: write and document me
+    """
+    pass
+
+
diff --git a/test/test_calculateP/test_simpleFunP.py b/test/test_calculateP/test_simpleFunP.py
index b770798a..be875cb8 100644
--- a/test/test_calculateP/test_simpleFunP.py
+++ b/test/test_calculateP/test_simpleFunP.py
@@ -13,7 +13,7 @@
 
 """
 
-import os, bet, unittest
+import os, bet, unittest, collections   
 import bet.calculateP.simpleFunP as sFun
 import numpy as np
 import numpy.testing as nptest
@@ -57,10 +57,11 @@ def test_domain(self):
         and that the probabilities outside of the prescribed domain are zero.
         """
         # d_distr_samples are (mdim, M)
+        # rect_domain is (mdim, 2)
         inside = np.logical_and(np.all(np.greater_equal(self.d_distr_samples,
-            self.rect_domain[:, 0]),axis=1),
+            self.rect_domain[:, 0]), axis=1),
             np.all(np.less_equal(self.d_distr_samples,
-            self.rect_domain[:, 1]),axis=1)) 
+            self.rect_domain[:, 1]), axis=1)) 
         assert np.all(self.rho_D_M[inside] >= 0.0)
         #print self.rect_domain
         #print "ind, inside", inside
@@ -124,7 +125,7 @@ def createData(self):
         """
         self.data = np.random.random((100, 3))*10.0
         self.Q_ref = np.array([5.0, 5.0, 5.0])
-        self.data_domain = np.array([[0.0, 10.0], [0.0, 10.0] , [0.0, 10.0]])
+        self.data_domain = np.array([[0.0, 10.0], [0.0, 10.0], [0.0, 10.0]])
         self.mdim = 3
 
 class unif_unif(prob_uniform):
@@ -144,7 +145,7 @@ def setUp(self):
             self.data_domain = np.expand_dims(self.data_domain, axis=0)
 
         self.rect_domain = np.zeros((self.data_domain.shape[0], 2))
-        r_width = 0.1*self.data_domain[:,1]
+        r_width = 0.1*self.data_domain[:, 1]
 
         self.rect_domain[:, 0] = self.Q_ref - .5*r_width
         self.rect_domain[:, 1] = self.Q_ref + .5*r_width
@@ -202,7 +203,7 @@ def setUp(self):
         super(test_unif_unif_3D, self).createData()
         super(test_unif_unif_3D, self).setUp()
 
-@unittest.skip("Implement or remove multivariate_gaussian method from module")        
+@unittest.skip("Implement or remove multivariate_gaussian method from module")
 class test_multivariate_gaussian(unittest.TestCase):
     """
     TODO: Write a test for multivariate_gaussian or remove from module
@@ -220,7 +221,7 @@ def setUp(self):
         if type(self.Q_ref) != np.array and type(self.Q_ref) != np.ndarray:
             std = 1.0
         else:
-	    std = np.ones(self.Q_ref.shape)
+            std = np.ones(self.Q_ref.shape)
         self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.normal_normal(self.Q_ref, 
 		M=67, std=std, num_d_emulate=1E3)
          
@@ -283,50 +284,568 @@ class uniform_hyperrectangle_base(prob_uniform):
     Provides set up and a test to check the number of ``d_distr_samples`` for
     an exact simple function approximation of a hyperrectangle.
     """
+    def test_M(self):
+        """
+        Test that the right number of d_distr_samples are used to create
+        rho_D_M.
+        """
+        if not isinstance(self.center_pts_per_edge, collections.Iterable):
+            assert len(self.rho_D_M) == (self.center_pts_per_edge+2)**self.mdim
+        else:
+            assert len(self.rho_D_M) == np.prod(self.center_pts_per_edge+2)
+
+
+class uniform_hyperrectangle_int(uniform_hyperrectangle_base):
+    """
+    Set up :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_*` with an
+    int type value for ``center_pts_per_edge``.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.center_pts_per_edge = 2
+
+
+class uniform_hyperrectangle_list(uniform_hyperrectangle_base):
+    """
+    Set up :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_*` with an
+    iterable type value for ``center_pts_per_edge``.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.center_pts_per_edge = 2*np.ones((self.mdim,), dtype=np.int)
+
+class uniform_hyperrectangle_user_int(uniform_hyperrectangle_int):
+    """
+    Set up :met:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user` with an
+    int type of value fo r``center_pts_per_edge``
+    
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(uniform_hyperrectangle_user_int, self).setUp()
+        if type(self.Q_ref) != np.array:
+            Q_ref = np.array([self.Q_ref])
+        else:
+            Q_ref = self.Q_ref
+        if len(self.data_domain.shape) == 1:
+            data_domain = np.expand_dims(self.data_domain, axis=0)
+        else:
+            data_domain = self.data_domain
+
+        self.rect_domain = np.zeros((data_domain.shape[0], 2))
+        r_width = 0.1*data_domain[:, 1]
+
+        self.rect_domain[:, 0] = Q_ref - .5*r_width
+        self.rect_domain[:, 1] = Q_ref + .5*r_width
+
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.uniform_hyperrectangle_user(self.data, 
+                self.rect_domain.transpose(), self.center_pts_per_edge)
+
+class uniform_hyperrectangle_user_list(uniform_hyperrectangle_list):
+    """
+    Set up :met:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user` with an
+    int type of value fo r``center_pts_per_edge``
+    
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(uniform_hyperrectangle_user_list, self).setUp()
+        if type(self.Q_ref) != np.array:
+            Q_ref = np.array([self.Q_ref])
+        else:
+            Q_ref = self.Q_ref
+        if len(self.data_domain.shape) == 1:
+            data_domain = np.expand_dims(self.data_domain, axis=0)
+        else:
+            data_domain = self.data_domain
+
+        self.rect_domain = np.zeros((data_domain.shape[0], 2))
+        r_width = 0.1*data_domain[:, 1]
+
+        self.rect_domain[:, 0] = Q_ref - .5*r_width
+        self.rect_domain[:, 1] = Q_ref + .5*r_width
+
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.uniform_hyperrectangle_user(self.data, 
+                self.rect_domain.transpose(), self.center_pts_per_edge)
+
+
+class test_uniform_hyperrectangle_user_int_01D(data_01D, uniform_hyperrectangle_user_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user_int` on 01D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_user_int_01D, self).createData()
+        super(test_uniform_hyperrectangle_user_int_01D, self).setUp()
+
+class test_uniform_hyperrectangle_user_int_1D(data_1D, uniform_hyperrectangle_user_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user_int` on 1D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_user_int_1D, self).createData()
+        super(test_uniform_hyperrectangle_user_int_1D, self).setUp()
+
+
+class test_uniform_hyperrectangle_user_int_2D(data_2D, uniform_hyperrectangle_user_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user_int` on 2D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_user_int_2D, self).createData()
+        super(test_uniform_hyperrectangle_user_int_2D, self).setUp()
+
+
+class test_uniform_hyperrectangle_user_int_3D(data_3D, uniform_hyperrectangle_user_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user_int` on 3D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_user_int_3D, self).createData()
+        super(test_uniform_hyperrectangle_user_int_3D, self).setUp()
+
+
+class test_uniform_hyperrectangle_user_list_01D(data_01D, uniform_hyperrectangle_user_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user_list` on 01D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_user_list_01D, self).createData()
+        super(test_uniform_hyperrectangle_user_list_01D, self).setUp()
+
+class test_uniform_hyperrectangle_user_list_1D(data_1D, uniform_hyperrectangle_user_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user_list` on 1D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_user_list_1D, self).createData()
+        super(test_uniform_hyperrectangle_user_list_1D, self).setUp()
+
+
+class test_uniform_hyperrectangle_user_list_2D(data_2D, uniform_hyperrectangle_user_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user_list` on 2D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_user_list_2D, self).createData()
+        super(test_uniform_hyperrectangle_user_list_2D, self).setUp()
+
+
+class test_uniform_hyperrectangle_user_list_3D(data_3D, uniform_hyperrectangle_user_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user_list` on 3D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_user_list_3D, self).createData()
+        super(test_uniform_hyperrectangle_user_list_3D, self).setUp()
+
+
+class uniform_hyperrectangle_size_int(uniform_hyperrectangle_int):
+    """
+    Set up :met:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size` with an
+    int type of value fo r``center_pts_per_edge``
+    
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(uniform_hyperrectangle_size_int, self).setUp()
+        if type(self.Q_ref) != np.array:
+            Q_ref = np.array([self.Q_ref])
+        else:
+            Q_ref = self.Q_ref
+        if len(self.data_domain.shape) == 1:
+            data_domain = np.expand_dims(self.data_domain, axis=0)
+        else:
+            data_domain = self.data_domain
+
+        self.rect_domain = np.zeros((data_domain.shape[0], 2))
+        binsize = 1.0
+        r_width = binsize*np.ones(data_domain[:, 1].shape)
+
+        self.rect_domain[:, 0] = Q_ref - .5*r_width
+        self.rect_domain[:, 1] = Q_ref + .5*r_width
+
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.uniform_hyperrectangle_binsize(self.data, 
+                self.Q_ref, binsize, self.center_pts_per_edge)
+
+class uniform_hyperrectangle_size_list(uniform_hyperrectangle_list):
+    """
+    Set up :met:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size` with an
+    int type of value fo r``center_pts_per_edge``
+    
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(uniform_hyperrectangle_size_list, self).setUp()
+        if type(self.Q_ref) != np.array:
+            Q_ref = np.array([self.Q_ref])
+        else:
+            Q_ref = self.Q_ref
+        if len(self.data_domain.shape) == 1:
+            data_domain = np.expand_dims(self.data_domain, axis=0)
+        else:
+            data_domain = self.data_domain
+
+        self.rect_domain = np.zeros((data_domain.shape[0], 2))
+        binsize = 1.0*np.ones((data_domain.shape[0],))
+        r_width = binsize
+
+        self.rect_domain[:, 0] = Q_ref - .5*r_width
+        self.rect_domain[:, 1] = Q_ref + .5*r_width
+
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.uniform_hyperrectangle_binsize(self.data, 
+                self.Q_ref, binsize, self.center_pts_per_edge)
+
+
+class test_uniform_hyperrectangle_size_int_01D(data_01D, uniform_hyperrectangle_size_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size_int` on 01D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_size_int_01D, self).createData()
+        super(test_uniform_hyperrectangle_size_int_01D, self).setUp()
+
+class test_uniform_hyperrectangle_size_int_1D(data_1D, uniform_hyperrectangle_size_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size_int` on 1D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_size_int_1D, self).createData()
+        super(test_uniform_hyperrectangle_size_int_1D, self).setUp()
+
+
+class test_uniform_hyperrectangle_size_int_2D(data_2D, uniform_hyperrectangle_size_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size_int` on 2D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_size_int_2D, self).createData()
+        super(test_uniform_hyperrectangle_size_int_2D, self).setUp()
+
+
+class test_uniform_hyperrectangle_size_int_3D(data_3D, uniform_hyperrectangle_size_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size_int` on 3D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_size_int_3D, self).createData()
+        super(test_uniform_hyperrectangle_size_int_3D, self).setUp()
+
+
+class test_uniform_hyperrectangle_size_list_01D(data_01D, uniform_hyperrectangle_size_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size_list` on 01D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_size_list_01D, self).createData()
+        super(test_uniform_hyperrectangle_size_list_01D, self).setUp()
+
+class test_uniform_hyperrectangle_size_list_1D(data_1D, uniform_hyperrectangle_size_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size_list` on 1D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_size_list_1D, self).createData()
+        super(test_uniform_hyperrectangle_size_list_1D, self).setUp()
+
+
+class test_uniform_hyperrectangle_size_list_2D(data_2D, uniform_hyperrectangle_size_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size_list` on 2D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_size_list_2D, self).createData()
+        super(test_uniform_hyperrectangle_size_list_2D, self).setUp()
+
+
+class test_uniform_hyperrectangle_size_list_3D(data_3D, uniform_hyperrectangle_size_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_size_list` on 3D data domain.
+    """
     def setUp(self):
         """
         Set up problem.
         """
+        super(test_uniform_hyperrectangle_size_list_3D, self).createData()
+        super(test_uniform_hyperrectangle_size_list_3D, self).setUp()
+
+class uniform_hyperrectangle_ratio_int(uniform_hyperrectangle_int):
+    """
+    Set up :met:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio` with an
+    int type of value fo r``center_pts_per_edge``
+    
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(uniform_hyperrectangle_ratio_int, self).setUp()
+        if type(self.Q_ref) != np.array:
+            Q_ref = np.array([self.Q_ref])
+        else:
+            Q_ref = self.Q_ref
+        if len(self.data_domain.shape) == 1:
+            data_domain = np.expand_dims(self.data_domain, axis=0)
+        else:
+            data_domain = self.data_domain
+
+        self.rect_domain = np.zeros((data_domain.shape[0], 2))
+        binratio = 0.1
+        r_width = binratio*data_domain[:, 1]
+
+        self.rect_domain[:, 0] = Q_ref - .5*r_width
+        self.rect_domain[:, 1] = Q_ref + .5*r_width
+
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.uniform_hyperrectangle(self.data, 
+                self.Q_ref, binratio, self.center_pts_per_edge)
+
+class uniform_hyperrectangle_ratio_list(uniform_hyperrectangle_list):
+    """
+    Set up :met:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio` with an
+    int type of value fo r``center_pts_per_edge``
+    
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(uniform_hyperrectangle_ratio_list, self).setUp()
+        if type(self.Q_ref) != np.array:
+            Q_ref = np.array([self.Q_ref])
+        else:
+            Q_ref = self.Q_ref
+        if len(self.data_domain.shape) == 1:
+            data_domain = np.expand_dims(self.data_domain, axis=0)
+        else:
+            data_domain = self.data_domain
+
+        self.rect_domain = np.zeros((data_domain.shape[0], 2))
+        binratio = 0.1*np.ones((data_domain.shape[0],))
+        r_width = binratio*data_domain[:,1]
+
+        self.rect_domain[:, 0] = Q_ref - .5*r_width
+        self.rect_domain[:, 1] = Q_ref + .5*r_width
+
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.uniform_hyperrectangle(self.data, 
+                self.Q_ref, binratio, self.center_pts_per_edge)
+
+
+class test_uniform_hyperrectangle_ratio_int_01D(data_01D, uniform_hyperrectangle_ratio_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio_int` on 01D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_ratio_int_01D, self).createData()
+        super(test_uniform_hyperrectangle_ratio_int_01D, self).setUp()
+
+class test_uniform_hyperrectangle_ratio_int_1D(data_1D, uniform_hyperrectangle_ratio_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio_int` on 1D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_ratio_int_1D, self).createData()
+        super(test_uniform_hyperrectangle_ratio_int_1D, self).setUp()
+
+
+class test_uniform_hyperrectangle_ratio_int_2D(data_2D, uniform_hyperrectangle_ratio_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio_int` on 2D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_ratio_int_2D, self).createData()
+        super(test_uniform_hyperrectangle_ratio_int_2D, self).setUp()
+
+
+class test_uniform_hyperrectangle_ratio_int_3D(data_3D, uniform_hyperrectangle_ratio_int):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio_int` on 3D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_ratio_int_3D, self).createData()
+        super(test_uniform_hyperrectangle_ratio_int_3D, self).setUp()
+
+
+class test_uniform_hyperrectangle_ratio_list_01D(data_01D, uniform_hyperrectangle_ratio_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio_list` on 01D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_ratio_list_01D, self).createData()
+        super(test_uniform_hyperrectangle_ratio_list_01D, self).setUp()
+
+class test_uniform_hyperrectangle_ratio_list_1D(data_1D, uniform_hyperrectangle_ratio_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio_list` on 1D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_ratio_list_1D, self).createData()
+        super(test_uniform_hyperrectangle_ratio_list_1D, self).setUp()
+
+
+class test_uniform_hyperrectangle_ratio_list_2D(data_2D, uniform_hyperrectangle_ratio_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio_list` on 2D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_ratio_list_2D, self).createData()
+        super(test_uniform_hyperrectangle_ratio_list_2D, self).setUp()
+
+
+class test_uniform_hyperrectangle_ratio_list_3D(data_3D, uniform_hyperrectangle_ratio_list):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_ratio_list` on 3D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_hyperrectangle_ratio_list_3D, self).createData()
+        super(test_uniform_hyperrectangle_ratio_list_3D, self).setUp()
+
+class uniform_data(prob_uniform):
+    """
+    Set up :meth:`bet.calculateP.simpleFunP.uniform_data` on data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = sFun.uniform_data(self.data)
+
         if type(self.Q_ref) != np.array:
             self.Q_ref = np.array([self.Q_ref])
         if len(self.data_domain.shape) == 1:
             self.data_domain = np.expand_dims(self.data_domain, axis=0)
-
-        self.rect_domain = np.zeros((self.data_domain.shape[0], 2))
+        self.rect_domain = self.data_domain
          
     def test_M(self):
         """
         Test that the right number of d_distr_samples are used to create
         rho_D_M.
         """
-        if not isinstance(self.center_pts_per_edge, collections.Iterable):
-            assert len(self.rho_D_M) == (self.center_pts_per_edge+2)**self.mdim
-        else:
-            assert len(self.rho_D_M) == np.sum(self.center_pts_per_edge+2)**self.mdim
+        assert len(self.rho_D_M) == self.data.shape[0]
 
+class test_uniform_data_01D(data_01D, uniform_data):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_data` on 01D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_data_01D, self).createData()
+        super(test_uniform_data_01D, self).setUp()
 
-class uniform_hyperrectangle_int(uniform_hyperrectanlge_base):
+class test_uniform_data_1D(data_1D, uniform_data):
     """
-    Set up :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user` on data domain.
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_data` on 1D data domain.
     """
     def setUp(self):
         """
         Set up problem.
         """
-        self.center_pts_per_edge = 2
-        super(uniform_hyperrectangle_base, self).setUp()        
+        super(test_uniform_data_1D, self).createData()
+        super(test_uniform_data_1D, self).setUp()
 
 
-class uniform_hyperrectangle_list(uniform_hyperrectangle_base):
+class test_uniform_data_2D(data_2D, uniform_data):
     """
-    Set up :meth:`bet.calculateP.simpleFunP.uniform_hyperrectangle_user` on data domain.
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_data` on 2D data domain.
     """
     def setUp(self):
         """
         Set up problem.
         """
-        self.center_pts_per_edge = 2*np.ones((self.mdim,), dtype=np.int)
-        super(uniform_hyperrectangle_base, self).setUp()   
+        super(test_uniform_data_2D, self).createData()
+        super(test_uniform_data_2D, self).setUp()
+
+
+class test_uniform_data_3D(data_3D, uniform_data):
+    """
+    Tests :meth:`bet.calculateP.simpleFunP.uniform_data` on 3D data domain.
+    """
+    def setUp(self):
+        """
+        Set up problem.
+        """
+        super(test_uniform_data_3D, self).createData()
+        super(test_uniform_data_3D, self).setUp()
 
-class uniform_hyperrectangle_user_int(uniform_hyperrectangle_int):
 

From b884007b714bb92dfa806feea72baf298c679d74 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Sun, 12 Apr 2015 20:28:30 -0500
Subject: [PATCH 35/85] working on voronoHistogram tests

---
 bet/calculateP/voronoiHistogram.py            |  3 ++
 test/test_calculateP/test_voronoiHistogram.py | 48 +++++++++++++++++++
 2 files changed, 51 insertions(+)

diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index 7cac1112..a10d05b6 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -3,6 +3,9 @@
 from scipy import spatial
 import bet.util as util
 
+# TODO: update documentation to make sure that the retuns and rtype are corrct
+# for each of the methods
+
 def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_domain):
     """
     Generates a regular grid of center points that define the voronoi
diff --git a/test/test_calculateP/test_voronoiHistogram.py b/test/test_calculateP/test_voronoiHistogram.py
index e69de29b..afafdb07 100644
--- a/test/test_calculateP/test_voronoiHistogram.py
+++ b/test/test_calculateP/test_voronoiHistogram.py
@@ -0,0 +1,48 @@
+# Lindley Graham 04/12/2015
+"""
+"""
+
+import os, unittest, collections
+import bet.caculcateP.voronoiHistogram as vHist
+import numpy as np
+import numpy.testing as nptest
+
+# center_and_layer1_points_binsize
+# center_and_layer1_points
+
+# make sure dimensions are correct
+# make sure the rect_domain is correct
+# make sure the bounding layer is correct 
+
+
+
+# edges_regular_binsize
+# edges_regular
+# edges_from_points
+
+# make sure dimensions are correct
+# make sure int_l1 is correct
+# make sure int_l2 is correct
+
+
+
+# points_from_edges
+# make sure dimensions are correct
+# make sure shape is correct
+# make sure points are correct
+
+
+# histogrammdd_volumes
+# make sure dimensions are correct
+# make sure H sums to 1
+# make sure volume sums to 1?
+# make sure volumes are nonnegative
+
+# simple_fun_uniform
+# make sure dimensions are correct
+# make sure rho_D_M sums to 1
+# make sure rho_D_M is nonnegative
+
+
+
+

From 084c617c52dc6705753dbc807ac6455b8ee2e2e7 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Sun, 12 Apr 2015 20:51:42 -0500
Subject: [PATCH 36/85] working on voronoi tests

---
 test/test_calculateP/test_voronoiHistogram.py | 28 +++++++++++++++++--
 1 file changed, 25 insertions(+), 3 deletions(-)

diff --git a/test/test_calculateP/test_voronoiHistogram.py b/test/test_calculateP/test_voronoiHistogram.py
index afafdb07..22fae0da 100644
--- a/test/test_calculateP/test_voronoiHistogram.py
+++ b/test/test_calculateP/test_voronoiHistogram.py
@@ -13,16 +13,37 @@
 # make sure dimensions are correct
 # make sure the rect_domain is correct
 # make sure the bounding layer is correct 
-
-
+class test_center_and_layer1_points(unittest.TestCase):
+    def setUp(self):
+        pass
+    def test_dimensions(self):
+        pass
+    def test_domain(self):
+        pass
+    def test_bounding_layer(self):
+        pass
+
+class test_center_and_layer1_points_binsize(test_center_and_layer1_points):
+    def setUp(self):
+        self.r_ratio = None
+        super(center_and_layer1_points_binsize, self).setUp()
+        self.r_size = None
 
 # edges_regular_binsize
 # edges_regular
 # edges_from_points
-
 # make sure dimensions are correct
 # make sure int_l1 is correct
 # make sure int_l2 is correct
+class edges(object):
+    def test_dimensions(self):
+        pass
+
+class edges_regular(edges, unittest.TestCase):
+    def test_l1(self):
+        pass
+    def test_l2(self):
+        pass
 
 
 
@@ -32,6 +53,7 @@
 # make sure points are correct
 
 
+
 # histogrammdd_volumes
 # make sure dimensions are correct
 # make sure H sums to 1

From 236e30c4633cd2a2fe9d146e2a24ff7e815d08d4 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 13 Apr 2015 20:52:34 -0500
Subject: [PATCH 37/85] finished testing  center_pts_and_layer1(binsize)

---
 bet/calculateP/voronoiHistogram.py            |  22 +-
 test/test_calculateP/test_voronoiHistogram.py | 301 +++++++++++++++++-
 2 files changed, 300 insertions(+), 23 deletions(-)

diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index a10d05b6..f347d966 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -29,10 +29,11 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
 
     :rtype: tuple
-    :returns: (points, interior_and_layer1) where where points is an
+    :returns: (points, interior_and_layer1, rect_domain) where where points is an
         :class:`numpy.ndarray` of shape (num_points, dim), interior_and_layer1
         is a list() of dim :class:`numpy.ndarray`s of shape
-        (center_pts_per_edge+2,).
+        (center_pts_per_edge+2,), rect_domain is a :class:`numpy.ndarray` of
+        shape (mdim, 2)
 
     """
     if np.all(np.greater(r_size, 1)):
@@ -47,6 +48,9 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
     rect_domain[:, 0] = center - .5*rect_width
     rect_domain[:, 1] = center + .5*rect_width
 
+    if not isinstance(center_pts_per_edge, np.ndarray):
+        center_pts_per_edge = np.array(center_pts_per_edge)
+
     # determine the locations of the points for the 1st bounding layer
     layer1_left = rect_domain[:, 0]-rect_width/(2*center_pts_per_edge)
     layer1_right = rect_domain[:, 1]+rect_width/(2*center_pts_per_edge)
@@ -59,11 +63,7 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
         interior_and_layer1.append(int_l1)
 
     # use meshgrid to make the hyperrectangle shells
-    if sur_domain.shape[0] == 1:
-        points = interior_and_layer1[0]
-    else:
-        points = util.meshgrid_ndim(interior_and_layer1)
-
+    points = util.meshgrid_ndim(interior_and_layer1)
     return (points, interior_and_layer1, rect_domain)
 
 # TODO This and the previous function are almost identical. Maybe calculate r_size
@@ -94,7 +94,8 @@ def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
     :returns: (points, interior_and_layer1) where where points is an
         :class:`numpy.ndarray` of shape (num_points, dim), interior_and_layer1
         is a list() of dim :class:`numpy.ndarray`s of shape
-        (center_pts_per_edge+2,).
+        (center_pts_per_edge+2,), rect_domain is a :class:`numpy.ndarray` of
+        shape (mdim, 2).
 
     """
     if np.all(np.greater(r_ratio, 1)):
@@ -122,10 +123,7 @@ def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
         interior_and_layer1.append(int_l1)
 
     # use meshgrid to make the hyperrectangle shells
-    if sur_domain.shape[0] == 1:
-        points = interior_and_layer1[0]
-    else:
-        points = util.meshgrid_ndim(interior_and_layer1)
+    points = util.meshgrid_ndim(interior_and_layer1)
     return (points, interior_and_layer1, rect_domain)
 
 
diff --git a/test/test_calculateP/test_voronoiHistogram.py b/test/test_calculateP/test_voronoiHistogram.py
index 22fae0da..0a8461a6 100644
--- a/test/test_calculateP/test_voronoiHistogram.py
+++ b/test/test_calculateP/test_voronoiHistogram.py
@@ -1,33 +1,312 @@
+# -*- coding: utf-8 -*-
 # Lindley Graham 04/12/2015
 """
+Test methods in :mod:`bet.calculateP.voronoiHistogram`. Since this module is
+not meant to be directly accessed by the user we only test for dimensions 1, 2,
+3. We also assume that ``center_points_per_edge`` is a list as specified in the
+docString for the methods in :mod:`bet.calculateP.voronoiHistogram`. In other
+words, all the dimensions of any arrays must be correct before calling these
+methods.
 """
 
 import os, unittest, collections
-import bet.caculcateP.voronoiHistogram as vHist
+import bet.calculateP.voronoiHistogram as vHist
+import bet.util as util
 import numpy as np
 import numpy.testing as nptest
 
+# Do below for dimensions 01, 1, 2, and 3
+class domain_1D(object):
+    """
+    Sets up 1D domain domain problem.
+    """
+    def createDomain(self):
+        """
+        Set up data.
+        """
+        self.center = np.array([5.0])
+        self.sur_domain = np.expand_dims(np.array([0.0, 10.0]), axis=0)
+        self.mdim = 1
+        self.center_pts_per_edge = [1]
+
+
+class domain_2D(object):
+    """
+    Sets up 2D domain domain problem.
+    """
+    def createDomain(self):
+        """
+        Set up data.
+        """
+        self.center = np.array([5.0, 5.0])
+        self.sur_domain = np.array([[0.0, 10.0], [0.0, 10.0]])
+        self.mdim = 2
+        self.center_pts_per_edge = [1,2]
+
+
+class domain_3D(object):
+    """
+    Sets up 3D domain domain problem.
+    """
+    def createDomain(self):
+        """
+        Set up data.
+        """
+        self.center = np.array([5.0, 5.0, 5.0])
+        self.sur_domain = np.array([[0.0, 10.0], [0.0, 10.0], [0.0, 10.0]])
+        self.mdim = 3
+        self.center_pts_per_edge = [1,2,1]
+
 # center_and_layer1_points_binsize
 # center_and_layer1_points
 
 # make sure dimensions are correct
 # make sure the rect_domain is correct
 # make sure the bounding layer is correct 
-class test_center_and_layer1_points(unittest.TestCase):
+#class center_and_layer1_points(unittest.TestCase):
+class center_and_layer1_points(object):
+    """
+    Test :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points`
+    """
     def setUp(self):
-        pass
+        """
+        Set up the problem for
+        :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points`
+        """
+        self.create_output()
+        output = vHist.center_and_layer1_points(self.center_pts_per_edge,
+                self.center, self.r_ratio, self.sur_domain)
+        self.points, self.interior_and_layer1_VH, self.rect_domain_VH = output
+
+    def create_output(self):
+        """
+        Create output to test the output of
+        :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points`
+        against.
+
+        :param r_ratio: The ratio of the length of the sides of the
+            hyperrectangle to the surrounding domain
+        :type r_ratio: int or list()
+        """
+        sur_width = self.sur_domain[:, 1] - self.sur_domain[:, 0]
+        rect_width = self.r_ratio*sur_width
+        self.rect_domain = np.empty(self.sur_domain.shape)
+        self.rect_domain[:, 0] = self.center - .5*rect_width
+        self.rect_domain[:, 1] = self.center + .5*rect_width
+        if not isinstance(self.center_pts_per_edge, np.ndarray):
+            self.center_pts_per_edge = np.array(self.center_pts_per_edge)
+        layer1_left = self.rect_domain[:, 0]-rect_width/(2*self.center_pts_per_edge)
+        layer1_right = self.rect_domain[:, 1]+rect_width/(2*self.center_pts_per_edge)
+        self.interior_and_layer1 = list()
+        for dim in xrange(self.mdim):
+            self.interior_and_layer1.append(np.linspace(layer1_left[dim],
+                layer1_right[dim], self.center_pts_per_edge[dim]+2))
+
     def test_dimensions(self):
-        pass
-    def test_domain(self):
-        pass
+        """
+        Test the dimensions for :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points`
+        """
+        assert self.points.shape == (np.prod(self.center_pts_per_edge+2),
+                self.mdim)
+        assert len(self.interior_and_layer1_VH) == self.mdim
+        nptest.assert_array_almost_equal(self.center_pts_per_edge+2, 
+                [len(dim_layer) for dim_layer in self.interior_and_layer1_VH])
+        assert self.rect_domain.shape == (self.mdim, 2)
+
+    def test_rect_domain(self):
+        """
+        Test that the ``rect_domain`` is correct.
+        """
+        nptest.assert_array_almost_equal(self.rect_domain,
+                self.rect_domain_VH)
+
     def test_bounding_layer(self):
-        pass
+        """
+        Test that the interior_and_layer1 is correct.
+        """
+        compare_list = list()
+        for mine, meth in zip(self.interior_and_layer1, self.interior_and_layer1_VH):
+            compare_list.append(np.allclose(mine, meth))
+        assert np.all(compare_list)        
+
+    def test_points(self):
+        """
+        Test that the points are correct.
+        """
+        nptest.assert_array_almost_equal(self.points,
+            util.meshgrid_ndim(self.interior_and_layer1))
+
+class center_and_layer1_points_double(center_and_layer1_points):
+    """
+    Provides set up for
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` when
+    r_ratio is a double.
+    """
+    def setUp(self):
+        self.r_ratio = 0.2
+        super(center_and_layer1_points_double, self).setUp()
 
-class test_center_and_layer1_points_binsize(test_center_and_layer1_points):
+class center_and_layer1_points_list(center_and_layer1_points):
+    """
+    Provides set up for
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` when
+    r_ratio is a list.
+    """
     def setUp(self):
-        self.r_ratio = None
-        super(center_and_layer1_points_binsize, self).setUp()
-        self.r_size = None
+        self.r_ratio = 0.2*np.ones(self.mdim)
+        super(center_and_layer1_points_list, self).setUp()
+
+
+class center_and_layer1_points_binsize(center_and_layer1_points):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points_binsize`
+    """
+    def setUp(self):
+        """
+        Set up the problem for
+        :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points`
+        """
+        self.r_ratio = self.r_size/self.sur_domain[:,1]
+        super(center_and_layer1_points_binsize, self).create_output()
+        output = vHist.center_and_layer1_points_binsize(self.center_pts_per_edge,
+                self.center, self.r_size, self.sur_domain)
+        self.points, self.interior_and_layer1_VH, self.rect_domain_VH = output
+
+
+class center_and_layer1_points_binsize_list(center_and_layer1_points_binsize):
+    """
+    Provides set up for
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points_binsize` when
+    r_size is a list.
+    """
+    def setUp(self):
+        self.r_size = self.sur_domain[:,1]*.02
+        super(center_and_layer1_points_binsize_list, self).setUp()
+
+class center_and_layer1_points_binsize_double(center_and_layer1_points_binsize):
+    """
+    Provides set up for
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points_binsize` when
+    r_size is a double.
+    """
+    def setUp(self):
+        self.r_size = self.sur_domain[0,1]*.02
+        super(center_and_layer1_points_binsize_double, self).setUp()
+
+class test_calp_list_1D(domain_1D, center_and_layer1_points_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 1D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_calp_list_1D, self).createDomain()
+        super(test_calp_list_1D, self).setUp()
+class test_calp_list_2D(domain_2D, center_and_layer1_points_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 2D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_calp_list_2D, self).createDomain()
+        super(test_calp_list_2D, self).setUp()
+class test_calp_list_3D(domain_3D, center_and_layer1_points_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 3D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_calp_list_3D, self).createDomain()
+        super(test_calp_list_3D, self).setUp()
+
+class test_calp_double_1D(domain_1D, center_and_layer1_points_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 1D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_calp_double_1D, self).createDomain()
+        super(test_calp_double_1D, self).setUp()
+class test_calp_double_2D(domain_2D, center_and_layer1_points_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 2D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_calp_double_2D, self).createDomain()
+        super(test_calp_double_2D, self).setUp()
+class test_calp_double_3D(domain_3D, center_and_layer1_points_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 3D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_calp_double_3D, self).createDomain()
+        super(test_calp_double_3D, self).setUp()
+
+
+class test_calps_list_1D(domain_1D, center_and_layer1_points_binsize_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 1D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_calps_list_1D, self).createDomain()
+        super(test_calps_list_1D, self).setUp()
+class test_calps_list_2D(domain_2D, center_and_layer1_points_binsize_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 2D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_calps_list_2D, self).createDomain()
+        super(test_calps_list_2D, self).setUp()
+class test_calps_list_3D(domain_3D, center_and_layer1_points_binsize_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 3D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_calps_list_3D, self).createDomain()
+        super(test_calps_list_3D, self).setUp()
+
+class test_calps_double_1D(domain_1D, center_and_layer1_points_binsize_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 1D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_calps_double_1D, self).createDomain()
+        super(test_calps_double_1D, self).setUp()
+class test_calps_double_2D(domain_2D, center_and_layer1_points_binsize_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 2D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_calps_double_2D, self).createDomain()
+        super(test_calps_double_2D, self).setUp()
+class test_calps_double_3D(domain_3D, center_and_layer1_points_binsize_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points` for a 3D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_calps_double_3D, self).createDomain()
+        super(test_calps_double_3D, self).setUp()
+
 
 # edges_regular_binsize
 # edges_regular

From 15b642ac314b5b18837f88ffd6cf073c18009b7f Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 15 Apr 2015 14:38:35 -0500
Subject: [PATCH 38/85] two methods remaining in voronoiHistogram that are
 untested

---
 bet/calculateP/voronoiHistogram.py            |  31 +-
 test/test_calculateP/test_voronoiHistogram.py | 273 +++++++++++++++++-
 2 files changed, 276 insertions(+), 28 deletions(-)

diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index f347d966..dc026db4 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -157,7 +157,7 @@ def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
         where points is an :class:`numpy.ndarray` of shape (num_points, dim),
         interior_and_layer1 and interior_and_layer2 are lists of dim
         :class:`numpy.ndarray`s of shape (center_pts_per_edge+2,) and
-        (center_pts_per_edge+4,) respectively.
+        (center_pts_per_edge+3,) respectively.
 
     """
     # TODO This is redoing stuff that was done when generating the points
@@ -167,6 +167,15 @@ def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
     rect_domain[:, 0] = center - .5*rect_width
     rect_domain[:, 1] = center + .5*rect_width
 
+    if np.any(np.greater_equal(sur_domain[:, 0], rect_domain[:, 0])):
+        msg = "The hyperrectangle defined by this size is larger than the"
+        msg += "original domain."
+        print msg
+    elif np.any(np.less_equal(sur_domain[:, 1], rect_domain[:, 1])):
+        msg = "The hyperrectangle defined by this size is larger than the"
+        msg += "original domain."
+        print msg
+    
     rect_edges = list()
     rect_and_sur_edges = list()
     for dim in xrange(sur_domain.shape[0]):
@@ -208,18 +217,19 @@ def edges_regular(center_pts_per_edge, center, r_ratio, sur_domain):
     :param sur_domain: minima and maxima of each dimension defining the
         surrounding domain
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
-
+    TODO fix return documentation
     :rtype: tuple
+    TODO FIX description of the return to match what is acctually returned
     :returns: (points, interior_and_layer1, interior_and_doublelayer) where
         where points is an :class:`numpy.ndarray` of shape (num_points, dim),
         interior_and_layer1 and interior_and_layer2 are lists of dim
         :class:`numpy.ndarray`s of shape (center_pts_per_edge+2,) and
-        (center_pts_per_edge+4,) respectively.
+        (center_pts_per_edge+3,) respectively.
 
     """
-    if np.all(np.greater(r_ratio, 1)):
+    if np.any(np.greater(r_ratio, 1)):
         msg = "The hyperrectangle defined by this ratio is larger than the"
-        msg += "original domain."
+        msg += "original domain. RATIO: {}".format(r_ratio)
         print msg
 
     # determine the width of the surrounding domain
@@ -230,17 +240,6 @@ def edges_regular(center_pts_per_edge, center, r_ratio, sur_domain):
     rect_domain[:, 0] = center - .5*rect_width
     rect_domain[:, 1] = center + .5*rect_width
     
-    if np.all(np.greater_equal(sur_domain[:, 0], rect_domain[:, 0])):
-        msg = "The hyperrectangle defined by this ratio is larger than the"
-        msg += "original domain."
-        print msg
-
-    elif np.all(np.less_equal(sur_domain[:, 1], rect_domain[:, 1])):
-        msg = "The hyperrectangle defined by this ratio is larger than the"
-        msg += "original domain."
-        print msg
-
-
     rect_edges = list()
     rect_and_sur_edges = list()
     for dim in xrange(sur_domain.shape[0]):
diff --git a/test/test_calculateP/test_voronoiHistogram.py b/test/test_calculateP/test_voronoiHistogram.py
index 0a8461a6..75f02edd 100644
--- a/test/test_calculateP/test_voronoiHistogram.py
+++ b/test/test_calculateP/test_voronoiHistogram.py
@@ -181,7 +181,7 @@ class center_and_layer1_points_binsize_list(center_and_layer1_points_binsize):
     r_size is a list.
     """
     def setUp(self):
-        self.r_size = self.sur_domain[:,1]*.02
+        self.r_size = self.sur_domain[:,1]*.2
         super(center_and_layer1_points_binsize_list, self).setUp()
 
 class center_and_layer1_points_binsize_double(center_and_layer1_points_binsize):
@@ -191,7 +191,7 @@ class center_and_layer1_points_binsize_double(center_and_layer1_points_binsize):
     r_size is a double.
     """
     def setUp(self):
-        self.r_size = self.sur_domain[0,1]*.02
+        self.r_size = self.sur_domain[0,1]*.2
         super(center_and_layer1_points_binsize_double, self).setUp()
 
 class test_calp_list_1D(domain_1D, center_and_layer1_points_list):
@@ -315,23 +315,272 @@ def setUp(self):
 # make sure int_l1 is correct
 # make sure int_l2 is correct
 class edges(object):
+    """
+    Provides a method to test that the dimensions of the output (and the output
+    itself) from methods with the pattern
+    ``bet.calculateP.voronoiHistogram.edges_*`` are correct.
+    """
     def test_dimensions(self):
-        pass
+        """
+        Compares the dimensions of the output from
+        ``bet.calculateP.voronoiHistogram.edges_*``.
+        """
+        compare_dim = list()
+        for edge, center_ppe in zip(self.rect_and_sur_edges,
+                self.center_pts_per_edge):
+            compare_dim.append(len(edge) == center_ppe+3)
+        assert np.all(compare_dim)
+        
+    def test_output(self):
+        """
+        Compares the output from ``bet.calcuateP.voronoiHistogram.edges_*``
+        with a known solution
+        """
+        compare_dim = list()
+        for edge, edgeVH in zip(self.my_edges, self.rect_and_sur_edges):
+            compare_dim.append(np.allclose(edge, edgeVH))
+        assert np.all(compare_dim)
 
-class edges_regular(edges, unittest.TestCase):
-    def test_l1(self):
-        pass
-    def test_l2(self):
-        pass
 
+class edges_regular(edges):
+    """
+    Test :meth:`bet.calculateP.voronoiHistogram.edges_regular`
+    """
+    def create_output(self):
+        sur_width = self.sur_domain[:,1]-self.sur_domain[:,0]
+        rect_width = self.r_ratio*sur_width
+        rect_domain = np.empty(self.sur_domain.shape)
+        rect_domain[:, 0] = self.center - .5*rect_width
+        rect_domain[:, 1] = self.center + .5*rect_width
+        print rect_domain
+        self.my_edges = list()
+        for dim in xrange(self.sur_domain.shape[0]):
+            int_l1 = np.linspace(rect_domain[dim, 0], rect_domain[dim, 1],
+                    self.center_pts_per_edge[dim]+1)
+            int_l2 = np.empty((int_l1.shape[0]+2,))
+            int_l2[1:-1] = int_l1
+            int_l2[0] = self.sur_domain[dim, 0]
+            int_l2[-1] = self.sur_domain[dim, 1]
+            self.my_edges.append(int_l2)
+    
+    def setUp(self):
+        self.create_output()
+        self.rect_and_sur_edges = vHist.edges_regular(self.center_pts_per_edge, self.center,
+                self.r_ratio, self.sur_domain)
 
 
-# points_from_edges
-# make sure dimensions are correct
-# make sure shape is correct
-# make sure points are correct
+class edges_regular_binsize(edges_regular):
+    """
+    Test :meth:`bet.calculateP.voronoiHistogram.edges_regular_binsize`
+    """
+    def setUp(self):
+        self.r_ratio = self.r_size/self.sur_domain[:,1]
+        super(edges_regular_binsize, self).create_output()
+        self.rect_and_sur_edges = vHist.edges_regular_binsize(self.center_pts_per_edge,
+                self.center, self.r_size, self.sur_domain)
+
+
+class edges_regular_double(edges_regular):
+    """
+    Provides set up for
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` when
+    r_ratio is a double.
+    """
+    def setUp(self):
+        self.r_ratio = 0.2
+        super(edges_regular_double, self).setUp()
+
+class edges_regular_list(edges_regular):
+    """
+    Provides set up for
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` when
+    r_ratio is a list.
+    """
+    def setUp(self):
+        self.r_ratio = 0.2*np.ones(self.mdim)
+        super(edges_regular_list, self).setUp()
+
+class edges_regular_binsize_list(edges_regular_binsize):
+    """
+    Provides set up for
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular_binsize` when
+    r_size is a list.
+    """
+    def setUp(self):
+        self.r_size = self.sur_domain[:,1]*.2
+        super(edges_regular_binsize_list, self).setUp()
+
+class edges_regular_binsize_double(edges_regular_binsize):
+    """
+    Provides set up for
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular_binsize` when
+    r_size is a double.
+    """
+    def setUp(self):
+        self.r_size = self.sur_domain[0,1]*.2
+        super(edges_regular_binsize_double, self).setUp()
+
+class test_er_list_1D(domain_1D, edges_regular_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 1D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_er_list_1D, self).createDomain()
+        super(test_er_list_1D, self).setUp()
+class test_er_list_2D(domain_2D, edges_regular_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 2D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_er_list_2D, self).createDomain()
+        super(test_er_list_2D, self).setUp()
+class test_er_list_3D(domain_3D, edges_regular_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 3D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_er_list_3D, self).createDomain()
+        super(test_er_list_3D, self).setUp()
+
+class test_er_double_1D(domain_1D, edges_regular_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 1D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_er_double_1D, self).createDomain()
+        super(test_er_double_1D, self).setUp()
+class test_er_double_2D(domain_2D, edges_regular_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 2D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_er_double_2D, self).createDomain()
+        super(test_er_double_2D, self).setUp()
+class test_er_double_3D(domain_3D, edges_regular_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 3D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_er_double_3D, self).createDomain()
+        super(test_er_double_3D, self).setUp()
 
 
+class test_ers_list_1D(domain_1D, edges_regular_binsize_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 1D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_ers_list_1D, self).createDomain()
+        super(test_ers_list_1D, self).setUp()
+class test_ers_list_2D(domain_2D, edges_regular_binsize_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 2D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_ers_list_2D, self).createDomain()
+        super(test_ers_list_2D, self).setUp()
+class test_ers_list_3D(domain_3D, edges_regular_binsize_list):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 3D
+    domain with r_ratio as a list.
+    """
+    def setUp(self):
+        super(test_ers_list_3D, self).createDomain()
+        super(test_ers_list_3D, self).setUp()
+
+class test_ers_double_1D(domain_1D, edges_regular_binsize_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 1D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_ers_double_1D, self).createDomain()
+        super(test_ers_double_1D, self).setUp()
+class test_ers_double_2D(domain_2D, edges_regular_binsize_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 2D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_ers_double_2D, self).createDomain()
+        super(test_ers_double_2D, self).setUp()
+class test_ers_double_3D(domain_3D, edges_regular_binsize_double):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 3D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_ers_double_3D, self).createDomain()
+        super(test_ers_double_3D, self).setUp()
+
+class edges_from_points(edges):
+    """
+    Test :meth:`bet.calculateP.edges_from_points`
+    """
+    def setUp(self):
+        points = list()
+        self.my_edges = list()
+        for dim in xrange(self.mdim):
+            points_dim = np.linspace(self.sur_domain[dim, 0],
+                self.sur_domain[dim, 1], 4)
+            points.append(points_dim)
+            self.my_edges.append((points_dim[1:]+points_dim[:-1])/2)
+        self.rect_and_sur_edges = vHist.edges_from_points(points)
+
+    def test_dimensions(self):
+        compare_dim = list()
+        for edge, my_edge in zip(self.rect_and_sur_edges,
+                self.my_edges):
+            compare_dim.append(len(edge) == len(my_edge))
+        assert np.all(compare_dim)
+ 
+class test_efp_1D(domain_1D, edges_from_points):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_from_points` for a 1D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_efp_1D, self).createDomain()
+        super(test_efp_1D, self).setUp()
+class test_efp_2D(domain_2D, edges_from_points):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_from_points` for a 2D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_efp_2D, self).createDomain()
+        super(test_efp_2D, self).setUp()
+class test_efp_3D(domain_3D, edges_from_points):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.edges_from_points` for a 3D
+    domain with r_ratio as a double.
+    """
+    def setUp(self):
+        super(test_efp_3D, self).createDomain()
+        super(test_efp_3D, self).setUp()
 
 # histogrammdd_volumes
 # make sure dimensions are correct

From 68414b0ef02e3045f1fbfb17070c2997e9bce229 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 15 Apr 2015 16:46:06 -0500
Subject: [PATCH 39/85] finsihed testing histogramdd_volume

---
 bet/calculateP/voronoiHistogram.py            |  11 +-
 test/test_calculateP/test_voronoiHistogram.py | 139 +++++++++++++++---
 2 files changed, 123 insertions(+), 27 deletions(-)

diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index dc026db4..e5980c9a 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -310,6 +310,7 @@ def histogramdd_volumes(edges, points):
     along each dimension and an 'ij' ordered sequence of points (1 per voronoi
     cell) returns a list of the volumes associated with these voronoic cells.
 
+    TODO fill this in
     :param edges: 
     :type edges:
     :param points: points used to define the voronoi tesselation (only the
@@ -327,17 +328,17 @@ def histogramdd_volumes(edges, points):
     for dim, e in enumerate(edges):
         if len(edges) == 1:
             if e[0] >= points_min:
-                e[0] = points_min-1
+                e[0] = points_min-np.finfo(float).eps
             if e[-1] <= points_max:
-                e[-1] = points_max+1
+                e[-1] = points_max+np.finfo(float).eps
         else:
             if e[0] >= points_min[dim]:
-                e[0] = points_min[dim]-1
+                e[0] = points_min[dim]-np.finfo(float).eps
             if e[-1] <= points_max[dim]:
-                e[-1] = points_max[dim]+1
+                e[-1] = points_max[dim]+np.finfo(float).eps
 
     H, _ = np.histogramdd(points, edges, normed=True)
-    volume = 1/(H*points.shape[0])
+    volume = 1.0/(H*points.shape[0]) # account for number of bins
     # works as long as points are created with 'ij' indexing in meshgrid
     volume = volume.ravel()
     return H, volume, edges
diff --git a/test/test_calculateP/test_voronoiHistogram.py b/test/test_calculateP/test_voronoiHistogram.py
index 75f02edd..4f299c23 100644
--- a/test/test_calculateP/test_voronoiHistogram.py
+++ b/test/test_calculateP/test_voronoiHistogram.py
@@ -57,13 +57,6 @@ def createDomain(self):
         self.mdim = 3
         self.center_pts_per_edge = [1,2,1]
 
-# center_and_layer1_points_binsize
-# center_and_layer1_points
-
-# make sure dimensions are correct
-# make sure the rect_domain is correct
-# make sure the bounding layer is correct 
-#class center_and_layer1_points(unittest.TestCase):
 class center_and_layer1_points(object):
     """
     Test :meth:`bet.calculateP.voronoiHistogram.center_and_layer1_points`
@@ -308,12 +301,6 @@ def setUp(self):
         super(test_calps_double_3D, self).setUp()
 
 
-# edges_regular_binsize
-# edges_regular
-# edges_from_points
-# make sure dimensions are correct
-# make sure int_l1 is correct
-# make sure int_l2 is correct
 class edges(object):
     """
     Provides a method to test that the dimensions of the output (and the output
@@ -352,7 +339,6 @@ def create_output(self):
         rect_domain = np.empty(self.sur_domain.shape)
         rect_domain[:, 0] = self.center - .5*rect_width
         rect_domain[:, 1] = self.center + .5*rect_width
-        print rect_domain
         self.my_edges = list()
         for dim in xrange(self.sur_domain.shape[0]):
             int_l1 = np.linspace(rect_domain[dim, 0], rect_domain[dim, 1],
@@ -538,6 +524,9 @@ class edges_from_points(edges):
     Test :meth:`bet.calculateP.edges_from_points`
     """
     def setUp(self):
+        """
+        Set up problem.
+        """
         points = list()
         self.my_edges = list()
         for dim in xrange(self.mdim):
@@ -548,6 +537,9 @@ def setUp(self):
         self.rect_and_sur_edges = vHist.edges_from_points(points)
 
     def test_dimensions(self):
+        """
+        Test dimensions of :meth:`bet.calculateP.edges_from_points`
+        """
         compare_dim = list()
         for edge, my_edge in zip(self.rect_and_sur_edges,
                 self.my_edges):
@@ -558,7 +550,7 @@ class test_efp_1D(domain_1D, edges_from_points):
     """
     Test
     :meth:`bet.calculateP.voronoiHistogram.edges_from_points` for a 1D
-    domain with r_ratio as a double.
+    domain.
     """
     def setUp(self):
         super(test_efp_1D, self).createDomain()
@@ -567,7 +559,7 @@ class test_efp_2D(domain_2D, edges_from_points):
     """
     Test
     :meth:`bet.calculateP.voronoiHistogram.edges_from_points` for a 2D
-    domain with r_ratio as a double.
+    domain.
     """
     def setUp(self):
         super(test_efp_2D, self).createDomain()
@@ -576,17 +568,120 @@ class test_efp_3D(domain_3D, edges_from_points):
     """
     Test
     :meth:`bet.calculateP.voronoiHistogram.edges_from_points` for a 3D
-    domain with r_ratio as a double.
+    domain.
     """
     def setUp(self):
         super(test_efp_3D, self).createDomain()
         super(test_efp_3D, self).setUp()
 
-# histogrammdd_volumes
-# make sure dimensions are correct
-# make sure H sums to 1
-# make sure volume sums to 1?
-# make sure volumes are nonnegative
+class histogramdd_volumes(object):
+    """
+    Test :meth:`bet.calculateP.voronoiHistogram.histogramdd_volumes`
+    """
+    def setUp(self):
+        points = list()
+        self.edges = list()
+        for dim in xrange(self.mdim):
+            points_dim = np.linspace(self.sur_domain[dim, 0],
+                self.sur_domain[dim, 1], 4)
+            points.append(points_dim[1:-1])
+            self.edges.append((points_dim[1:]+points_dim[:-1])/2.0)
+        self.points = util.meshgrid_ndim(points)
+        self.H, _ = np.histogramdd(self.points, self.edges, normed=True)
+        volume = 1.0/(self.H*(2.0**self.mdim))
+        self.volume = volume.ravel()
+        output = vHist.histogramdd_volumes(self.edges, self.points)
+        self.o_H, self.o_volume, self.o_edges = output
+
+    def test_dimensions_H(self):
+        """
+        Test the dimensions of H from
+        :meth:`bet.calculateP.histogramdd_volumes``
+        """
+        assert self.H.shape == self.o_H.shape
+
+    def test_dimensions_volume(self):
+        """
+        Test the dimensions of volume from
+        :meth:`bet.calculateP.histogramdd_volumes``
+        """
+        assert self.volume.shape == self.o_volume.shape
+
+    def test_dimensions_edges(self):
+        """
+        Test the dimensions of edges from
+        :meth:`bet.calculateP.histogramdd_volumes``
+        """
+        compare_dim = list()
+        for edge, my_edge in zip(self.o_edges, self.edges):
+            compare_dim.append(len(edge) == len(my_edge))
+        assert np.all(compare_dim)
+
+    def test_H_nonnegative(self):
+        """
+        Test that H from :meth:`bet.calculateP.histogramdd_volumes``
+        is nonnegative.
+        """
+        assert np.all(np.less(0.0, self.o_H))
+
+    def test_volumes_nonnegative(self):
+        """
+        Test that volume from :meth:`bet.calculateP.histogramdd_volumes``
+        is nonnegative.
+        """
+        assert np.all(np.less(0.0, self.o_volume))
+
+    def test_H(self):
+        """
+        Test that H from :meth:`bet.calculateP.histogramdd_volumes``
+        is correct.
+        """
+        assert np.allclose(self.H, self.o_H)
+
+    def test_volume(self):
+        """
+        Test that volume from :meth:`bet.calculateP.histogramdd_volumes``
+        is correct.
+        """
+        assert np.allclose(self.volume, self.o_volume)
+
+    def test_edges(self):
+        """
+        Test that the edges from :meth:`bet.calculateP.histogramdd_volumes``
+        are correct.
+        """
+        compare_dim = list()
+        for edge, my_edge in zip(self.o_edges, self.edges):
+            compare_dim.append(np.allclose(edge, my_edge))
+        assert np.all(compare_dim)
+
+class test_hddv_1D(domain_1D, histogramdd_volumes):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.histogramdd_volumes` for a 1D
+    domain.
+    """
+    def setUp(self):
+        super(test_hddv_1D, self).createDomain()
+        super(test_hddv_1D, self).setUp()
+class test_hddv_2D(domain_2D, histogramdd_volumes):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.histogramdd_volumes` for a 2D
+    domain.
+    """
+    def setUp(self):
+        super(test_hddv_2D, self).createDomain()
+        super(test_hddv_2D, self).setUp()
+class test_hddv_3D(domain_3D, histogramdd_volumes):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.histogramdd_volumes` for a 3D
+    domain.
+    """
+    def setUp(self):
+        super(test_hddv_3D, self).createDomain()
+        super(test_hddv_3D, self).setUp()
 
 # simple_fun_uniform
 # make sure dimensions are correct

From f54cc80c0f6b430197d23fe9ef3a08ba14cb841b Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 15 Apr 2015 17:06:27 -0500
Subject: [PATCH 40/85] added TODOs about ordering and mpi4py

---
 bet/calculateP/calculateP.py     | 3 +++
 bet/calculateP/simpleFunP.py     | 3 ++-
 bet/postProcess/plotP.py         | 2 ++
 bet/sampling/adaptiveSampling.py | 2 ++
 bet/sampling/basicSampling.py    | 2 ++
 5 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index b32e603a..c240c1e8 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -1,3 +1,6 @@
+# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
+# presuming it preserse order anywhere
+
 r"""
 This module provides methods for calulating the probability
 measure :math:`P_{\Lambda}`.
diff --git a/bet/calculateP/simpleFunP.py b/bet/calculateP/simpleFunP.py
index f00eab98..16a2f458 100644
--- a/bet/calculateP/simpleFunP.py
+++ b/bet/calculateP/simpleFunP.py
@@ -7,7 +7,8 @@
 import scipy.spatial as spatial
 import bet.calculateP.voronoiHistogram as vHist
 import collections
-
+# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
+# presuming it preserse order anywhere
 # TODO change bins_size to more informative name like bound_size?
 
 def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
diff --git a/bet/postProcess/plotP.py b/bet/postProcess/plotP.py
index 9209e937..07596bab 100644
--- a/bet/postProcess/plotP.py
+++ b/bet/postProcess/plotP.py
@@ -1,3 +1,5 @@
+# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
+# presuming it preserse order anywhere
 """
 This module provides methods for plotting probabilities. 
 """
diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 8568c93a..97fdb556 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -1,3 +1,5 @@
+# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
+# presuming it preserse order anywhere
 # -*- coding: utf-8 -*-
 # Lindley Graham 3/10/2014
 """
diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index 68de1687..5ca69280 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -1,3 +1,5 @@
+# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
+# presuming it preserse order anywhere
 # Lindley Graham 4/15/2014
 """
 This module contains functions for sampling. We assume we are given access to a

From 98215af0129281410efea5b9b9d1226b275432e7 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 15 Apr 2015 17:24:01 -0500
Subject: [PATCH 41/85] finished writing tests for voronoiHistogram

---
 test/__init__.py                              |  3 +-
 test/test_calculateP/__init__.py              |  5 +-
 test/test_calculateP/test_voronoiHistogram.py | 61 ++++++++++++++++---
 3 files changed, 57 insertions(+), 12 deletions(-)

diff --git a/test/__init__.py b/test/__init__.py
index 1ff8b48a..15bdfe52 100644
--- a/test/__init__.py
+++ b/test/__init__.py
@@ -2,5 +2,4 @@
 This package contains all of the tests for :program:`BET`. The package
 structure mirrors the ``bet`` package structure.
 """
-__all__ = ['test_calculateP', 'test_loadBalance', 'test_sampling',
-    'test_visualize']
+__all__ = ['test_calculateP', 'test_postProcess', 'test_sampling']
diff --git a/test/test_calculateP/__init__.py b/test/test_calculateP/__init__.py
index 19a745be..076ed6ae 100644
--- a/test/test_calculateP/__init__.py
+++ b/test/test_calculateP/__init__.py
@@ -1,4 +1,5 @@
 """
-This subpackage contains tests for the ``bet.calculateP`` subpackage.
+This package contains all of the tests for :program:`BET`. The package
+structure mirrors the ``bet`` package structure.
 """
-__all__ = ['test_calulcateP', 'test_simpleFunP']
+__all__ = ['test_voronoiHistogram', 'test_calculateP', 'test_simpleFunP']
diff --git a/test/test_calculateP/test_voronoiHistogram.py b/test/test_calculateP/test_voronoiHistogram.py
index 4f299c23..c5fb1b9c 100644
--- a/test/test_calculateP/test_voronoiHistogram.py
+++ b/test/test_calculateP/test_voronoiHistogram.py
@@ -14,6 +14,7 @@
 import bet.util as util
 import numpy as np
 import numpy.testing as nptest
+from test.test_calculateP.test_simpleFunP import prob_uniform
 
 # Do below for dimensions 01, 1, 2, and 3
 class domain_1D(object):
@@ -683,11 +684,55 @@ def setUp(self):
         super(test_hddv_3D, self).createDomain()
         super(test_hddv_3D, self).setUp()
 
-# simple_fun_uniform
-# make sure dimensions are correct
-# make sure rho_D_M sums to 1
-# make sure rho_D_M is nonnegative
-
-
-
-
+class simple_fun_uniform(prob_uniform):
+    """
+    Test :meth:'bet.calculateP.voronoiHistogram.simple_fun_uniform`
+    """
+    def setUp(self):
+        """
+        Set up the problem
+        """
+        points = list()
+        edges = list()
+        self.rect_domain = np.empty((self.mdim, 2))
+        for dim in xrange(self.mdim):
+            points_dim = np.linspace(self.sur_domain[dim, 0],
+                self.sur_domain[dim, 1], 4)
+            points.append(points_dim[1:-1])
+            edge = (points_dim[1:]+points_dim[:-1])/2.0
+            edges.append(edge)
+            self.rect_domain[dim, :] = edge[[0, -1]]
+        points = util.meshgrid_ndim(points)
+        H, _ = np.histogramdd(points, edges, normed=True)
+        volume = 1.0/(H*(2.0**self.mdim))
+        volumes = volume.ravel()
+        output = vHist.simple_fun_uniform(points, volumes, self.rect_domain)
+        self.rho_D_M, self.d_distr_samples, self.d_Tree = output
+
+class test_sfu_1D(domain_1D, simple_fun_uniform):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.simple_fun_uniform` for a 1D
+    domain.
+    """
+    def setUp(self):
+        super(test_sfu_1D, self).createDomain()
+        super(test_sfu_1D, self).setUp()
+class test_sfu_2D(domain_2D, simple_fun_uniform):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.simple_fun_uniform` for a 2D
+    domain.
+    """
+    def setUp(self):
+        super(test_sfu_2D, self).createDomain()
+        super(test_sfu_2D, self).setUp()
+class test_sfu_3D(domain_3D, simple_fun_uniform):
+    """
+    Test
+    :meth:`bet.calculateP.voronoiHistogram.simple_fun_uniform` for a 3D
+    domain.
+    """
+    def setUp(self):
+        super(test_sfu_3D, self).createDomain()
+        super(test_sfu_3D, self).setUp()

From bf2bbb2946a3bff32fd0d97a9f01c3d73d7a691d Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 15 Apr 2015 19:56:20 -0500
Subject: [PATCH 42/85] minor changes to Comm and util

---
 bet/Comm.py | 104 +++++++++++++++++++++++++++++++++++++++++++++-------
 bet/util.py |   7 +---
 2 files changed, 93 insertions(+), 18 deletions(-)

diff --git a/bet/Comm.py b/bet/Comm.py
index d8862d5f..949caf23 100644
--- a/bet/Comm.py
+++ b/bet/Comm.py
@@ -1,42 +1,120 @@
 """
-This module provides a workaround for people without mpi4py installed 
+This module provides a workaround for people without mpi4py installed
 to run BET.
 """
 
-class comm_for_no_mpi4py:
+
+class comm_for_no_mpi4py(object):
+
+    """
+    Provides a fake MPI.COMM_WORLD implementation so that the user need not
+    install mpi4py.
+    """
+
     def __init__(self):
+        """
+        Initialization
+        """
         pass
+
     def Get_size(self):
+        """
+        :rtype: int
+        :returns: 1
+        """
         return 1
+
     def Get_rank(self):
-        return 0 
-    def allgather(self,val):
+        """
+        :rtype: int
+        :returns 0
+        """
+        return 0
+
+    def allgather(self, val):
+        """
+        :param object val: object to allgather
+        :rtype: object
+        :returns: val
+        """
         return val
-    def allreduce(self,val1, op=None):
+
+    def allreduce(self, val1, op=None):
+        """
+        :param object val1: object to allreduce
+        :rtype: object
+        :returns: val1
+        """
         return val1
-    def bcast(self,val, root=0):
-        return val   
-    def Allgather(self,val):
+
+    def bcast(self, val, root=0):
+        """
+        :param object val: object to broadcast
+        :param int root: 0
+        :rtype: object
+        :returns: val
+        """
+        return val
+
+    def Allgather(self, val):
+        """
+        :param object val: object to Allgather
+        :rtype: object
+        :returns: val
+        """
         return val
-    def Allreduce(self,val1, val2, op=None):
+
+    def Allreduce(self, val1, val2, op=None):
+        """
+        :param object val1: object to Allreduce
+        :param object val2: object to Allreduce
+        :param op: None
+        :rtype: object
+        :returns: val1
+        """
         return val1
-    def Bcast(self,val, root=0):
+
+    def Bcast(self, val, root=0):
+        """
+        :param object val: object to gather
+        :param int root: 0
+        :rtype: object
+        :returns: val
+        """
         return val
+
     def Scatter(self, val1, val2, root=0):
+        """
+        :param object val1: object to Scatter
+        :param object val2: object to Scatter
+        :param int root: 0
+        :rtype: object
+        :returns: val1
+        """
         return val1
 
-class MPI_for_no_mpi4py:
+
+class MPI_for_no_mpi4py(object):
+
+    """
+    Provides a fake MPI implementation so that the user need not install
+    mpi4py.
+    """
+
     def __init__(self):
+        """
+        Initialization
+        """
         self.SUM = None
         self.DOUBLE = float
         self.INT = int
-        
+
 try:
     from mpi4py import MPI
     comm = MPI.COMM_WORLD
 except ImportError:
     MPI = MPI_for_no_mpi4py()
     comm = comm_for_no_mpi4py()
-    
+
 size = comm.Get_size()
 rank = comm.Get_rank()
diff --git a/bet/util.py b/bet/util.py
index 4be3b105..7d598f97 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -1,10 +1,9 @@
-import numpy as np
-from bet.Comm import *
-
 """
 The module contains general tools for BET.
 """
 
+import numpy as np
+from bet.Comm import *
 
 def meshgrid_ndim(X):
     """
@@ -40,7 +39,6 @@ def meshgrid_ndim(X):
                                                alist[9],
                                                indexing='ij')
 
-    # TODO: use x.flat instead of x.flat[:]
     X_new = np.vstack(
         (a.flat[:],
          b.flat[:],
@@ -52,7 +50,6 @@ def meshgrid_ndim(X):
          h.flat[:],
          i.flat[:],
          j.flat[:])).transpose()
-    # TODO: I don't think this line does anything, remove it
     X_new = X_new[:, 0:n]
 
     return X_new

From 277532d65b448027be5608d05e653a71a541531d Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 15 Apr 2015 19:58:15 -0500
Subject: [PATCH 43/85] minor change to util

---
 bet/util.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/bet/util.py b/bet/util.py
index 7d598f97..1f660fa8 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -78,6 +78,13 @@ def fix_dimensions_vector(vector):
     TODO: write and document me
     """
     pass
+
+def fix_dimensions_vector_array(vector):
+    """
+    TODO: write and document me
+    """
+    pass
+
 def fix_dimensions_domain(domain):
     """
     TODO: write and document me

From 21c6a378a3507614cde2b02df123326ec25620aa Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 16 Apr 2015 16:23:28 -0500
Subject: [PATCH 44/85] fixed and tested TODOs in Comm and util

---
 bet/Comm.py       |   1 +
 bet/util.py       | 127 +++++++++++++++++++++++++++++++++++++++-------
 test/test_util.py |  76 ++++++++++++++++++++++++---
 3 files changed, 179 insertions(+), 25 deletions(-)

diff --git a/bet/Comm.py b/bet/Comm.py
index 949caf23..e7c69c04 100644
--- a/bet/Comm.py
+++ b/bet/Comm.py
@@ -108,6 +108,7 @@ def __init__(self):
         self.SUM = None
         self.DOUBLE = float
         self.INT = int
+        self.BOOL = bool
 
 try:
     from mpi4py import MPI
diff --git a/bet/util.py b/bet/util.py
index 1f660fa8..4cd9d188 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -4,6 +4,9 @@
 
 import numpy as np
 from bet.Comm import *
+import collections
+
+possible_types = {int:MPI.INT, float:MPI.DOUBLE}
 
 def meshgrid_ndim(X):
     """
@@ -54,11 +57,8 @@ def meshgrid_ndim(X):
 
     return X_new
 
-def get_global_values(array):
+def get_global_values(array, shape=None):
     """
-    TODO: write a version that also works with captial mpi4py method calls
-    (optional?)
-
     Concatenates local arrays into global array using :meth:`np.vstack`.
 
     :param array: Array.
@@ -66,34 +66,123 @@ def get_global_values(array):
     :rtype: :class:'~numpy.ndarray'
     :returns: array
     """
-    a_shape = len(array.shape)
-    array = comm.allgather(array, array)
-    if a_shape == 1:
-        return np.hstack(array)
+
+    # Figure out the subtype of the elements of the array
+    dtype = array.dtype
+    mpi_dtype = False
+    print dtype
+    for ptype in possible_types.iterkeys():
+        if np.issubdtype(dtype, ptype):
+            mpi_dtype = True
+            dtype = ptype
+
+    if shape == None or not mpi_dtype:
+        # do a lowercase allgather
+        a_shape = len(array.shape)
+        array = comm.allgather(array, array)
+        if a_shape == 1:
+            return np.hstack(array)
+        else:
+            return np.vstack(array)
     else:
-        return np.vstack(array)
+        # do an uppercase Allgather
+        whole_a = np.empty(shape, dtype=dtype)
+        print dtype
+        comm.Allgather([array, possible_types[dtype]], [whole_a,
+            possible_types[dtype]])
+        return array
 
 def fix_dimensions_vector(vector):
     """
-    TODO: write and document me
+    Fix the dimensions of an input so that it is a :class:`numpy.ndarray` of
+    shape (N,).
+
+    :param vector: numerical object
+    :rtype: :class:`numpy.ndarray`
+    :returns: array of shape (N,)
+
     """
-    pass
+    if not isinstance(vector, collections.Iterable):
+        vector = np.array([vector])
+    elif not isinstance(vector, np.ndarray):
+        vector = np.array(vector)
+    return vector
 
-def fix_dimensions_vector_array(vector):
+def fix_dimensions_vector_2darray(vector):
     """
-    TODO: write and document me
+    Fix the dimensions of an input so that it is a :class:`numpy.ndarray` of
+    shape (N,1).
+
+    :param vector: numerical object
+    :rtype: :class:`numpy.ndarray`
+    :returns: array of shape (N,1)
+
     """
-    pass
+    vector = fix_dimensions_vector(vector)
+    if len(vector.shape) == 1:
+        vector = np.expand_dims(vector, axis=1)
+    return vector
 
 def fix_dimensions_domain(domain):
     """
-    TODO: write and document me
+    Fix the dimensions of an input so that it is a :class:`numpy.ndarray` of
+    shape (dim, 2).
+
+    :param vector: numerical object of at least length 2
+    :type vector: :class:`collections.Iterable`
+    :rtype: :class:`numpy.ndarray`
+    :retuns: array of shape (dim, 2)
+
     """
-    pass
-def fix_dimensions_data(data):
+    if not isinstance(domain, np.ndarray):
+        if len(domain) == 2:
+            print domain
+            domain = np.expand_dims(domain, axis=0)
+            print domain
+            print domain.shape
+        else:
+            raise TypeError("The length must be at least 2.")
+    elif len(domain.shape) == 1 and domain.shape[0] == 2:
+        domain = np.expand_dims(domain, axis=0)
+    elif len(domain.shape) == 2 and domain.shape[1] == 2:
+        pass # The shape is already correct!
+    elif len(domain.shape) == 2 and domain.shape[0] == 2:
+        domain = domain.transpose()
+    else:
+        raise TypeError("At least one dimension must have a length of 2.")
+    return domain
+
+def fix_dimensions_data(data, dim=None):
     """
-    TODO: write and document me
+    Fix the dimensions of an input so that it is a :class:`numpy.ndarray` of
+    shape (N, dim). 
+    
+    If ``dim`` is non-specified:
+    If ``data`` is a non-iterable number assumes that ``dim==1``.
+    If ``data`` is a numpy array with len(shape) == 1 assumes that ``dim==1``.
+    If ``data`` is a numpy array with len(shape) == 2 assumes that
+    ``dim==shape[1]``.
+
+
+    :param data: numerical object
+    :param int dim: The dimension of the "data" space.
+    :rtype: :class:`numpy.ndarray`
+    :returns: array of shape (N, dim)
+    
     """
-    pass
+    if dim == None:
+        if not isinstance(data, np.ndarray):
+            return fix_dimensions_vector_2darray(data)
+        elif len(data.shape) == 1:
+            return fix_dimensions_vector_2darray(data)
+        else:
+            return data
+
+    data = fix_dimensions_vector_2darray(data)
+    if data.shape[1] != dim:
+        return data.transpose()
+    else:
+        return data
+
 
 
diff --git a/test/test_util.py b/test/test_util.py
index 67d2b656..c298314b 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -46,14 +46,13 @@ def test_meshgrid_ndim():
 
 def test_get_global_values():
     """
-    TODO: make sure the newer version of util matches this test
-
     Tests :meth:`bet.util.get_global_values`.
     """
-    for i in xrange(5):
-        yield compare_get_global_values, i
+    for provide_shape in [True, False]:
+        for i in xrange(5):
+            yield compare_get_global_values, i, provide_shape
 
-def compare_get_global_values(i):
+def compare_get_global_values(i, provide_shape):
     """
     Compares the results of get global values for a vector of shape ``(size*2,
     i)``.
@@ -75,6 +74,71 @@ def compare_get_global_values(i):
         my_array = original_array[my_index]
     else:
         my_array = original_array[my_index, :]
-    recomposed_array = util.get_global_values(my_array)
+    if provide_shape:
+        recomposed_array = util.get_global_values(my_array, original_array.shape)
+    else:
+        recomposed_array = util.get_global_values(my_array)
     nptest.assert_array_equal(original_array, recomposed_array)
 
+
+def test_fix_dimensions_vector():
+    """
+    Tests :meth:`bet.util.fix_dimensions_vector`
+    """
+    values = [1, [1], range(5), np.array(range(5))]
+    shapes = [(1,), (1,), (5,), (5,)]
+    for value, shape in zip(values, shapes):
+        vector = util.fix_dimensions_vector(value)
+        assert vector.shape == shape
+
+def test_fix_dimensions_vector_2darray():
+    """
+    Tests :meth:`bet.util.fix_dimensions_vector_2darray`
+    """
+    values = [1, [1], np.empty((1,1)), range(5), np.array(range(5)),
+            np.empty((5,1))]
+    shapes = [(1,1), (1,1), (1,1), (5,1), (5,1), (5,1)]
+    for value, shape in zip(values, shapes):
+        vector = util.fix_dimensions_vector_2darray(value)
+        assert vector.shape == shape
+
+def test_fix_dimensions_domain():
+    """
+    Tests :meth:`bet.util.fix_dimensions_domain`
+    """
+    values = [range(2), np.empty((2,)), np.empty((2,1)), np.empty((1,2)),
+            np.empty((5,2)), np.empty((2,5))]
+    shapes = [(1,2), (1,2), (1,2), (1,2), (5,2), (5,2)]
+    for value, shape in zip(values, shapes):
+        vector = util.fix_dimensions_domain(value)
+        assert vector.shape == shape
+
+def test_fix_dimensions_data_nodim():
+    """
+    Tests :meth`bet.util.fix_dimensions_domain` when `dim` is not specified
+    """
+    values = [1, [1], range(2), np.empty((2,)), np.empty((2,1)), np.empty((1,2)),
+            np.empty((5,2)), np.empty((2,5))]
+    shapes = [(1,1), (1,1), (2,1), (2,1), (2,1), (1,2), (5,2), (2,5)]
+    print len(values), len(shapes)
+    for value, shape in zip(values, shapes):
+        vector = util.fix_dimensions_data(value)
+        print vector, value
+        print vector.shape, shape
+        assert vector.shape == shape
+
+def test_fix_dimensions_data_dim():
+    """
+    Tests :meth`bet.util.fix_dimensions_domain` when `dim` is specified
+    """
+    values = [1, [1], range(2), np.empty((2,)), np.empty((2,1)), np.empty((1,2)),
+            np.empty((5,2)), np.empty((2,5)), np.empty((5,2)), np.empty((2,5))]
+    shapes = [(1,1), (1,1), (1,2), (1,2), (1,2), (1,2), (5,2), (5,2), (2,5),
+            (2,5)]
+    dims = [1, 1, 2, 2, 2, 2, 2, 2, 5, 5] 
+    for value, shape, dim in zip(values, shapes, dims):
+        vector = util.fix_dimensions_data(value, dim)
+        print vector, value
+        print vector.shape, shape, dim
+        assert vector.shape == shape
+

From 4e76a15e1f89d932eae13a36df21d675ae31cc55 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 16 Apr 2015 18:32:52 -0500
Subject: [PATCH 45/85] worked on TODOs in simpleFunP and voronoiHistogram,
 updated a test

---
 bet/calculateP/simpleFunP.py            | 159 +++++-------------------
 bet/calculateP/voronoiHistogram.py      | 152 +++++-----------------
 test/test_calculateP/test_simpleFunP.py |  25 ++++
 3 files changed, 86 insertions(+), 250 deletions(-)

diff --git a/bet/calculateP/simpleFunP.py b/bet/calculateP/simpleFunP.py
index 16a2f458..e411f3fb 100644
--- a/bet/calculateP/simpleFunP.py
+++ b/bet/calculateP/simpleFunP.py
@@ -7,14 +7,12 @@
 import scipy.spatial as spatial
 import bet.calculateP.voronoiHistogram as vHist
 import collections
-# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
-# presuming it preserse order anywhere
-# TODO change bins_size to more informative name like bound_size?
+import bet.util as util
+
+# TODO change bin_size to more informative name like bound_size?
 
 def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     r"""
-    TODO: THIS OCCASIONALLY FAILS test.test_calculateP.test_domain FIX ME
-
     Creates a simple function approximation of :math:`\rho_{\mathcal{D}}`
     where :math:`\rho_{\mathcal{D}}` is a uniform probability density on
     a generalized rectangle centered at Q_ref.
@@ -53,12 +51,8 @@ def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree` is
     the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
-    if len(data.shape) == 1:
-        data = np.expand_dims(data, axis=1)   
-    # Determine the appropriate bin size for this QoI
-    data_max = np.max(data, 0)
-    data_min = np.min(data, 0)
-    bin_size = (data_max-data_min)*bin_ratio
+    data = util.fix_dimensions_data(data)
+    bin_size = (np.max(data, 0) - np.min(data,0))*bin_ratio
 
     r'''
     Create M samples defining M Voronoi cells (i.e., "bins") in D used to 
@@ -123,53 +117,11 @@ def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     can then be stored and accessed later by the algorithm using a completely
     different set of parameter samples and model solves.
     '''
+    # TODO check that the parallel implementation of this is correct 
+    # TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
+    # presuming it preserse order anywhere
     return (rho_D_M, d_distr_samples, d_Tree)
 
-#TODO Empty function hist_regular 
-def hist_regular(data, distr_samples, nbins):
-    """
-    create nbins regulary spaced bins
-    check to make sure  each bin has about 1 data sample per bin, if not
-    recompute bins
-    (hist, edges) = histdd(distr_samples, bins)
-    http://docs.scipy.org/doc/numpy/reference/generated/numpy.histogramdd.html#numpy.histogramdd
-    determine d_distr_samples from edges
-    """
-    pass
-
-# TODO Empty function hist_gaussian
-def hist_gaussian(data, distr_samples, nbins):
-    """
-    determine mean, standard deviation of distr_samples
-    partition D into nbins of equal probability for N(mean, sigma)
-    check to make sure  each bin has about 1 data sample per bin, if not
-    recompute bins
-    (hist, edges) = histdd(distr_samples, bins)
-    determine d_distr_samples from edges
-    """
-    pass
-
-# TODO Empty Function hist_unif
-def hist_unif(data, distr_samples, nbins):
-    """
-    same as hist_regular bit with uniformly spaced bins
-    unif_unif can and should call this function
-    """
-    pass
-
-# TODO Empty function gaussian_regular
-def gaussian_regular(data, Q_ref, std, nbins, num_d_emulate=1E6):
-    pass
-    #return (d_distr_prob, d_distr_samples, d_Tree)
-
-# TODO Comment or remove multivariate_gaussian
-def multivariate_gaussian(x, mean, std):
-    dim = len(mean)
-    detDiagCovMatrix = np.sqrt(np.prod(np.diag(std(std))))
-    frac = (2.0*np.pi)**(-dim/2.0)  * (1.0/detDiagCovMatrix)
-    fprime = x-mean
-    return frac*np.exp(-0.5*np.dot(fprime, 1.0/np.diag(std*std)))
-
 def normal_normal(Q_ref, M, std, num_d_emulate=1E6):
     r"""
     Creates a simple function approximation of :math:`\rho_{\mathcal{D},M}`
@@ -326,11 +278,6 @@ def unif_normal(Q_ref, M, std, num_d_emulate=1E6):
     # solving the model EVER! This can be done "offline" so to speak.
     return (rho_D_M, d_distr_samples, d_Tree)
 
-# TODO gaussian_unif is a blank function
-def gaussian_unif(data, Q_ref, std, nbins, num_d_emulate=1E6):
-    pass
-    #return (d_distr_prob, d_distr_samples, d_Tree)
-
 def uniform_hyperrectangle_user(data, domain, center_pts_per_edge=1):
     r"""
     Creates a simple funciton appoximation of :math:`\rho_{\mathcal{D},M}`
@@ -355,38 +302,13 @@ def uniform_hyperrectangle_user(data, domain, center_pts_per_edge=1):
         ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
-    # TODO following code could probably be reduced to a few lines
-    # determine the center of the domain
-    if len(domain.shape) == 1:
-        domain = np.expand_dims(domain, axis=1)
-    # TODO make sure the shape of the domain is correct, if not transpose it
+    # make sure the shape of the data and the domain are correct
+    data = util.fix_dimensions_data(data)
+    domain = util.fix_dimensions_data(domain, data.shape[1])
     domain_center = np.mean(domain, 0)
-    domain_min = np.min(domain, 0)
-    domain_max = np.max(domain, 0)
-    domain_lengths = domain_max - domain_min
+    domain_lengths = np.max(domain, 0) - np.min(domain, 0)
  
-    # determine the ratio of the lengths of the domain to the lengths of the
-    # hyperrectangle containing the data
-    if len(data.shape) == 1:
-        data = np.expand_dims(data, axis=1)
-    data_max = np.max(data, 0)
-    data_min = np.min(data, 0)
-    data_lengths = data_max - data_min
-    bin_ratios = domain_lengths/data_lengths  
-
-    # TODO: Check for inputted center_pts_per_edge in case given as list
-    # or as numpy array to see if dimensions match data space dimensions and
-    # that positive integer values are being used. Also, create this change
-    # elsewhere since center_pts_per_edge is only a scalar if dim(D)=1.
-    if not isinstance(center_pts_per_edge, collections.Iterable):
-        center_pts_per_edge = np.ones((data.shape[1],)) * center_pts_per_edge
-    else:
-        if not len(center_pts_per_edge) == data.shape[1]:
-            center_pts_per_edge = np.ones((data.shape[1],))
-            print 'Warning: center_pts_per_edge dimension mismatch.'
-            print 'Using 1 in each dimension.'
-
-    return uniform_hyperrectangle(data, domain_center, bin_ratios,
+    return uniform_hyperrectangle_binsize(data, domain_center, domain_lengths,
             center_pts_per_edge)
 
 def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1):
@@ -418,15 +340,8 @@ def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1)
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
     """
-    if len(data.shape) == 1:
-        data = np.expand_dims(data, axis=1)
-    data_max = np.max(data, 0)
-    data_min = np.min(data, 0)
-
-    # TODO: Check for inputted center_pts_per_edge in case given as list
-    # or as numpy array to see if dimensions match data space dimensions and
-    # that positive integer values are being used. Also, create this change
-    # elsewhere since center_pts_per_edge is only a scalar if dim(D)=1.
+    data = util.fix_dimensions_data(data)
+
     if not isinstance(center_pts_per_edge, collections.Iterable):
         center_pts_per_edge = np.ones((data.shape[1],)) * center_pts_per_edge
     else:
@@ -434,13 +349,15 @@ def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1)
             center_pts_per_edge = np.ones((data.shape[1],))
             print 'Warning: center_pts_per_edge dimension mismatch.'
             print 'Using 1 in each dimension.'
-    # TODO: see note above
+    if np.any(np.less(center_pts_per_edge, 0)):
+            print 'Warning: center_pts_per_edge must be greater than 0'
     if not isinstance(bin_size, collections.Iterable):
         bin_size = bin_size*np.ones((data.shape[1],))
+    if np.any(np.less(bin_size, 0)):
+            print 'Warning: center_pts_per_edge must be greater than 0'
+
+    sur_domain = np.array([np.min(data, 0),np.max(data, 0)]).transpose()
 
-    sur_domain = np.zeros((data.shape[1], 2))
-    sur_domain[:, 0] = data_min
-    sur_domain[:, 1] = data_max
     points, _, rect_domain = vHist.center_and_layer1_points_binsize(center_pts_per_edge, 
             Q_ref, bin_size, sur_domain)
     edges = vHist.edges_regular_binsize(center_pts_per_edge, Q_ref, bin_size,
@@ -477,34 +394,15 @@ def uniform_hyperrectangle(data, Q_ref, bin_ratio, center_pts_per_edge=1):
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
     """
-    if len(data.shape) == 1:
-        data = np.expand_dims(data, axis=1)
-    data_max = np.max(data, 0)
-    data_min = np.min(data, 0)
-
-    # TODO: Check for inputted center_pts_per_edge in case given as list
-    # or as numpy array to see if dimensions match data space dimensions and
-    # that positive integer values are being used. Also, create this change
-    # elsewhere since center_pts_per_edge is only a scalar if dim(D)=1.
-    if not isinstance(center_pts_per_edge, collections.Iterable):
-        center_pts_per_edge = np.ones((data.shape[1])) * center_pts_per_edge
-    else:
-        if not len(center_pts_per_edge) == data.shape[1]:
-            center_pts_per_edge = np.ones((data.shape[1], ))
-            print 'Warning: center_pts_per_edge dimension mismatch.'
-            print 'Using 1 in each dimension.'
+    data = util.fix_dimensions_data(data)
+
     if not isinstance(bin_ratio, collections.Iterable):
         bin_ratio = bin_ratio*np.ones((data.shape[1], ))
 
-    sur_domain = np.zeros((data.shape[1], 2))
-    sur_domain[:, 0] = data_min
-    sur_domain[:, 1] = data_max
-    points, _, rect_domain = vHist.center_and_layer1_points(center_pts_per_edge, 
-            Q_ref, bin_ratio, sur_domain)
-    edges = vHist.edges_regular(center_pts_per_edge, Q_ref, bin_ratio,
-            sur_domain) 
-    _, volumes, _ = vHist.histogramdd_volumes(edges, points)
-    return vHist.simple_fun_uniform(points, volumes, rect_domain)
+    sur_domain = np.array([np.min(data, 0),np.max(data, 0)]).transpose()
+    bin_size = (np.max(data, 0) - np.min(data, 0))*bin_ratio 
+    return uniform_hyperrectangle_binsize(data, Q_ref, bin_size,
+            center_pts_per_edge)
 
 def uniform_data(data):
     r"""
@@ -526,8 +424,7 @@ def uniform_data(data):
         ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
         is the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
-    if len(data.shape) == 1:
-        data = np.expand_dims(data, axis=1)
+    data = util.fix_dimensions_data(data)
 
     d_distr_prob = np.ones((data.shape[0],), dtype=np.float)/data.shape[0]
     d_Tree = spatial.KDTree(data)
diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index e5980c9a..10771355 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -3,9 +3,6 @@
 from scipy import spatial
 import bet.util as util
 
-# TODO: update documentation to make sure that the retuns and rtype are corrct
-# for each of the methods
-
 def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_domain):
     """
     Generates a regular grid of center points that define the voronoi
@@ -36,21 +33,15 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
         shape (mdim, 2)
 
     """
-    if np.all(np.greater(r_size, 1)):
+    # determine the hyperrectangle defined by center and r_size
+    rect_width = r_size*np.ones(sur_domain[:,0].shape)
+    rect_domain = np.column_stack([center - .5*rect_width,
+        center + .5*rect_width])
+    if np.all(np.greater(r_size, rect_width)):
         msg = "The hyperrectangle defined by this size is larger than the"
         msg += "original domain."
         print msg
     
-    #TODO This can be done in fewer lines
-    # determine the hyperrectangle defined by center and r_size
-    rect_width = r_size*np.ones(sur_domain[:,0].shape)
-    rect_domain = np.empty(sur_domain.shape)
-    rect_domain[:, 0] = center - .5*rect_width
-    rect_domain[:, 1] = center + .5*rect_width
-
-    if not isinstance(center_pts_per_edge, np.ndarray):
-        center_pts_per_edge = np.array(center_pts_per_edge)
-
     # determine the locations of the points for the 1st bounding layer
     layer1_left = rect_domain[:, 0]-rect_width/(2*center_pts_per_edge)
     layer1_right = rect_domain[:, 1]+rect_width/(2*center_pts_per_edge)
@@ -66,8 +57,6 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
     points = util.meshgrid_ndim(interior_and_layer1)
     return (points, interior_and_layer1, rect_domain)
 
-# TODO This and the previous function are almost identical. Maybe calculate r_size
-# from r_ratio and call the previous function.
 def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
     r"""
     Generates a regular grid of center points that define the voronoi
@@ -103,29 +92,11 @@ def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
         msg += "original domain."
         print msg
 
-    # determine the width of the surrounding domain
-    sur_width = sur_domain[:, 1]-sur_domain[:, 0]
-    # determine the hyperrectangle defined by center and r_ratio
-    rect_width = r_ratio*sur_width
-    rect_domain = np.empty(sur_domain.shape)
-    rect_domain[:, 0] = center - .5*rect_width
-    rect_domain[:, 1] = center + .5*rect_width
-
-    # determine the locations of the points for the 1st bounding layer
-    layer1_left = rect_domain[:, 0]-rect_width/(2*center_pts_per_edge)
-    layer1_right = rect_domain[:, 1]+rect_width/(2*center_pts_per_edge)
-
-    interior_and_layer1 = list()
-    for dim in xrange(sur_domain.shape[0]):
-        # create interior points and 1st layer
-        int_l1 = np.linspace(layer1_left[dim],
-            layer1_right[dim], center_pts_per_edge[dim]+2)
-        interior_and_layer1.append(int_l1)
-
-    # use meshgrid to make the hyperrectangle shells
-    points = util.meshgrid_ndim(interior_and_layer1)
-    return (points, interior_and_layer1, rect_domain)
+    # determine r_size from the width of the surrounding domain
+    r_size = r_ratio*(sur_domain[:, 1]-sur_domain[:, 0])
 
+    return center_and_layer1_points_binsize(center_pts_per_edge, center,
+            r_size, sur_domain)
 
 def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
     """
@@ -153,19 +124,14 @@ def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
 
     :rtype: tuple
-    :returns: (points, interior_and_layer1, interior_and_doublelayer) where
-        where points is an :class:`numpy.ndarray` of shape (num_points, dim),
-        interior_and_layer1 and interior_and_layer2 are lists of dim
-        :class:`numpy.ndarray`s of shape (center_pts_per_edge+2,) and
-        (center_pts_per_edge+3,) respectively.
-
+    :returns: interior_and_layer1 is a list of dim
+        :class:`numpy.ndarray`s of shape (center_pts_per_edge+2,)
     """
     # TODO This is redoing stuff that was done when generating the points
     # determine the hyperrectangle defined by center and r_size
     rect_width = r_size*np.ones(sur_domain[:,0].shape)
-    rect_domain = np.empty(sur_domain.shape)
-    rect_domain[:, 0] = center - .5*rect_width
-    rect_domain[:, 1] = center + .5*rect_width
+    rect_domain = np.column_stack([center - .5*rect_width,
+        center + .5*rect_width])
 
     if np.any(np.greater_equal(sur_domain[:, 0], rect_domain[:, 0])):
         msg = "The hyperrectangle defined by this size is larger than the"
@@ -191,8 +157,7 @@ def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
         rect_and_sur_edges.append(int_l2) 
 
     return rect_and_sur_edges
-# TODO This and the previous function are almost identical. Maybe calculate r_size
-# from r_ratio (or vice versa)  and call the previous function.
+
 def edges_regular(center_pts_per_edge, center, r_ratio, sur_domain):
     """
     Generates a sequence of arrays describing the edges of the finite voronoi
@@ -217,44 +182,20 @@ def edges_regular(center_pts_per_edge, center, r_ratio, sur_domain):
     :param sur_domain: minima and maxima of each dimension defining the
         surrounding domain
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
-    TODO fix return documentation
     :rtype: tuple
-    TODO FIX description of the return to match what is acctually returned
-    :returns: (points, interior_and_layer1, interior_and_doublelayer) where
-        where points is an :class:`numpy.ndarray` of shape (num_points, dim),
-        interior_and_layer1 and interior_and_layer2 are lists of dim
-        :class:`numpy.ndarray`s of shape (center_pts_per_edge+2,) and
-        (center_pts_per_edge+3,) respectively.
+    :returns: interior_and_layer1 is a list of dim
+        :class:`numpy.ndarray`s of shape (center_pts_per_edge+2,)
 
     """
     if np.any(np.greater(r_ratio, 1)):
         msg = "The hyperrectangle defined by this ratio is larger than the"
         msg += "original domain. RATIO: {}".format(r_ratio)
         print msg
-
-    # determine the width of the surrounding domain
-    sur_width = sur_domain[:, 1]-sur_domain[:, 0]
-    # determine the hyperrectangle defined by center and r_ratio
-    rect_width = r_ratio*sur_width
-    rect_domain = np.empty(sur_domain.shape)
-    rect_domain[:, 0] = center - .5*rect_width
-    rect_domain[:, 1] = center + .5*rect_width
     
-    rect_edges = list()
-    rect_and_sur_edges = list()
-    for dim in xrange(sur_domain.shape[0]):
-        # create interior points and 1st layer
-        int_l1 = np.linspace(rect_domain[dim, 0],
-            rect_domain[dim, 1], center_pts_per_edge[dim]+1)
-        rect_edges.append(int_l1)
-        # add layers together using indexing fu
-        int_l2 = np.zeros((int_l1.shape[0]+2,))
-        int_l2[1:-1] = int_l1
-        int_l2[0] = sur_domain[dim, 0]
-        int_l2[-1] = sur_domain[dim, 1]
-        rect_and_sur_edges.append(int_l2)
+    # determine r_size from the width of the surrounding domain
+    r_size = r_ratio*(sur_domain[:, 1]-sur_domain[:, 0])
 
-    return rect_and_sur_edges
+    return edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain)
 
 def edges_from_points(points):
     """
@@ -277,54 +218,30 @@ def edges_from_points(points):
         edges.append((points_dim[1:]+points_dim[:-1])/2)
     return edges
 
-def points_from_edges(edges):
-    """
-    Given a sequence of arrays describing the edges of bins formed by voronoi
-    cells along each dimensions returns the voronoi points that would generate
-    these cells.
-
-    ..todo:: This method only creates points in the center of the bins. Needs
-        Needs to be adjusted so that it creates points in the voronoi cell
-        locations. 
-
-    :param edges: A sequence of arrays describing the edges of bins along each
-        dimension.
-    :type edges: A list() containing mdim :class:`numpy.ndarray`s of shape
-        (nbins_per_dim+1,)
-
-    :returns: points, the coordindates of voronoi points that would generate
-        these bins
-    :rtype: :class:`numpy.ndarray` of shape (num_points, dim) where num_points
-        = product(nbins_per_dim)
-
-    """
-    #TODO: Implement me.
-    # create a point inside each of the bins defined by the edges
-    centers = list()
-    for e in edges:
-        centers.append((e[1:]+e[:-1])/2)
-
 def histogramdd_volumes(edges, points):
     """
     Given a sequence of arrays describing the edges of voronoi cells (bins)
     along each dimension and an 'ij' ordered sequence of points (1 per voronoi
     cell) returns a list of the volumes associated with these voronoic cells.
 
-    TODO fill this in
-    :param edges: 
-    :type edges:
+    :param edges: A sequence of arrays describing the edges of bins along
+        each dimension.
+    :type edges: A list() containing mdim :class:`numpy.ndarray`s of shape
+        (nbins_per_dim+1,)
     :param points: points used to define the voronoi tesselation (only the
         points that define regions of finite volumes)
     :type points: :class:`numpy.ndarrray` of shape (num_points, mdim)
 
-    :returns: finite volumes associated with ``points``
-    :rtype: :class:`numpy.ndarray` of shape (len(points),)
+    :rtype: tuple of (H, volume, edges)
+    :returns: H is the result of :meth:`np.histogramdd(points, edges,
+        normed=True)`, volumes is a :class:`numpy.ndarray` of shape (len(points),)
+        continaing the  finite volumes associated with ``points``
 
     """
     # adjust edges
     points_max = np.max(points, 0)
     points_min = np.min(points, 0)
-    # TODO replace with logical arrays to get rid of for loop.
+    # Use a loop because the number of edges per dimension is not the same.
     for dim, e in enumerate(edges):
         if len(edges) == 1:
             if e[0] >= points_min:
@@ -364,14 +281,11 @@ def simple_fun_uniform(points, volumes, rect_domain):
         `d_Tree` is the :class:`~scipy.spatial.KDTree` for points
 
     """
-    if len(points.shape) == 1:
-        points = np.expand_dims(points, axis=1)
-    # TODOthe lines below could be done in one or two lines.
-    rect_left = np.repeat([rect_domain[:, 0]], points.shape[0], 0)
-    rect_right = np.repeat([rect_domain[:,1]], points.shape[0], 0)
-    rect_left = np.all(np.greater_equal(points, rect_left), axis=1)
-    rect_right = np.all(np.less_equal(points, rect_right), axis=1)
-    inside = np.logical_and(rect_left, rect_right)
+    util.fix_dimensions_data(points)
+
+    inside = np.logical_and(np.all(np.greater_equal(points, rect_domain[:, 0]),
+        axis=1), np.all(np.less_equal(points, rect_domain[:, 1]), axis=1)) 
+
     rho_D_M = np.zeros(volumes.shape)
     rho_D_M[inside] = volumes[inside]/np.sum(volumes[inside]) # normalize on Lambda not D
 
diff --git a/test/test_calculateP/test_simpleFunP.py b/test/test_calculateP/test_simpleFunP.py
index be875cb8..ba952c4b 100644
--- a/test/test_calculateP/test_simpleFunP.py
+++ b/test/test_calculateP/test_simpleFunP.py
@@ -157,6 +157,31 @@ def test_M(self):
         """
         assert len(self.rho_D_M) == 67
 
+    def test_domain(self):
+        """
+        Test that the probabilities within the prescribed domain are non-zero
+        and that the probabilities outside of the prescribed domain are zero.
+        """
+        # d_distr_samples are (mdim, M)
+        # rect_domain is (mdim, 2)
+        inside = np.logical_and(np.all(np.greater_equal(self.d_distr_samples,
+            self.rect_domain[:, 0]), axis=1),
+            np.all(np.less_equal(self.d_distr_samples,
+            self.rect_domain[:, 1]), axis=1)) 
+        msg = "Due to the inherent randomness of this method, this may fail."
+        print msg
+        print np.sum(self.rho_D_M[inside] >= 0.0)
+        assert np.sum(self.rho_D_M[inside] >= 0.0)<100
+        #print self.rect_domain
+        #print "ind, inside", inside
+        #print "ind, outside", np.logical_not(inside)
+        #print "inside", self.d_distr_samples[inside]
+        #print "outside", self.d_distr_samples[np.logical_not(inside)]
+        #print "inside", self.rho_D_M[inside]
+        #print "outside", self.rho_D_M[np.logical_not(inside)]
+        print np.sum(self.rho_D_M[np.logical_not(inside)] == 0.0)
+        assert np.sum(self.rho_D_M[np.logical_not(inside)] == 0.0)<100
+
 class test_unif_unif_01D(data_01D, unif_unif):
     """
     Tests :meth:`bet.calculateP.simpleFunP.unif_unif` on 01D data domain.

From 2a5b7b400ab3b5b9b43bd514fd4d62a5b3a6a7fa Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Thu, 16 Apr 2015 21:46:14 -0500
Subject: [PATCH 46/85] fixes todos in calculate_P

---
 bet/calculateP/calculateP.py            | 186 ++++++++----------------
 bet/sampling/basicSampling.py           |   2 +-
 test/test_calculateP/test_calculateP.py |  30 ++--
 3 files changed, 69 insertions(+), 149 deletions(-)

diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index b32e603a..73aeebca 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -13,6 +13,7 @@
 from bet.Comm import *
 import numpy as np
 import scipy.spatial as spatial
+import bet.util as util
 
 def emulate_iid_lebesgue(lam_domain, num_l_emulate):
     """
@@ -35,13 +36,10 @@ def emulate_iid_lebesgue(lam_domain, num_l_emulate):
         lam_domain.shape[0]))+lam_domain[:, 0] 
     return lambda_emulate 
 
-def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
+def prob_emulated(samples, data, rho_D_M, d_distr_samples,
         lambda_emulate=None, d_Tree=None): 
     r"""
 
-    TODO: remove lam_domain as an argument, make sure to fix in all examples
-    and tests
-
     Calculates :math:`P_{\Lambda}(\mathcal{V}_{\lambda_{emulate}})`, the
     probability assoicated with a set of voronoi cells defined by
     ``num_l_emulate`` iid samples :math:`(\lambda_{emulate})`.
@@ -56,8 +54,6 @@ def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
         parition of D to for the simple function approximation
     :type d_distr_samples: :class:`~numpy.ndarray` of shape  (M, mdim) 
     :param d_Tree: :class:`~scipy.spatial.KDTree` for d_distr_samples
-    :param lam_domain: The domain for each parameter for the model.
-    :type lam_domain: :class:`~numpy.ndarray` of shape (ndim, 2)
     :param lambda_emulate: Samples used to partition the parameter space
     :type lambda_emulate: :class:`~numpy.ndarray` of shape (num_l_emulate, ndim)
     :rtype: tuple
@@ -76,7 +72,6 @@ def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
         d_Tree = spatial.KDTree(d_distr_samples)
         
     # Determine which inputs go to which M bins using the QoI
-    #io_ptr = dsearchn(d_distr_samples, data);
     (_, io_ptr) = d_Tree.query(data)
     
     # Determine which emulated samples match with which model run samples
@@ -86,7 +81,7 @@ def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
     # Calculate Probabilties
     P = np.zeros((lambda_emulate.shape[0],))
     d_distr_emu_ptr = np.zeros(emulate_ptr.shape)
-    d_distr_emu_ptr = io_ptr[emulate_ptr] #io_ptr_inverse[emulate_ptr] 
+    d_distr_emu_ptr = io_ptr[emulate_ptr]
     for i in range(rho_D_M.shape[0]):
         Itemp = np.equal(d_distr_emu_ptr, i)
         Itemp_sum = np.sum(Itemp)
@@ -96,12 +91,9 @@ def prob_emulated(samples, data, rho_D_M, d_distr_samples, lam_domain,
 
     return (P, lambda_emulate, io_ptr, emulate_ptr)
 
-def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None): 
+def prob(samples, data, rho_D_M, d_distr_samples, d_Tree=None): 
     r"""
     
-    TODO: remove lam_domain as an argument, make sure to fix in all examples
-    and tests
-
     Calculates :math:`P_{\Lambda}(\mathcal{V}_{\lambda_{samples}})`, the
     probability assoicated with a set of voronoi cells defined by the model
     solves at :math:`(\lambda_{samples})` where the volumes of these voronoi
@@ -117,129 +109,51 @@ def prob(samples, data, rho_D_M, d_distr_samples, lam_domain, d_Tree=None):
         parition of D to for the simple function approximation
     :type d_distr_samples: :class:`~numpy.ndarray` of shape  (M, mdim) 
     :param d_Tree: :class:`~scipy.spatial.KDTree` for d_distr_samples
-    :param lam_domain: The domain for each parameter for the model.
-    :type lam_domain: :class:`~numpy.ndarray` of shape (ndim, 2)
     :rtype: tuple of :class:`~numpy.ndarray` of sizes (num_samples,),
         (num_samples,), (ndim, num_l_emulate), (num_samples,), (num_l_emulate,)
-    :returns: (P, lam_vol, lambda_emulate, io_ptr, emulate_ptr) where P is the
-        probability associated with samples, lam_vol the volumes associated
-        with the samples, io_ptr a pointer from data to M bins, and emulate_ptr
-        a pointer from emulated samples to samples (in parameter space)
+    :returns: (P, lam_vol, lambda_emulate, io_ptr) where P is the
+        probability associated with samples, and lam_vol the volumes associated
+        with the samples, io_ptr a pointer from data to M bins.
 
     """
     if len(samples.shape) == 1:
         samples = np.expand_dims(samples, axis=1) 
     if len(data.shape) == 1:
         data = np.expand_dims(data, axis=1) 
-
-    # Calculate pointers and volumes
-    (P, _, io_ptr, emulate_ptr) = prob_emulated(samples, data,
-            rho_D_M, d_distr_samples, lam_domain, None, d_Tree)
-    
-    # Apply the standard MC approximation 
-    lam_vol = np.ones((samples.shape[0],))
-    # Calculate Probabilities
-    P = np.zeros((samples.shape[0],))
-    # TODO Fix issue of unecessary mpi call
-
-    for i in range(rho_D_M.shape[0]):
-        Itemp = np.equal(io_ptr, i)
-        Itemp_sum = np.sum(lam_vol[Itemp])
-        Itemp_sum = comm.allreduce(Itemp_sum, op=MPI.SUM)
-        if Itemp_sum > 0:
-            P[Itemp] = rho_D_M[i]*lam_vol[Itemp]/Itemp_sum 
-    lam_vol = (1.0/float(samples.shape[0]))*np.ones((samples.shape[0],))
-
-    return (P, lam_vol, io_ptr, emulate_ptr)
-
-def prob_qhull(samples, data, rho_D_M, d_distr_samples,
-        lam_domain, d_Tree=None): 
-    r"""
-
-    TODO: FIX OR REMOVE
-
-    Calculates :math:`P_{\Lambda}(\mathcal{V}_{\lambda_{emulate}})`, the
-    probability assoicated with a set of voronoi cells defined by
-    ``num_l_emulate`` iid samples :math:`(\lambda_{emulate})`.
-
-    This method is only intended when ``lam_domain`` is a generalized rectangle.
-
-    :param samples: The samples in parameter space for which the model was run.
-    :type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim)
-    :param data: The data from running the model given the samples.
-    :type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
-    :param rho_D_M: The simple function approximation of rho_D
-    :type rho_D_M: :class:`~numpy.ndarray` of shape  (M,) 
-    :param d_distr_samples: The samples in the data space that define a
-        parition of D to for the simple function approximation
-    :type d_distr_samples: :class:`~numpy.ndarray` of shape  (M,mdim) 
-    :param d_Tree: :class:`~scipy.spatial.KDTree` for d_distr_samples
-    :param lam_domain: The domain for each parameter for the model.
-    :type lam_domain: :class:`~numpy.ndarray` of shape (ndim, 2)
-    :returns: (P, io_ptr, lam_vol)
-
-    """
-    import pyhull
     if len(d_distr_samples.shape) == 1:
         d_distr_samples = np.expand_dims(d_distr_samples, axis=1)
-
     if d_Tree == None:
         d_Tree = spatial.KDTree(d_distr_samples)
-        
-    # Determine which inputs go to which M bins using the QoI
-    io_ptr = d_Tree.query(data)
 
-    # Calcuate the bounding region for the parameters
-    lam_bound = np.copy(samples)
-    lam_width = lam_domain[:, 1] - lam_domain[:, 0]
-    nbins = d_distr_samples.shape[1]
-    # Add fake samples outside of lam_domain to close Voronoi tesselations.
-    pts_per_edge = nbins
-    sides = np.zeros((2, pts_per_edge))
-    for i in range(lam_domain.shape[0]):
-        sides[i, :] = np.linspace(lam_domain[i, 0], lam_domain[i, 1],
-                pts_per_edge)
-    # add midpoints
-    for i in range(lam_domain.shape[0]):
-        new_pt = sides
-        new_pt[i, :] = np.repeat(lam_domain[i, 0] - lam_width[i]/pts_per_edge,
-                pts_per_edge, 0).transpose() 
-        lam_bound = np.vstack((lam_bound, new_pt))
-        new_pt = sides
-        new_pt[i, :] = np.repeat(lam_domain[i, 1] - lam_width[i]/pts_per_edge,
-                pts_per_edge, 0).transpose() 
-        lam_bound = np.vstack((lam_bound, new_pt))
+    # Set up local arrays for parallelism
+    local_index = range(0+rank, samples.shape[0], size)
+    samples_local = samples[local_index, :]
+    data_local = data[local_index, :]
+    local_array = np.array(local_index)
         
-    # add corners
-    corners = np.zeros((2**lam_domain.shape[0], lam_domain.shape[0]))
-    for i in range(lam_domain.shape[0]):
-        corners[i, :] = lam_domain[i, np.repeat(np.hstack((np.ones((1,
-            2**(i-1))), 2*np.ones((1, 2**(i - 1))))), 
-            2**(lam_domain.shape[0]-i), 0).transpose()] 
-        corners[i, :] += lam_width[i]*np.repeat(np.hstack((np.ones((1,
-            2**(i-1))), -np.ones((1, 2**(i - 1))))),
-            2**(lam_domain.shape[0]-i)/pts_per_edge, 0).transpose()
+    # Determine which inputs go to which M bins using the QoI
+    (_, io_ptr) = d_Tree.query(data_local)
 
-    lam_bound = np.vstack((lam_bound, corners))
-    
-    # Calculate the Voronoi diagram for samples. Calculate the volumes of 
-    # the convex hulls of the corresponding Voronoi regions.
-    lam_vol = np.zeros((samples.shape[-1],))
-    for i in range((samples.shape[0])):
-        vornoi = spatial.Voronoi(lam_bound)
-        lam_vol[i] = float(pyhull.qconvex('Qt FA', vornoi.vertices).split()[-1])
-    
-    # Calculate probabilities.
-    P = np.zeros((samples.shape[0],))
+    # Apply the standard MC approximation and
+    # calculate probabilities
+    P_local = np.zeros((samples_local.shape[0],))
     for i in range(rho_D_M.shape[0]):
         Itemp = np.equal(io_ptr, i)
-        P[Itemp] = rho_D_M[i]*lam_vol[Itemp]/np.sum(lam_vol[Itemp])
-    P = P/np.sum[P]
+        Itemp_sum = np.sum(Itemp)
+        Itemp_sum = comm.allreduce(Itemp_sum, op=MPI.SUM)
+        if Itemp_sum > 0:
+            P_local[Itemp] = rho_D_M[i]/Itemp_sum 
+    P_global = util.get_global_values(P_local)
+    global_index = util.get_global_values(local_array)
+    P = np.zeros(P_global.shape)
+    P[global_index] = P_global[:]
+
+    lam_vol = (1.0/float(samples.shape[0]))*np.ones((samples.shape[0],))
 
     return (P, lam_vol, io_ptr)
 
 def prob_mc(samples, data, rho_D_M, d_distr_samples,
-        lam_domain, lambda_emulate=None, d_Tree=None): 
+            lambda_emulate=None, d_Tree=None): 
     r"""
     Calculates :math:`P_{\Lambda}(\mathcal{V}_{\lambda_{samples}})`, the
     probability assoicated with a set of voronoi cells defined by the model
@@ -256,8 +170,6 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
         parition of D to for the simple function approximation
     :type d_distr_samples: :class:`~numpy.ndarray` of shape  (M, mdim) 
     :param d_Tree: :class:`~scipy.spatial.KDTree` for d_distr_samples
-    :param lam_domain: The domain for each parameter for the model.
-    :type lam_domain: :class:`~numpy.ndarray` of shape (ndim,2)
     :param int num_l_emulate: The number of iid samples used to parition the
         parameter space
     :rtype: tuple of :class:`~numpy.ndarray` of sizes (num_samples,),
@@ -272,11 +184,20 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
         samples = np.expand_dims(samples, axis=1) 
     if len(data.shape) == 1:
         data = np.expand_dims(data, axis=1) 
-    # Calculate pointers and volumes
-    (P, lambda_emulate, io_ptr, emulate_ptr) = prob_emulated(samples,
-            data, rho_D_M, d_distr_samples, lam_domain, lambda_emulate,
-            d_Tree)
+    if lambda_emulate == None:
+        lambda_emulate = samples
+    if len(d_distr_samples.shape) == 1:
+        d_distr_samples = np.expand_dims(d_distr_samples, axis=1)
+    if d_Tree == None:
+        d_Tree = spatial.KDTree(d_distr_samples)
+        
+    # Determine which inputs go to which M bins using the QoI
+    (_, io_ptr) = d_Tree.query(data)
     
+    # Determine which emulated samples match with which model run samples
+    l_Tree = spatial.KDTree(samples)
+    (_, emulate_ptr) = l_Tree.query(lambda_emulate)
+
     # Apply the standard MC approximation to determine the number of emulated
     # samples per model run sample. This is for approximating 
     # \mu_Lambda(A_i \intersect b_j)
@@ -288,17 +209,28 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
     lam_vol = clam_vol
     lam_vol = lam_vol/(len(lambda_emulate)*size)
 
+    # Set up local arrays for parallelism
+    local_index = range(0+rank, samples.shape[0], size)
+    samples_local = samples[local_index, :]
+    data_local = data[local_index, :]
+    lam_vol_local = lam_vol[local_index]
+    local_array = np.array(local_index)
+        
+    # Determine which inputs go to which M bins using the QoI
+    (_, io_ptr_local) = d_Tree.query(data_local)
+
     # Calculate Probabilities
-    # TODO Fix issue of unecessary mpi call
-    P = np.zeros((samples.shape[0],))
+    P_local = np.zeros((samples_local.shape[0],))
     for i in range(rho_D_M.shape[0]):
-        Itemp = np.equal(io_ptr, i)
-        # Prevent a divide by zero error
-        Itemp_sum = np.sum(lam_vol[Itemp])
+        Itemp = np.equal(io_ptr_local, i)
+        Itemp_sum = np.sum(lam_vol_local[Itemp])
         Itemp_sum = comm.allreduce(Itemp_sum, op=MPI.SUM)
         if Itemp_sum > 0:
-            P[Itemp] = rho_D_M[i]*lam_vol[Itemp]/Itemp_sum
-
+            P_local[Itemp] = rho_D_M[i]*lam_vol_local[Itemp]/Itemp_sum 
+    P_global = util.get_global_values(P_local)
+    global_index = util.get_global_values(local_array)
+    P = np.zeros(P_global.shape)
+    P[global_index] = P_global[:]
     return (P, lam_vol, lambda_emulate, io_ptr, emulate_ptr)
 
 
diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index 68de1687..a3a9ca45 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -258,7 +258,7 @@ def user_samples(self, samples, savefile, parallel=False):
                 my_samples = samples[my_index, :]
             my_data = self.lb_model(my_samples)
             data = util.get_global_values(my_data)
-            samples = util.get_global_values(my_data)
+            samples = util.get_global_values(my_samples)
         
         # if data or samples are of shape (num_samples,) expand dimensions
         if len(samples.shape) == 1:
diff --git a/test/test_calculateP/test_calculateP.py b/test/test_calculateP/test_calculateP.py
index 165de28c..51d678b3 100644
--- a/test/test_calculateP/test_calculateP.py
+++ b/test/test_calculateP/test_calculateP.py
@@ -64,7 +64,7 @@ def test_prob_sum_to_1(self):
         Test to see if the prob. sums to 1.
         """
         nptest.assert_almost_equal(np.sum(self.P),1.0)
-    @unittest.skipIf(size > 1, 'Only run in serial')
+    #@unittest.skipIf(size > 1, 'Only run in serial')
     def test_P_matches_true(self):
         """
         Test against reference probs. (Only in serial)
@@ -152,12 +152,11 @@ def setUp(self):
         Set up problem.
         """
         super(Test_prob_3to2, self).setUp()
-        (self.P, self.lam_vol , _ , _ ) = calcP.prob(samples=self.samples,
-                                                     data=self.data,
-                                                     rho_D_M = self.d_distr_prob,
-                                                     d_distr_samples = self.d_distr_samples,
-                                                     lam_domain = self.lam_domain,
-                                                     d_Tree = self.d_Tree)
+        (self.P, self.lam_vol , _  ) = calcP.prob(samples=self.samples,
+                                                  data=self.data,
+                                                  rho_D_M = self.d_distr_prob,
+                                                  d_distr_samples = self.d_distr_samples,
+                                                  d_Tree = self.d_Tree)
         self.P_ref = np.loadtxt(data_path + "/3to2_prob.txt.gz")
 
 
@@ -175,7 +174,6 @@ def setUp(self):
                                                               data=self.data,
                                                               rho_D_M = self.d_distr_prob,
                                                               d_distr_samples = self.d_distr_samples,
-                                                              lam_domain = self.lam_domain,
                                                               lambda_emulate = self.lambda_emulate,
                                                               d_Tree = self.d_Tree)
         self.P_emulate_ref=np.loadtxt(data_path + "/3to2_prob_emulated.txt.gz")
@@ -196,7 +194,6 @@ def setUp(self):
                                                            data=self.data,
                                                            rho_D_M = self.d_distr_prob,
                                                            d_distr_samples = self.d_distr_samples,
-                                                           lam_domain = self.lam_domain,
                                                            lambda_emulate = self.lambda_emulate,
                                                            d_Tree = self.d_Tree)
         self.P_ref = np.loadtxt(data_path + "/3to2_prob_mc.txt.gz")
@@ -231,11 +228,10 @@ def setUp(self):
         Set up problem.
         """
         super(Test_prob_3to1, self).setUp()
-        (self.P, self.lam_vol , _ , _ ) = calcP.prob(samples=self.samples,
+        (self.P, self.lam_vol, _ ) = calcP.prob(samples=self.samples,
                                                      data=self.data,
                                                      rho_D_M = self.d_distr_prob,
                                                      d_distr_samples = self.d_distr_samples,
-                                                     lam_domain = self.lam_domain,
                                                      d_Tree = self.d_Tree)
         self.P_ref = np.loadtxt(data_path + "/3to1_prob.txt.gz")
 
@@ -253,7 +249,6 @@ def setUp(self):
                                                               data=self.data,
                                                               rho_D_M = self.d_distr_prob,
                                                               d_distr_samples = self.d_distr_samples,
-                                                              lam_domain = self.lam_domain,
                                                               lambda_emulate = self.lambda_emulate,
                                                               d_Tree = self.d_Tree)
         self.P_emulate_ref=np.loadtxt(data_path + "/3to1_prob_emulated.txt.gz")
@@ -274,7 +269,6 @@ def setUp(self):
                                                            data=self.data,
                                                            rho_D_M = self.d_distr_prob,
                                                            d_distr_samples = self.d_distr_samples,
-                                                           lam_domain = self.lam_domain,
                                                            lambda_emulate = self.lambda_emulate,
                                                            d_Tree = self.d_Tree)
         self.P_ref = np.loadtxt(data_path + "/3to1_prob_mc.txt.gz")
@@ -313,11 +307,10 @@ def setUp(self):
         Set up problem.
         """
         super(Test_prob_10to4, self).setUp()
-        (self.P, self.lam_vol , _ , _ ) = calcP.prob(samples=self.samples,
+        (self.P, self.lam_vol , _ ) = calcP.prob(samples=self.samples,
                                                      data=self.data,
                                                      rho_D_M = self.d_distr_prob,
                                                      d_distr_samples = self.d_distr_samples,
-                                                     lam_domain = self.lam_domain,
                                                      d_Tree = self.d_Tree)
 
     
@@ -336,7 +329,6 @@ def setUp(self):
                                                               data=self.data,
                                                               rho_D_M = self.d_distr_prob,
                                                               d_distr_samples = self.d_distr_samples,
-                                                              lam_domain = self.lam_domain,
                                                               lambda_emulate = self.lambda_emulate,
                                                               d_Tree = self.d_Tree)
         self.P_emulate = util.get_global_values(self.P_emulate)
@@ -357,7 +349,6 @@ def setUp(self):
                                                            data=self.data,
                                                            rho_D_M = self.d_distr_prob,
                                                            d_distr_samples = self.d_distr_samples,
-                                                           lam_domain = self.lam_domain,
                                                            lambda_emulate = self.lambda_emulate,
                                                            d_Tree = self.d_Tree)
 
@@ -394,11 +385,10 @@ def setUp(self):
         Set up problem.
         """
         super(Test_prob_1to1, self).setUp()
-        (self.P, self.lam_vol , _ , _ ) = calcP.prob(samples=self.samples,
+        (self.P, self.lam_vol, _ ) = calcP.prob(samples=self.samples,
                                                      data=self.data,
                                                      rho_D_M = self.d_distr_prob,
                                                      d_distr_samples = self.d_distr_samples,
-                                                     lam_domain = self.lam_domain,
                                                      d_Tree = self.d_Tree)
 
 
@@ -416,7 +406,6 @@ def setUp(self):
                                                               data=self.data,
                                                               rho_D_M = self.d_distr_prob,
                                                               d_distr_samples = self.d_distr_samples,
-                                                              lam_domain = self.lam_domain,
                                                               lambda_emulate = self.lambda_emulate,
                                                               d_Tree = self.d_Tree)
         self.P_emulate = util.get_global_values(self.P_emulate)
@@ -435,7 +424,6 @@ def setUp(self):
                                                            data=self.data,
                                                            rho_D_M = self.d_distr_prob,
                                                            d_distr_samples = self.d_distr_samples,
-                                                           lam_domain = self.lam_domain,
                                                            lambda_emulate = self.lambda_emulate,
                                                            d_Tree = self.d_Tree)
 

From 23105d705cd7422cfc003e05f45b469614dd86f1 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 16 Apr 2015 22:07:49 -0500
Subject: [PATCH 47/85] addressed comments concerning the pull-request

---
 bet/calculateP/calculateP.py            |  3 ---
 bet/calculateP/voronoiHistogram.py      | 22 +++++++++++++---------
 bet/postProcess/plotP.py                |  2 --
 bet/sampling/adaptiveSampling.py        |  2 --
 bet/sampling/basicSampling.py           |  2 --
 test/test_calculateP/test_simpleFunP.py |  7 -------
 6 files changed, 13 insertions(+), 25 deletions(-)

diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index c240c1e8..b32e603a 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -1,6 +1,3 @@
-# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
-# presuming it preserse order anywhere
-
 r"""
 This module provides methods for calulating the probability
 measure :math:`P_{\Lambda}`.
diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index 10771355..6c09ac98 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -19,10 +19,11 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
     :param center: location of the center of the hyperrectangle
     :type center: :class:`numpy.ndarray` of shape (mdim,)
     :param r_size: size of the length of the sides of the
-        hyperrectangle to the surrounding domain
+        hyperrectangle rect_domain to definie voronoi cells for
     :type r_size: double or list()
     :param sur_domain: minima and maxima of each dimension defining the
-        surrounding domain
+        surrounding domain. The surrounding domain is the bounded domain
+        in the data space (i.e. the data domain).
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
 
     :rtype: tuple
@@ -33,7 +34,7 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
         shape (mdim, 2)
 
     """
-    # determine the hyperrectangle defined by center and r_size
+    # determine the hyperrectangle (rect_domain) defined by center and r_size
     rect_width = r_size*np.ones(sur_domain[:,0].shape)
     rect_domain = np.column_stack([center - .5*rect_width,
         center + .5*rect_width])
@@ -73,10 +74,11 @@ def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
     :param center: location of the center of the hyperrectangle
     :type center: :class:`numpy.ndarray` of shape (mdim,)
     :param r_ratio: ratio of the length of the sides of the
-        hyperrectangle to the surrounding domain
+        hyperrectangle rect_domain to definie voronoi cells for
     :type r_ratio: double or list()
     :param sur_domain: minima and maxima of each dimension defining the
-        surrounding domain
+        surrounding domain. The surrounding domain is the bounded domain
+        in the data space (i.e. the data domain).    
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
 
     :rtype: tuple
@@ -117,10 +119,11 @@ def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
     :param center: location of the center of the hyperrectangle
     :type center: :class:`numpy.ndarray` of shape (mdim,)
     :param r_size: size of the length of the sides of the
-        hyperrectangle to the surrounding domain
+        hyperrectangle rect_domain to definie voronoi cells for
     :type r_size: double or list()
     :param sur_domain: minima and maxima of each dimension defining the
-        surrounding domain
+        surrounding domain. The surrounding domain is the bounded domain
+        in the data space (i.e. the data domain).    
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
 
     :rtype: tuple
@@ -177,10 +180,11 @@ def edges_regular(center_pts_per_edge, center, r_ratio, sur_domain):
     :param center: location of the center of the hyperrectangle
     :type center: :class:`numpy.ndarray` of shape (mdim,)
     :param r_ratio: ratio of the length of the sides of the
-        hyperrectangle to the surrounding domain
+        hyperrectangle rect_domain to definie voronoi cells for
     :type r_ratio: double or list()
     :param sur_domain: minima and maxima of each dimension defining the
-        surrounding domain
+        surrounding domain. The surrounding domain is the bounded domain
+        in the data space (i.e. the data domain).       
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
     :rtype: tuple
     :returns: interior_and_layer1 is a list of dim
diff --git a/bet/postProcess/plotP.py b/bet/postProcess/plotP.py
index 07596bab..9209e937 100644
--- a/bet/postProcess/plotP.py
+++ b/bet/postProcess/plotP.py
@@ -1,5 +1,3 @@
-# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
-# presuming it preserse order anywhere
 """
 This module provides methods for plotting probabilities. 
 """
diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 97fdb556..8568c93a 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -1,5 +1,3 @@
-# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
-# presuming it preserse order anywhere
 # -*- coding: utf-8 -*-
 # Lindley Graham 3/10/2014
 """
diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index 5ca69280..68de1687 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -1,5 +1,3 @@
-# TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
-# presuming it preserse order anywhere
 # Lindley Graham 4/15/2014
 """
 This module contains functions for sampling. We assume we are given access to a
diff --git a/test/test_calculateP/test_simpleFunP.py b/test/test_calculateP/test_simpleFunP.py
index ba952c4b..01caab23 100644
--- a/test/test_calculateP/test_simpleFunP.py
+++ b/test/test_calculateP/test_simpleFunP.py
@@ -172,13 +172,6 @@ def test_domain(self):
         print msg
         print np.sum(self.rho_D_M[inside] >= 0.0)
         assert np.sum(self.rho_D_M[inside] >= 0.0)<100
-        #print self.rect_domain
-        #print "ind, inside", inside
-        #print "ind, outside", np.logical_not(inside)
-        #print "inside", self.d_distr_samples[inside]
-        #print "outside", self.d_distr_samples[np.logical_not(inside)]
-        #print "inside", self.rho_D_M[inside]
-        #print "outside", self.rho_D_M[np.logical_not(inside)]
         print np.sum(self.rho_D_M[np.logical_not(inside)] == 0.0)
         assert np.sum(self.rho_D_M[np.logical_not(inside)] == 0.0)<100
 

From 2b3f938e916ab9633c052d03b0ea8409d59bdb34 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 20 Apr 2015 20:21:36 -0500
Subject: [PATCH 48/85] finished todos in sFun and vHist

---
 bet/calculateP/simpleFunP.py                  |   8 +-
 bet/calculateP/voronoiHistogram.py            |  73 ++------
 test/test_calculateP/test_voronoiHistogram.py | 174 ++----------------
 3 files changed, 33 insertions(+), 222 deletions(-)

diff --git a/bet/calculateP/simpleFunP.py b/bet/calculateP/simpleFunP.py
index e411f3fb..70469a72 100644
--- a/bet/calculateP/simpleFunP.py
+++ b/bet/calculateP/simpleFunP.py
@@ -9,8 +9,6 @@
 import collections
 import bet.util as util
 
-# TODO change bin_size to more informative name like bound_size?
-
 def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     r"""
     Creates a simple function approximation of :math:`\rho_{\mathcal{D}}`
@@ -117,9 +115,6 @@ def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     can then be stored and accessed later by the algorithm using a completely
     different set of parameter samples and model solves.
     '''
-    # TODO check that the parallel implementation of this is correct 
-    # TODO since mpi4py DOES NOT PRESERVE ORDERING double check that we are not
-    # presuming it preserse order anywhere
     return (rho_D_M, d_distr_samples, d_Tree)
 
 def normal_normal(Q_ref, M, std, num_d_emulate=1E6):
@@ -360,8 +355,7 @@ def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1)
 
     points, _, rect_domain = vHist.center_and_layer1_points_binsize(center_pts_per_edge, 
             Q_ref, bin_size, sur_domain)
-    edges = vHist.edges_regular_binsize(center_pts_per_edge, Q_ref, bin_size,
-            sur_domain) 
+    edges = vHist.edges_regular(center_pts_per_edge, rect_domain, sur_domain) 
     _, volumes, _ = vHist.histogramdd_volumes(edges, points)
     return vHist.simple_fun_uniform(points, volumes, rect_domain)
 
diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index 6c09ac98..a9641d23 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -82,7 +82,7 @@ def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
 
     :rtype: tuple
-    :returns: (points, interior_and_layer1) where where points is an
+    :returns: (points, interior_and_layer1, rect_domain) where where points is an
         :class:`numpy.ndarray` of shape (num_points, dim), interior_and_layer1
         is a list() of dim :class:`numpy.ndarray`s of shape
         (center_pts_per_edge+2,), rect_domain is a :class:`numpy.ndarray` of
@@ -100,42 +100,35 @@ def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
     return center_and_layer1_points_binsize(center_pts_per_edge, center,
             r_size, sur_domain)
 
-def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
+def edges_regular(center_pts_per_edge, rect_domain, sur_domain):
     """
     Generates a sequence of arrays describing the edges of the finite voronoi
     cells in each direction. The voronoi tesselation is defined by regular grid
     of center points that define the voronoi tesselation of exactly the
-    interior of a hyperrectangle centered at ``center`` with sides of length
-    ``r_size`` and the layers of voronoi cells that bound these
-    interior cells. The resulting voronoi tesselation exactly represents the
-    hyperrectangle. The bounding voronoi cells are made finite by bounding them
-    with an  additional layer to represent ``sur_domain``.
+    interior of a hyperrectangle  defined by ``rect_domain``  and the layers of
+    voronoi cells that bound these interior cells. The resulting voronoi
+    tesselation exactly represents the hyperrectangle. The bounding voronoi
+    cells are made finite by bounding them with an  additional layer to
+    represent ``sur_domain``.
     
     This method can also be used to tile ``sur_domain`` with points to define
     voronoi regions if the user sets ``r_ratio = 1``. use binratio below
 
     :param list() center_pts_per_edge: number of center points per edge and
         additional two points will be added to create the bounding layer
-    :param center: location of the center of the hyperrectangle
-    :type center: :class:`numpy.ndarray` of shape (mdim,)
-    :param r_size: size of the length of the sides of the
-        hyperrectangle rect_domain to definie voronoi cells for
-    :type r_size: double or list()
+    :param rect_domain: The rectangular domain to define the voronoi
+        tesselation for. This domain should be contained in the ``sur_domain``.
+    :type rect_domain: :class:`numpy.ndarray` of shape (mdim, 2)
     :param sur_domain: minima and maxima of each dimension defining the
         surrounding domain. The surrounding domain is the bounded domain
         in the data space (i.e. the data domain).    
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
 
     :rtype: tuple
-    :returns: interior_and_layer1 is a list of dim
-        :class:`numpy.ndarray`s of shape (center_pts_per_edge+2,)
-    """
-    # TODO This is redoing stuff that was done when generating the points
-    # determine the hyperrectangle defined by center and r_size
-    rect_width = r_size*np.ones(sur_domain[:,0].shape)
-    rect_domain = np.column_stack([center - .5*rect_width,
-        center + .5*rect_width])
+    :returns: interior_and_layer1 is a list of dim :class:`numpy.ndarray`s of
+        shape (center_pts_per_edge+2,)
 
+    """
     if np.any(np.greater_equal(sur_domain[:, 0], rect_domain[:, 0])):
         msg = "The hyperrectangle defined by this size is larger than the"
         msg += "original domain."
@@ -161,46 +154,6 @@ def edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain):
 
     return rect_and_sur_edges
 
-def edges_regular(center_pts_per_edge, center, r_ratio, sur_domain):
-    """
-    Generates a sequence of arrays describing the edges of the finite voronoi
-    cells in each direction. The voronoi tesselation is defined by regular grid
-    of center points that define the voronoi tesselation of exactly the
-    interior of a hyperrectangle centered at ``center`` with sides of length
-    ``r_ratio*sur_width`` and the layers of voronoi cells that bound these
-    interior cells. The resulting voronoi tesselation exactly represents the
-    hyperrectangle. The bounding voronoi cells are made finite by bounding them
-    with an  additional layer to represent ``sur_domain``.
-    
-    This method can also be used to tile ``sur_domain`` with points to define
-    voronoi regions if the user sets ``r_ratio = 1``.
-
-    :param list() center_pts_per_edge: number of center points per edge and
-        additional two points will be added to create the bounding layer
-    :param center: location of the center of the hyperrectangle
-    :type center: :class:`numpy.ndarray` of shape (mdim,)
-    :param r_ratio: ratio of the length of the sides of the
-        hyperrectangle rect_domain to definie voronoi cells for
-    :type r_ratio: double or list()
-    :param sur_domain: minima and maxima of each dimension defining the
-        surrounding domain. The surrounding domain is the bounded domain
-        in the data space (i.e. the data domain).       
-    :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
-    :rtype: tuple
-    :returns: interior_and_layer1 is a list of dim
-        :class:`numpy.ndarray`s of shape (center_pts_per_edge+2,)
-
-    """
-    if np.any(np.greater(r_ratio, 1)):
-        msg = "The hyperrectangle defined by this ratio is larger than the"
-        msg += "original domain. RATIO: {}".format(r_ratio)
-        print msg
-    
-    # determine r_size from the width of the surrounding domain
-    r_size = r_ratio*(sur_domain[:, 1]-sur_domain[:, 0])
-
-    return edges_regular_binsize(center_pts_per_edge, center, r_size, sur_domain)
-
 def edges_from_points(points):
     """
     Given a sequence of arrays describing the voronoi points in each dimension
diff --git a/test/test_calculateP/test_voronoiHistogram.py b/test/test_calculateP/test_voronoiHistogram.py
index c5fb1b9c..e5a1b85e 100644
--- a/test/test_calculateP/test_voronoiHistogram.py
+++ b/test/test_calculateP/test_voronoiHistogram.py
@@ -301,12 +301,11 @@ def setUp(self):
         super(test_calps_double_3D, self).createDomain()
         super(test_calps_double_3D, self).setUp()
 
-
 class edges(object):
     """
-    Provides a method to test that the dimensions of the output (and the output
-    itself) from methods with the pattern
-    ``bet.calculateP.voronoiHistogram.edges_*`` are correct.
+     Provides a method to test that the dimensions of the output (and the
+     output itself) from methods with the pattern
+     ``bet.calculateP.voronoiHistogram.edges_*`` are correct.
     """
     def test_dimensions(self):
         """
@@ -336,7 +335,7 @@ class edges_regular(edges):
     """
     def create_output(self):
         sur_width = self.sur_domain[:,1]-self.sur_domain[:,0]
-        rect_width = self.r_ratio*sur_width
+        rect_width = 0.2*sur_width
         rect_domain = np.empty(self.sur_domain.shape)
         rect_domain[:, 0] = self.center - .5*rect_width
         rect_domain[:, 1] = self.center + .5*rect_width
@@ -349,176 +348,41 @@ def create_output(self):
             int_l2[0] = self.sur_domain[dim, 0]
             int_l2[-1] = self.sur_domain[dim, 1]
             self.my_edges.append(int_l2)
+        self.rect_domain = rect_domain
     
     def setUp(self):
         self.create_output()
-        self.rect_and_sur_edges = vHist.edges_regular(self.center_pts_per_edge, self.center,
-                self.r_ratio, self.sur_domain)
-
-
-class edges_regular_binsize(edges_regular):
-    """
-    Test :meth:`bet.calculateP.voronoiHistogram.edges_regular_binsize`
-    """
-    def setUp(self):
-        self.r_ratio = self.r_size/self.sur_domain[:,1]
-        super(edges_regular_binsize, self).create_output()
-        self.rect_and_sur_edges = vHist.edges_regular_binsize(self.center_pts_per_edge,
-                self.center, self.r_size, self.sur_domain)
-
-
-class edges_regular_double(edges_regular):
-    """
-    Provides set up for
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` when
-    r_ratio is a double.
-    """
-    def setUp(self):
-        self.r_ratio = 0.2
-        super(edges_regular_double, self).setUp()
-
-class edges_regular_list(edges_regular):
-    """
-    Provides set up for
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` when
-    r_ratio is a list.
-    """
-    def setUp(self):
-        self.r_ratio = 0.2*np.ones(self.mdim)
-        super(edges_regular_list, self).setUp()
-
-class edges_regular_binsize_list(edges_regular_binsize):
-    """
-    Provides set up for
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular_binsize` when
-    r_size is a list.
-    """
-    def setUp(self):
-        self.r_size = self.sur_domain[:,1]*.2
-        super(edges_regular_binsize_list, self).setUp()
-
-class edges_regular_binsize_double(edges_regular_binsize):
-    """
-    Provides set up for
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular_binsize` when
-    r_size is a double.
-    """
-    def setUp(self):
-        self.r_size = self.sur_domain[0,1]*.2
-        super(edges_regular_binsize_double, self).setUp()
-
-class test_er_list_1D(domain_1D, edges_regular_list):
-    """
-    Test
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 1D
-    domain with r_ratio as a list.
-    """
-    def setUp(self):
-        super(test_er_list_1D, self).createDomain()
-        super(test_er_list_1D, self).setUp()
-class test_er_list_2D(domain_2D, edges_regular_list):
-    """
-    Test
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 2D
-    domain with r_ratio as a list.
-    """
-    def setUp(self):
-        super(test_er_list_2D, self).createDomain()
-        super(test_er_list_2D, self).setUp()
-class test_er_list_3D(domain_3D, edges_regular_list):
-    """
-    Test
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 3D
-    domain with r_ratio as a list.
-    """
-    def setUp(self):
-        super(test_er_list_3D, self).createDomain()
-        super(test_er_list_3D, self).setUp()
-
-class test_er_double_1D(domain_1D, edges_regular_double):
-    """
-    Test
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 1D
-    domain with r_ratio as a double.
-    """
-    def setUp(self):
-        super(test_er_double_1D, self).createDomain()
-        super(test_er_double_1D, self).setUp()
-class test_er_double_2D(domain_2D, edges_regular_double):
-    """
-    Test
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 2D
-    domain with r_ratio as a double.
-    """
-    def setUp(self):
-        super(test_er_double_2D, self).createDomain()
-        super(test_er_double_2D, self).setUp()
-class test_er_double_3D(domain_3D, edges_regular_double):
-    """
-    Test
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 3D
-    domain with r_ratio as a double.
-    """
-    def setUp(self):
-        super(test_er_double_3D, self).createDomain()
-        super(test_er_double_3D, self).setUp()
+        self.rect_and_sur_edges = vHist.edges_regular(self.center_pts_per_edge,
+                self.rect_domain, self.sur_domain)
 
 
-class test_ers_list_1D(domain_1D, edges_regular_binsize_list):
+class test_er_1D(domain_1D, edges_regular):
     """
     Test
     :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 1D
-    domain with r_ratio as a list.
-    """
-    def setUp(self):
-        super(test_ers_list_1D, self).createDomain()
-        super(test_ers_list_1D, self).setUp()
-class test_ers_list_2D(domain_2D, edges_regular_binsize_list):
-    """
-    Test
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 2D
-    domain with r_ratio as a list.
-    """
-    def setUp(self):
-        super(test_ers_list_2D, self).createDomain()
-        super(test_ers_list_2D, self).setUp()
-class test_ers_list_3D(domain_3D, edges_regular_binsize_list):
-    """
-    Test
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 3D
-    domain with r_ratio as a list.
-    """
-    def setUp(self):
-        super(test_ers_list_3D, self).createDomain()
-        super(test_ers_list_3D, self).setUp()
-
-class test_ers_double_1D(domain_1D, edges_regular_binsize_double):
-    """
-    Test
-    :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 1D
-    domain with r_ratio as a double.
+    domain.
     """
     def setUp(self):
-        super(test_ers_double_1D, self).createDomain()
-        super(test_ers_double_1D, self).setUp()
-class test_ers_double_2D(domain_2D, edges_regular_binsize_double):
+        super(test_er_1D, self).createDomain()
+        super(test_er_1D, self).setUp()
+class test_er_2D(domain_2D, edges_regular):
     """
     Test
     :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 2D
-    domain with r_ratio as a double.
+    domain .
     """
     def setUp(self):
-        super(test_ers_double_2D, self).createDomain()
-        super(test_ers_double_2D, self).setUp()
-class test_ers_double_3D(domain_3D, edges_regular_binsize_double):
+        super(test_er_2D, self).createDomain()
+        super(test_er_2D, self).setUp()
+class test_er_3D(domain_3D, edges_regular):
     """
     Test
     :meth:`bet.calculateP.voronoiHistogram.edges_regular` for a 3D
-    domain with r_ratio as a double.
+    domain.
     """
     def setUp(self):
-        super(test_ers_double_3D, self).createDomain()
-        super(test_ers_double_3D, self).setUp()
+        super(test_er_3D, self).createDomain()
+        super(test_er_3D, self).setUp()
 
 class edges_from_points(edges):
     """

From 3712aaf023b493fbfea5237ce4d51745f1c67c2f Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 20 Apr 2015 20:38:23 -0500
Subject: [PATCH 49/85] removed obsolete test

---
 test/test_calculateP/test_simpleFunP.py | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/test/test_calculateP/test_simpleFunP.py b/test/test_calculateP/test_simpleFunP.py
index 01caab23..d50faded 100644
--- a/test/test_calculateP/test_simpleFunP.py
+++ b/test/test_calculateP/test_simpleFunP.py
@@ -221,13 +221,6 @@ def setUp(self):
         super(test_unif_unif_3D, self).createData()
         super(test_unif_unif_3D, self).setUp()
 
-@unittest.skip("Implement or remove multivariate_gaussian method from module")
-class test_multivariate_gaussian(unittest.TestCase):
-    """
-    TODO: Write a test for multivariate_gaussian or remove from module
-    """
-    pass
-
 class normal_normal(prob):
     """
     Set up :meth:`bet.calculateP.simpleFunP.normal_normal` on data domain.

From 834f61c9076bda4e8ea2f25f4749fab73b9115f3 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Tue, 21 Apr 2015 12:27:00 -0500
Subject: [PATCH 50/85] making minor changes to samping subpackage

---
 bet/sampling/adaptiveSampling.py            | 86 +--------------------
 bet/sampling/basicSampling.py               |  5 +-
 test/test_sampling/test_adaptiveSampling.py | 14 ++--
 test/test_sampling/test_basicSampling.py    | 14 ++--
 4 files changed, 17 insertions(+), 102 deletions(-)

diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 8568c93a..6975ba03 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -132,56 +132,6 @@ def run_gen(self, kern_list, rho_D, maximum, param_min, param_max,
         sort_ind = np.argsort(results_rD)
         return (results, r_step_size, results_rD, sort_ind, mean_ss)
 
-    # TODO This appears to be an unused function
-    def run_reseed(self, kern_list, rho_D, maximum, param_min, param_max,
-            t_set, savefile, initial_sample_type="lhs", criterion='center',
-            reseed=3):
-        """
-        Generates samples using reseeded chains and a list of different
-        kernels.
-
-        THIS IS NOT OPERATIONAL DO NOT USE.
-
-        :param list() kern_list: List of
-            :class:~`bet.sampling.adaptiveSampling.kernel` objects.
-        :param rho_D: probability density on D
-        :type rho_D: callable function that takes a :class:`np.array` and
-            returns a :class:`numpy.ndarray`
-        :param double maximum: maximum value of rho_D
-        :param param_min: minimum value for each parameter dimension
-        :type param_min: np.array (ndim,)
-        :param param_max: maximum value for each parameter dimension
-        :type param_max: np.array (ndim,)
-        :param t_set: method for creating new parameter steps using
-            given a step size based on the paramter domain size
-        :type t_set: :class:~`bet.sampling.adaptiveSampling.transition_set`
-        :param string savefile: filename to save samples and data
-        :param string initial_sample_type: type of initial sample random (or r),
-            latin hypercube(lhs), or space-filling curve(TBD)
-         :param string criterion: latin hypercube criterion see 
-            `PyDOE <http://pythonhosted.org/pyDOE/randomized.html>`_
-        :rtype: tuple
-        :returns: ((samples, data), all_step_ratios, num_high_prob_samples,
-            sorted_incidices_of_num_high_prob_samples, average_step_ratio)
-
-        """
-        results = list()
-        # reseeding sampling
-        results = list()
-        r_step_size = list()
-        results_rD = list()
-        mean_ss = list()
-        for kern in kern_list:
-            (samples, data, step_sizes) = self.reseed_chains(
-                    param_min, param_max, t_set, kern, savefile,
-                    initial_sample_type, criterion, reseed)
-            results.append((samples, data))
-            r_step_size.append(step_sizes)
-            results_rD.append(int(sum(rho_D(data)/maximum)))
-            mean_ss.append(np.mean(step_sizes))
-        sort_ind = np.argsort(results_rD)
-        return (results, r_step_size, results_rD, sort_ind, mean_ss)
-
     def run_tk(self, init_ratio, min_ratio, max_ratio, rho_D, maximum,
             param_min, param_max, kernel, savefile,
             initial_sample_type="lhs", criterion='center'):
@@ -307,9 +257,9 @@ def generalized_chains(self, param_min, param_max, t_set, kern,
         # Initialize Nx1 vector Step_size = something reasonable (based on size
         # of domain and transition set type)
         # Calculate domain size
-        # TODO The following could probably be done in one line
         param_left = np.repeat([param_min], self.num_chains_pproc, 0)
         param_right = np.repeat([param_max], self.num_chains_pproc, 0)
+
         param_width = param_right - param_left
         # Calculate step_size
         max_ratio = t_set.max_ratio
@@ -395,40 +345,6 @@ def generalized_chains(self, param_min, param_max, t_set, kern,
 
         return (samples, data, all_step_ratios)
         
-    #TODO MOve this function to a dev branch since it is not implemented.
-    def reseed_chains(self, param_min, param_max, t_set, kern,
-            savefile, initial_sample_type="lhs", criterion='center', reseed=1):
-        """
-        Basic adaptive sampling algorithm.
-
-        NOT YET IMPLEMENTED.
-
-        :param string initial_sample_type: type of initial sample random (or r),
-            latin hypercube(lhs), or space-filling curve(TBD)
-        :param param_min: minimum value for each parameter dimension
-        :type param_min: np.array (ndim,)
-        :param param_max: maximum value for each parameter dimension
-        :type param_max: np.array (ndim,)
-        :param t_set: method for creating new parameter steps using
-            given a step size based on the paramter domain size
-        :type t_set: :class:~`bet.sampling.adaptiveSampling.transition_set`
-        :param function kern: functional that acts on the data used to
-            determine the proposed change to the ``step_size``
-        :type kernel: :class:~`bet.sampling.adaptiveSampling.kernel` object.
-        :param string savefile: filename to save samples and data
-        :param string criterion: latin hypercube criterion see 
-            `PyDOE <http://pythonhosted.org/pyDOE/randomized.html>`_
-
-
-        :param int reseed: number of times to reseed the chains
-        :rtype: tuple
-        :returns: (``parameter_samples``, ``data_samples``) where
-            ``parameter_samples`` is np.ndarray of shape (num_samples, ndim)
-            and ``data_samples`` is np.ndarray of shape (num_samples, mdim)
-
-        """
-        pass
-
 def kernels(Q_ref, rho_D, maximum):
     """
     Generates a list of kernstic objects.
diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index a3a9ca45..7c2439dd 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -105,17 +105,18 @@ def loadmat(save_file, model=None):
     # load the data from a *.mat file
     mdat = sio.loadmat(save_file)
     # load the samples
-    # TODO: calculate the number of samples rather than loading it
     if mdat.has_key('samples'):
         samples = mdat['samples']
+        num_samples = samples.shape[0]
     else:
         samples = None
+        num_samples = None
     # load the data
     if mdat.has_key('data'):
         data = mdat['data']
     else:
         data = None
-    loaded_sampler = sampler(model, mdat['num_samples'])    
+    loaded_sampler = sampler(model, num_samples)    
     return (loaded_sampler, samples, data)
 
 class sampler(object):
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index f01f5fb9..5d52e514 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -33,11 +33,12 @@ def test_loadmat_init():
         num_chains_pproc2])
     num_samples1, num_samples2 = chain_length * np.array([num_chains1,
         num_chains2])
+    
+    sio.savemat(os.path.join(local_path, 'testfile1'), mdat1)
+    sio.savemat(os.path.join(local_path, 'testfile2'), mdat2)
 
-    sio.savemat('testfile1', mdat1)
-    sio.savemat('testfile2', mdat2)
-
-    (loaded_sampler1, samples1, data1) = asam.loadmat('testfile1')
+    (loaded_sampler1, samples1, data1) = asam.loadmat(os.path.join(local_path,
+        'testfile1'))
     nptest.assert_array_equal(samples1, mdat1['samples'])
     nptest.assert_array_equal(data1, mdat1['data'])
     assert loaded_sampler1.num_samples == num_samples1
@@ -48,7 +49,8 @@ def test_loadmat_init():
             loaded_sampler1.sample_batch_no)
     assert loaded_sampler1.lb_model == None
 
-    (loaded_sampler2, samples2, data2) = asam.loadmat('testfile2', model)
+    (loaded_sampler2, samples2, data2) = asam.loadmat(os.path.join(local_path,
+        'testfile2'), model)
     nptest.assert_array_equal(samples2, mdat2['samples'])
     nptest.assert_array_equal(data2, None)
     assert loaded_sampler2.num_samples == num_samples2
@@ -60,7 +62,7 @@ def test_loadmat_init():
     if os.path.exists(os.path.join(local_path, 'testfile1.mat')):
         os.remove(os.path.join(local_path, 'testfile1.mat'))
     if os.path.exists(os.path.join(local_path, 'testfile2.mat')):
-        os.remove(os.path.join(loca_path, 'testfile2.mat'))
+        os.remove(os.path.join(local_path, 'testfile2.mat'))
 
 def verify_samples(model, QoI_range, sampler, param_min, param_max,
         t_set, savefile, initial_sample_type):
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index 109c9874..135fcf8f 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -61,8 +61,8 @@ def test_loadmat():
     mdat2 = {'samples':np.random.random((6,1)), 'num_samples':6}
     model = "this is not a model"
 
-    sio.savemat('testfile1', mdat1)
-    sio.savemat('testfile2', mdat2)
+    sio.savemat(os.path.join(local_path, 'testfile1'), mdat1)
+    sio.savemat(os.path.join(local_path, 'testfile2'), mdat2)
 
     (loaded_sampler1, samples1, data1) = bsam.loadmat('testfile1')
     nptest.assert_array_equal(samples1, mdat1['samples'])
@@ -78,7 +78,7 @@ def test_loadmat():
     if os.path.exists(os.path.join(local_path, 'testfile1.mat')):
         os.remove(os.path.join(local_path, 'testfile1.mat'))
     if os.path.exists(os.path.join(local_path, 'testfile2.mat')):
-        os.remove(os.path.join(loca_path, 'testfile2.mat'))
+        os.remove(os.path.join(local_path, 'testfile2.mat'))
 
 def verify_user_samples(model, sampler, samples, savefile, parallel):
     # evalulate the model at the samples directly
@@ -101,7 +101,7 @@ def verify_user_samples(model, sampler, samples, savefile, parallel):
     assert samples.shape[0] == sampler.num_samples
     # did the file get correctly saved?
 
-    mdat = sio.loadmat('savefile')
+    mdat = sio.loadmat(savefile)
     nptest.assert_array_equal(samples, mdat['samples'])
     nptest.assert_array_equal(data, mdat['data'])
     assert samples.shape[0] == sampler.num_samples
@@ -147,7 +147,7 @@ def verify_random_samples(model, sampler, sample_type, param_min, param_max,
     assert samples.shape[0] == sampler.num_samples
     # did the file get correctly saved?
 
-    mdat = sio.loadmat('savefile')
+    mdat = sio.loadmat(savefile)
     nptest.assert_array_equal(samples, mdat['samples'])
     nptest.assert_array_equal(data, mdat['data'])
     assert samples.shape[0] == sampler.num_samples
@@ -201,10 +201,6 @@ def test_init(self):
         assert self.samplers[0].lb_model == self.models[0]
         assert bsam.sampler(self.models[0], None).num_samples == None
 
-    @unittest.skip("Skipping testing saving")
-    def test_save(self):
-        pass
-
     def test_update(self):
         """
         Test :meth:`bet.sampling.basicSampling.sampler.save`

From f180113ed30f7cae61f666286c127efb7130be03 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Tue, 21 Apr 2015 20:01:51 -0500
Subject: [PATCH 51/85] finished todos in plotDomains

---
 bet/postProcess/plotDomains.py            | 148 ++++++++++++------
 test/test_postProcess/test_plotDomains.py | 178 ++++++++++++++--------
 2 files changed, 217 insertions(+), 109 deletions(-)

diff --git a/bet/postProcess/plotDomains.py b/bet/postProcess/plotDomains.py
index ab91a3a6..82ed80ea 100644
--- a/bet/postProcess/plotDomains.py
+++ b/bet/postProcess/plotDomains.py
@@ -2,13 +2,13 @@
 This module provides methods used to plot two-dimensional domains and/or
 two-dimensional slices/projections of domains.
 
-TODO : match method signatures with style in plotP
 """
 
 import matplotlib.tri as tri
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.lines import Line2D
+from itertools import combinations
 
 markers = []
 for m in Line2D.markers:
@@ -20,7 +20,7 @@
 
 colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k')
 
-def scatter_2D(samples, sample_nos, color, p_ref, save, show,
+def scatter_2D(samples, sample_nos, color, p_ref, save, interactive,
         xlabel, ylabel, filename):
     """
     Two-dimensional scatter plot of ``samples`` colored by ``color`` (usually
@@ -34,7 +34,7 @@ def scatter_2D(samples, sample_nos, color, p_ref, save, show,
     :param p_ref: reference parameter value
     :type p_ref: :class:`np.ndarray`
     :param boolean save: flag whether or not to save the figure
-    :param boolean show: flag whether or not to show the figure
+    :param boolean interactive: flag whether or not to show the figure
     :param string xlabel: x-axis label
     :param string ylabel: y-axis label
     :param string filename: filename to save the figure as
@@ -55,12 +55,12 @@ def scatter_2D(samples, sample_nos, color, p_ref, save, show,
         plt.ylabel(ylabel)
         plt.savefig(filename, bbox_inches='tight', transparent=True,
                 pad_inches=0)
-    if show:
+    if interactive:
         plt.show()
     else:
         plt.close()
 
-def scatter_3D(samples, sample_nos, color, p_ref, save, show,
+def scatter_3D(samples, sample_nos, color, p_ref, save, interactive,
         xlabel, ylabel, zlabel, filename):
     """
     Three-dimensional scatter plot of ``samples`` colored by ``color`` (usually
@@ -74,7 +74,7 @@ def scatter_3D(samples, sample_nos, color, p_ref, save, show,
     :param p_ref: reference parameter value
     :type p_ref: :class:`np.ndarray`
     :param boolean save: flag whether or not to save the figure
-    :param boolean show: flag whether or not to show the figure
+    :param boolean interactive: flag whether or not to show the figure
     :param string xlabel: x-axis label
     :param string ylabel: y-axis label
     :param string zlabel: z-axis label
@@ -100,17 +100,14 @@ def scatter_3D(samples, sample_nos, color, p_ref, save, show,
     if save:
         plt.savefig(filename, bbox_inches='tight', transparent=True,
                 pad_inches=0)
-    if show:
+    if interactive:
         plt.show()
     else:
         plt.close()
    
 def show_param(samples, data, rho_D=None, p_ref=None, sample_nos=None,
-        save=True, show=False, lnums=None):
+        save=True, interactive=False, lnums=None, showdim=None):
     """
-    TODO: add options for higher dimensional visualization or at least for
-    showing pairwise or tripletwise parameter point clouds
-
     Plot samples in parameter space and colors them either by rho_D or by
     sample batch number.
 
@@ -125,9 +122,11 @@ def show_param(samples, data, rho_D=None, p_ref=None, sample_nos=None,
     :param p_ref: reference parameter value
     :type p_ref: :class:`np.ndarray`
     :param boolean save: flag whether or not to save the figure
-    :param boolean show: flag whether or not to show the figure
+    :param boolean interactive: flag whether or not to show the figure
     :param list lnums: integers representing parameter domain coordinate
         numbers
+    :param int showdim: 2 or 3, flag showing pairwise or tripletwise parameter
+        sample clouds
 
     """
    
@@ -139,19 +138,31 @@ def show_param(samples, data, rho_D=None, p_ref=None, sample_nos=None,
     ylabel = r'$\lambda_{'+str(lnums[1])+'}$'
     savename = 'param_samples_cs.eps'
     if samples.shape[1] == 2:
-        scatter_2D(samples, sample_nos, rD, p_ref, save, show, xlabel, ylabel,
-                savename)
+        scatter_2D(samples, sample_nos, rD, p_ref, save, interactive, xlabel,
+                ylabel, savename)
     elif samples.shape[1] == 3:
         zlabel = r'$\lambda_{'+str(lnums[2])+'}$'
-        scatter_3D(samples, sample_nos, rD, p_ref, save, show, xlabel, ylabel,
-                zlabel, savename)
+        scatter_3D(samples, sample_nos, rD, p_ref, save, interactive, xlabel,
+                ylabel, zlabel, savename)
+    elif samples.shape[1] > 2 and showdim == 2:
+        for x, y in combinations(lnums, 2):
+            xlabel = r'$\lambda_{'+str(x)+'}$'
+            ylabel = r'$\lambda_{'+str(y)+'}$'
+            savename = 'param_samples_l'+str(x)+'l'+str(y)+'_cs.eps'
+            scatter_2D(samples[:, [x, y]], sample_nos, rD, p_ref, save,
+                    interactive, xlabel, ylabel, savename)
+    elif samples.shape[1] > 3 and showdim == 3:
+        for x, y, z in combinations(lnums, 3):
+            xlabel = r'$\lambda_{'+str(x)+'}$'
+            ylabel = r'$\lambda_{'+str(y)+'}$'
+            zlabel = r'$\lambda_{'+str(z)+'}$'
+            savename = 'param_samples_l'+str(x)+'l'+str(y)+'l'+str(z)+'_cs.eps'
+            scatter_3D(samples[:, [x, y, z]], sample_nos, rD, p_ref, save,
+                    interactive, xlabel, ylabel, zlabel, savename)
 
 def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
-        save=True, show=False, Q_nums=None):
+        save=True, interactive=False, Q_nums=None, showdim=None):
     """
-    TODO: add options for higher dimensional visualization or at least for
-    showing pairwise or tripletwise sample point clouds
-
     Plot samples in data space and colors them either by rho_D or by
     sample batch number.
 
@@ -164,7 +175,10 @@ def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
     :param Q_ref: reference data value
     :type Q_ref: :class:`np.ndarray`
     :param boolean save: flag whether or not to save the figure
-    :param boolean show: flag whether or not to show the figure
+    :param boolean interactive: flag whether or not to show the figure
+    :param list Q_nums: integers representing data domain coordinates
+    :param int showdim: 2 or 3, flag showing pairwise or tripletwise data
+        sample clouds
 
     """   
     if rho_D != None:
@@ -175,26 +189,39 @@ def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
     ylabel = r'$q_{'+str(Q_nums[1]+1)+'}$'
     savename = 'data_samples_cs.eps'
     if data.shape[1] == 2:
-        scatter_2D(data, sample_nos, rD, Q_ref, save, show, xlabel, ylabel,
-            savename)
+        scatter_2D(data, sample_nos, rD, Q_ref, save, interactive, xlabel,
+                ylabel, savename)
     elif data.shape[1] == 3:
         zlabel = r'$q_{'+str(Q_nums[2]+1)+'}$'
-        scatter_3D(data, sample_nos, rD, Q_ref, save, show, xlabel, ylabel,
-                zlabel, savename)
+        scatter_3D(data, sample_nos, rD, Q_ref, save, interactive, xlabel,
+                ylabel, zlabel, savename)
+    elif data.shape[1] > 2 and showdim == 2:
+        for x, y in combinations(Q_nums, 2):
+            xlabel = r'$q_{'+str(x)+'}$'
+            ylabel = r'$q_{'+str(y)+'}$'
+            savename = 'data_sampqes_q'+str(x)+'q'+str(y)+'_cs.eps'
+            scatter_2D(data[:, [x, y]], sample_nos, rD, Q_ref, save,
+                    interactive, xlabel, ylabel, savename)
+    elif data.shape[1] > 3 and showdim == 3:
+        for x, y, z in combinations(Q_nums, 3):
+            xlabel = r'$q_{'+str(x)+'}$'
+            ylabel = r'$q_{'+str(y)+'}$'
+            zlabel = r'$q_{'+str(z)+'}$'
+            savename = 'data_sampqes_q'+str(x)+'q'+str(y)+'q'+str(z)+'_cs.eps'
+            scatter_3D(data[:, [x, y, z]], sample_nos, rD, Q_ref, save,
+                    interactive, xlabel, ylabel, zlabel, savename)
 
 def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
         img_folder='figs/', ref_markers=None,
-        ref_colors=None):
+        ref_colors=None, showdim=None):
     r"""
-    TODO: add option for showing all pairs or altering which index for which
-    all pairs are shown
-
     Plot the data domain D using a triangulation based on the generating
     samples where :math:`Q={q_1, q_i}` for ``i=Q_nums``, with a marker for
     various :math:`Q_{ref}`. 
 
     :param samples: Samples to plot
-    :type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim)
+    :type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim). Only
+        uses the first two dimensions.
     :param data: Data associated with ``samples``
     :type data: :class:`np.ndarray`
     :param Q_ref: reference data value
@@ -203,6 +230,9 @@ def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
     :param string img_folder: folder to save the plots to
     :param list ref_markers: list of marker types for :math:`Q_{ref}`
     :param list ref_colors: list of colors for :math:`Q_{ref}`
+    :param showdim: default 1. If int then flag to show all combinations with a
+        given dimension or if ``all`` show all combinations.
+    :type showdim: int or string
 
     """
     if ref_markers == None:
@@ -211,28 +241,52 @@ def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
         ref_colors = colors
     if  Q_nums == None:
         Q_nums = range(data.shape[1])
+    if showdim == None:
+        showdim = 1
 
     triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
     triangles = triangulation.triangles
 
-    for i in Q_nums:
-        
-        plt.tricontourf(data[:, 0], data[:, i], np.zeros((data.shape[0],)),
-                triangles=triangles, colors='grey') 
-        plt.autoscale(tight=True)
-        plt.xlabel(r'$q_1$')
-        plt.ylabel(r'$q_{'+str(i+1)+r'}$')
+    
+    if type(showdim) == int:
+        for i in Q_nums:
+            plt.tricontourf(data[:, showdim], data[:, i],
+                    np.zeros((data.shape[0],)), triangles=triangles,
+                    colors='grey') 
+            plt.autoscale(tight=True)
+            xlabel = r'$q_{'+str(showdim+1)+r'}$'
+            ylabel = r'$q_{'+str(i+1)+r'}$'
+            plt.xlabel(xlabel)
+            plt.ylabel(ylabel)
+
+            filenames = [img_folder+'domain_q'+str(showdim+1)+'_'+str(i+1)+'.eps',
+                    img_folder+'q'+str(showdim+1)+'_q'+str(i+1)+'_domain_Q_cs.eps']
+                
+            show_data_domain_2D(samples, data[:, [showdim, i]], Q_ref[:,
+                [showdim, i]], ref_markers, ref_colors, xlabel=xlabel,
+                ylabel=ylabel, triangles=triangles, save=True,
+                interactive=False, filenames=filenames)
+    elif showdim == 'all' or showdim == 'ALL':
+        for x, y in combinations(Q_nums, 2):
+            plt.tricontourf(data[:, x], data[:, y], np.zeros((data.shape[0],)),
+                    triangles=triangles, colors='grey') 
+            plt.autoscale(tight=True)
+            xlabel = r'$q_{'+str(x+1)+r'}$'
+            ylabel = r'$q_{'+str(y+1)+r'}$'
+            plt.xlabel(xlabel)
+            plt.ylabel(ylabel)
 
-        filenames = [img_folder+'domain_q1_'+str(i)+'.eps',
-                img_folder+'q1_q'+str(i)+'_domain_Q_cs.eps']
-            
-        show_data_domain_2D(samples, data[:, [0, i]], Q_ref[:, [0, i]],
-            ref_markers, ref_colors, ylabel=r'$q_{'+str(i+1)+r'}$',
-            triangles=triangles, save=True, show=False, filenames=filenames)
+            filenames = [img_folder+'domain_q'+str(x+1)+'_'+str(y+1)+'.eps',
+                    img_folder+'q'+str(x+1)+'_q'+str(y+1)+'_domain_Q_cs.eps']
+                
+            show_data_domain_2D(samples, data[:, [x, y]], Q_ref[:,
+                [x, y]], ref_markers, ref_colors, xlabel=xlabel,
+                ylabel=ylabel, triangles=triangles, save=True,
+                interactive=False, filenames=filenames)
 
 def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
         ref_colors=None, xlabel=r'$q_1$', ylabel=r'$q_2',
-        triangles=None, save=True, show=True, filenames=None):
+        triangles=None, save=True, interactive=True, filenames=None):
     r"""
     Plot the data domain D using a triangulation based on the generating
     samples with a marker for various :math:`Q_{ref}`. Assumes that the first
@@ -251,7 +305,7 @@ def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
     :param triangles: triangulation defined by ``samples``
     :type triangles: :class:`tri.Triuangulation.triangles`
     :param boolean save: flag whether or not to save the figure
-    :param boolean show: flag whether or not to show the figure
+    :param boolean interactive: flag whether or not to show the figure
     :param list filenames: file names for the unmarked and marked domain plots
 
     """
@@ -279,11 +333,9 @@ def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
     if save:
         plt.savefig(filenames[1], bbox_inches='tight', transparent=True,
             pad_inches=0)
-    if show:
+    if interactive:
         plt.show()
     else:
         plt.close()
 
 
-
-
diff --git a/test/test_postProcess/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
index f0ca4f72..978723e8 100644
--- a/test/test_postProcess/test_plotDomains.py
+++ b/test/test_postProcess/test_plotDomains.py
@@ -15,40 +15,50 @@
 import numpy.testing as nptest
 from bet.Comm import *
 
-local_path = os.path.join(os.path.dirname(bet.__file__), "../test/test_sampling")
+local_path = os.path.join(os.path.dirname(bet.__file__),
+        "../test/test_sampling")
 
 class test_plotDomains(unittest.TestCase):
     """
-    Test :meth:`bet.postProcess.plotP.calculate_1D_marginal_probs` and  :meth:`bet.postProcess.plotP.calculate_2D_marginal_probs` for a 2D
+    Test :meth:`bet.postProcess.plotP.calculate_1D_marginal_probs` and  
+    :meth:`bet.postProcess.plotP.calculate_2D_marginal_probs` for a 2D
     parameter space.
     """
     def setUp(self):
         """
         Set up problem.
         """
-        self.lam_domain=np.array([[0.0,1.0],[0.0,1.0], [0.0, 1.0]])
-        self.samples=util.meshgrid_ndim((np.linspace(self.lam_domain[0][0],
-            self.lam_domain[0][1], 10),np.linspace(self.lam_domain[1][0],
-                self.lam_domain[1][1], 10), np.linspace(self.lam_domain[1][0],
-                    self.lam_domain[1][1], 10)))
+        self.lam_domain = np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]])
+        self.samples = util.meshgrid_ndim((np.linspace(self.lam_domain[0][0],
+            self.lam_domain[0][1], 10), np.linspace(self.lam_domain[1][0],
+            self.lam_domain[1][1], 10), np.linspace(self.lam_domain[1][0],
+            self.lam_domain[1][1], 10), np.linspace(self.lam_domain[1][0],
+            self.lam_domain[1][1], 10)))
         self.data = self.samples*3.0
         self.P_samples = (1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
         self.filename = "testfigure"
-        
+
         QoI_range = np.array([3.0, 3.0, 3.0])
         Q_ref = QoI_range*0.5
-	bin_size = 0.15*QoI_range
-	maximum = 1/np.product(bin_size)
-	def ifun(outputs):
-	    left = np.repeat([Q_ref-.5*bin_size], outputs.shape[0], 0)
-	    right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0)
-	    left = np.all(np.greater_equal(outputs, left), axis=1)
-	    right = np.all(np.less_equal(outputs, right), axis=1)
-	    inside = np.logial_and(left, right)
-	    max_values = np.repeate(maximum, outputs.shape[0], 0)
-	    return inside.astype('float64')*max_values
-	self.rho_D = ifun
-        self.lnums = [1,2,3]
+        bin_size = 0.15*QoI_range
+        maximum = 1/np.product(bin_size)
+        def ifun(outputs):
+            """
+            Indicator function.
+            :param outputs: outputs
+            :type outputs: :class:`numpy.ndarray` of shape (N, ndim)
+            :rtype: :class:`numpy.ndarray` of shape (N,)
+            :returns: 0 if outside of set or positive number if inside set
+            """
+            left = np.repeat([Q_ref-.5*bin_size], outputs.shape[0], 0)
+            right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0)
+            left = np.all(np.greater_equal(outputs, left), axis=1)
+            right = np.all(np.less_equal(outputs, right), axis=1)
+            inside = np.logial_and(left, right)
+            max_values = np.repeate(maximum, outputs.shape[0], 0)
+            return inside.astype('float64')*max_values
+        self.rho_D = ifun
+        self.lnums = [1, 2, 3]
         self.markers = []
         for m in Line2D.markers:
             try:
@@ -64,7 +74,8 @@ def tearDown(self):
         """
         # remove any files the we create
         filenames = glob.glob(self.filename+".*")
-        filenames.append('param_samples_cs.eps')
+        filenames.extend(glob.glob('param_samples_*cs.eps'))
+        filenames.extend(glob.glob('data_samples_*cs.eps'))
         filenames.append('domain_q1_q1_cs.eps')
         filenames.append('q1_q2_domain_Q_cs.eps')
         figfiles = glob.glob('figs/*')
@@ -78,7 +89,7 @@ def test_scatter_2D(self):
         Test :meth:`bet.postProcess.plotDomains.scatter_2D`
         """
         sample_nos = [None, 25]
-        p_ref = [None, self.samples[4,[0, 1]]]
+        p_ref = [None, self.samples[4, [0, 1]]]
         save = [True, False]
         for sn in sample_nos:
             for pr in p_ref:
@@ -86,21 +97,25 @@ def test_scatter_2D(self):
                     yield self.check_scatter_2D, sn, pr, s
 
     def check_scatter_2D(self, sample_nos, p_ref, save):
+        """
+        Check to see that the :meth:`bet.postTools.plotDomains.scatter_2D` ran
+        without generating an error.
+        """
         try:
-            plotDomains.scatter_2D(self.samples[:,[0, 1]], sample_nos,
-                self.P_samples, p_ref, save, False, 'XLABEL', 'YLABEL',
-                self.filename)
+            plotDomains.scatter_2D(self.samples[:, [0, 1]], sample_nos,
+                    self.P_samples, p_ref, save, False, 'XLABEL', 'YLABEL',
+                    self.filename)
             go = True
         except (RuntimeError, TypeError, NameError):
             go = False
         nptest.assert_equal(go, True)
- 
+
     def test_scatter_3D(self):
         """
         Test :meth:`bet.postProcess.plotDomains.scatter_3D`
         """
         sample_nos = [None, 25]
-        p_ref = [None, self.samples[4,:]]
+        p_ref = [None, self.samples[4, :]]
         save = [True, False]
         for sn in sample_nos:
             for pr in p_ref:
@@ -108,10 +123,14 @@ def test_scatter_3D(self):
                     yield self.check_scatter_3D, sn, pr, s
 
     def check_scatter_3D(self, sample_nos, p_ref, save):
+        """
+        Check to see that the :meth:`bet.postTools.plotDomains.scatter_3D` ran
+        without generating an error.
+        """
         try:
-            plotDomains.scatter_3D(self.samples, sample_nos, self.P_samples,
-                    p_ref, save, False, 'XLABEL', 'YLABEL', 'ZLABEL',
-                    self.filename) 
+            plotDomains.scatter_3D(self.samples[:, [0, 1, 2]], sample_nos,
+                    self.P_samples, p_ref, save, False, 'XLABEL', 'YLABEL',
+                    'ZLABEL', self.filename) 
             go = True
         except (RuntimeError, TypeError, NameError):
             go = False
@@ -123,21 +142,33 @@ def test_show_param(self):
         """
         sample_nos = [None, 25]
         save = [True, False]
-        samples = [self.samples, self.samples[:,[0, 1]]]
+        samples = [self.samples, self.samples[:, [0, 1]],
+                self.samples[:, [0, 1, 2]]]
         lnums = [None, self.lnums]
 
         for sample in samples:
-            for ln in lnums:
-                for sn in sample_nos:
-                    p_ref = [None, samples[4,:]]
-                    for pr in p_ref:
-                        for s in save:
-                            yield self.check_show_param, samples, pr, sn, s, ln
-
-    def check_show_param(self, samples, sample_nos, p_ref, save, lnums):
+            showdim = [None]
+            if sample.shape[0] > 2:
+                showdim.append(2)
+            if sample.shape[0] > 3:
+                showdim.append(3)
+            for sd in showdim:
+                for ln in lnums:
+                    for sn in sample_nos:
+                        p_ref = [None, sample[4, :]]
+                        for pr in p_ref:
+                            for s in save:
+                                yield self.check_show_param, samples, pr, sn, s, ln, sd
+
+    def check_show_param(self, samples, sample_nos, p_ref, save, lnums,
+            showdim):
+        """
+        Check to see that the :meth:`bet.postTools.plotDomains.show_param` ran
+        without generating an error.
+        """
         try:
             plotDomains.show_param(samples, self.data, self.rho_D, p_ref,
-                    sample_nos, save, False, lnums) 
+                    sample_nos, save, False, lnums, showdim) 
             go = True
         except (RuntimeError, TypeError, NameError):
             go = False
@@ -149,21 +180,31 @@ def test_show_data(self):
         """
         sample_nos = [None, 25]
         save = [True, False]
-        data_sets = [self.data, self.data[:,[0, 1]]]
+        data_sets = [self.data, self.data[:, [0, 1]]]
         qnums = [None, self.lnums]
 
         for data in data_sets:
-            for qn in qnums:
-                for sn in sample_nos:
-                    Q_ref = [None, data[4,:]]
-                    for qr in Q_ref:
-                        for s in save:
-                            yield self.check_show_data, data, sn, qr, s, qn
-
-    def check_show_data(self, data, sample_nos, q_ref, save, qnums):
+            showdim = [None]
+            if data.shape[0] > 2:
+                showdim.append(2)
+            if data.shape[0] > 3:
+                showdim.append(3)
+            for sd in showdim:
+                for qn in qnums:
+                    for sn in sample_nos:
+                        Q_ref = [None, data[4, :]]
+                        for qr in Q_ref:
+                            for s in save:
+                                yield self.check_show_data, data, sn, qr, s, qn, sd
+
+    def check_show_data(self, data, sample_nos, q_ref, save, qnums, showdim):
+        """
+        Check to see that the :meth:`bet.postTools.plotDomains.show_data` ran
+        without generating an error.
+        """
         try:
             plotDomains.show_data(data, self.rho_D, q_ref,
-                    sample_nos, save, False, qnums) 
+                    sample_nos, save, False, qnums, showdim) 
             go = True
         except (RuntimeError, TypeError, NameError):
             go = False
@@ -182,14 +223,18 @@ def test_show_data_domain_2D(self):
 
         for rm in ref_markers:
             for rc in ref_colors:
-                    for t in triangles:
-                        for s in save:
-                            for fn in filenames:
-                                yield self.check_show_data_domain_2D, rm, rc, t, s,
-                                fn
+                for t in triangles:
+                    for s in save:
+                        for fn in filenames:
+                            yield self.check_show_data_domain_2D, rm, rc, t, s, fn
 
     def check_show_data_domain_2D(self, ref_markers, ref_colors, triangles,
             save, filenames):
+        """
+        Check to see that the
+        :meth:`bet.postTools.plotDomains.show_data_domain_2D` ran
+        without generating an error.
+        """
         Q_ref = self.data[4, [0, 1]]
         data = self.data[:, [0, 1]]
         try:
@@ -200,27 +245,38 @@ def check_show_data_domain_2D(self, ref_markers, ref_colors, triangles,
         except (RuntimeError, TypeError, NameError):
             go = False
         nptest.assert_equal(go, True) 
-        
+
     def test_show_data_domain_multi(self):
         """
         Test :meth:`bet.postProcess.plotDomains.show_data_domain_multi`
         """
         os.mkdir('figs/')
-        Q_nums = [None, [1, 2]]
+        Q_nums = [None, [1, 2], [1, 2, 3]]
         ref_markers = [None, self.markers]
         ref_colors = [None, self.colors]
         for rm in ref_markers:
             for rc in ref_colors:
                 for qn in Q_nums:
-                    yield self.check_show_data_domain_multi, rm, rc, qn
+                    showdim = [None, 1]
+                    if len(qn) > 2:
+                        showdim.append('all', 'ALL')
+                    for sd in showdim:
+                        yield self.check_show_data_domain_multi, rm, rc, qn, sd
 
-    def check_show_data_domain_multi(self, ref_markers, ref_colors, Q_nums):
-        Q_ref = self.data[4,:]
+    def check_show_data_domain_multi(self, ref_markers, ref_colors, Q_nums,
+            showdim):
+        """
+        Check to see that the
+        :meth:`bet.postTools.plotDomains.show_data_domain_multi` ran
+        without generating an error.
+        """
+        Q_ref = self.data[4, :]
         try:
             plotDomains.show_data_domain_multi(self.samples, self.data,
                     Q_ref, Q_nums, ref_markers=ref_markers,
-                    ref_colors=ref_colors)
+                    ref_colors=ref_colors, showdim=showdim)
             go = True
         except (RuntimeError, TypeError, NameError):
             go = False
+        nptest.assert_equal(go, True)      
 

From 9b09e7938d2e0bf33684205fe6a002bdec69e39f Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 22 Apr 2015 18:21:50 -0500
Subject: [PATCH 52/85] finished testing 2 of 5 kernels

---
 test/test_sampling/test_adaptiveSampling.py | 517 ++++++++++++++++++--
 1 file changed, 471 insertions(+), 46 deletions(-)

diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 5d52e514..19e6ecad 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 # Lindley Graham 04/07/2015
 
 """
@@ -12,7 +13,8 @@
 from bet.Comm import *
 import bet
 
-local_path = os.path.join(os.path.dirname(bet.__file__), "../test/test_sampling")
+local_path = os.path.join(os.path.dirname(bet.__file__),
+    "../test/test_sampling")
 
 def test_loadmat_init():
     """
@@ -20,10 +22,10 @@ def test_loadmat_init():
     :meth:`bet.sampling.adaptiveSampling.sampler.init`.
     """
     chain_length = 10
-    mdat1 = {'samples':np.random.random((5,1)),
-            'data':np.random.random((5,1)), 'num_samples':50,
+    mdat1 = {'samples':np.random.random((5, 1)),
+            'data':np.random.random((5, 1)), 'num_samples':50,
             'chain_length':chain_length} 
-    mdat2 = {'samples':np.random.random((6,1)),
+    mdat2 = {'samples':np.random.random((6, 1)),
                     'num_samples':60, 'chain_length':chain_length}
     model = "this is not a model"
     
@@ -64,14 +66,22 @@ def test_loadmat_init():
     if os.path.exists(os.path.join(local_path, 'testfile2.mat')):
         os.remove(os.path.join(local_path, 'testfile2.mat'))
 
-def verify_samples(model, QoI_range, sampler, param_min, param_max,
+def verify_samples(QoI_range, sampler, param_min, param_max,
         t_set, savefile, initial_sample_type):
+    """
+    Run :meth:`bet.sampling.adaptiveSampling.sampler.generalized_chains` and
+    verify that the samples have the correct dimensions and are containted in
+    the bounded parameter space.
+    """
 
     # create indicator function
     Q_ref = QoI_range*0.5
     bin_size = 0.15*QoI_range
     maximum = 1/np.product(bin_size)
     def ifun(outputs):
+        """
+        Indicator function
+        """
         left = np.repeat([Q_ref-.5*bin_size], outputs.shape[0], 0)
         right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0)
         left = np.all(np.greater_equal(outputs, left), axis=1)
@@ -128,23 +138,39 @@ class Test_adaptive_sampler(unittest.TestCase):
     """
 
     def setUp(self):
+        """
+        Set up
+        """
+
         # create 1-1 map
         self.param_min1 = np.zeros((1, ))
         self.param_max1 = np.zeros((1, ))
         def map_1t1(x):
+            """
+            1 to 1 map
+            """
             return x*2.0
         # create 3-1 map
         self.param_min3 = np.zeros((3, ))
         self.param_max3 = np.ones((3, ))
         def map_3t1(x):
+            """
+            3 to 1 map
+            """
             return np.sum(x, 1)
         # create 3-2 map
         def map_3t2(x):
+            """
+            3 to 2 map
+            """
             return np.vstack(([x[:, 0]+x[:, 1], x[:, 2]])).transpose()
         # create 10-4 map
         self.param_min10 = np.zeros((10, ))
-        self.param_min10 = np.ones((10, ))
+        self.param_max10 = np.ones((10, ))
         def map_10t4(x):
+            """
+            10 to 4 map
+            """
             x1 = x[:, 0] + x[:, 1]
             x2 = x[:, 2] + x[:, 3]
             x3 = x[:, 4] + x[:, 5]
@@ -168,16 +194,25 @@ def map_10t4(x):
             self.samplers.append(asam.sampler(num_samples, chain_length,
                 model))
 
+        self.param_min_list = [self.param_min1, self.param_min1,
+                self.param_min3, self.param_min3, self.param_min10]
+        self.param_max_list = [self.param_max1, self.param_max1,
+                self.param_max3, self.param_max3, self.param_max10]
+
+        self.test_list = zip(self.models, self.QoI_range, self.samplers,
+                self.param_min_list, self.param_max_list, self.savefiles)
+
+
     def tearDown(self):
         for f in self.savefiles:
             if os.path.exists(f+".mat"):
                 os.remove(f+".mat")
         if size > 1:
             for f in self.savefiles:
-                for proc in range(size):
-                    proc_savefile = os.path.join(local_path,
-                            os.path.dirname(f), "proc{}{}".format(rank,
-                                os.path.basename(f)))
+                proc_savefile = os.path.join(local_path, os.path.dirname(f),
+                        "proc{}{}".format(rank, os.path.basename(f)))
+                if os.path.exists(proc_savefile):
+                    os.remove(proc_savefile)
 
     def test_update(self):
         """
@@ -192,85 +227,475 @@ def test_update(self):
                 np.repeat(range(self.samplers[0].num_chains),
                     self.samplers[0].chain_length, 0))
     
-    @unittest.skip("Implement me")
     def test_run_gen(self):
+        """
+        Run :meth:`bet.sampling.adaptiveSampling.sampler.run_gen` and verify
+        that the output has the correct dimensions.
+        """
         # sampler.run_gen(kern_list, rho_D, maximum, param_min, param_max,
         # t_set, savefile, initial_sample_type)
         # returns list where each member is a tuple ((samples, data),
         # all_step_ratios, num_high_prob_samples,
         # sorted_indices_of_num_high_prob_samples, average_step_ratio)
-        pass
-    @unittest.skip("Implement me")
+            # create indicator function
+        inputs = self.test_list[3]
+        _, QoI_range, sampler, param_min, param_max, savefile = inputs
+        
+        Q_ref = QoI_range*0.5
+        bin_size = 0.15*QoI_range
+        maximum = 1/np.product(bin_size)
+        def ifun(outputs):
+            """
+            Indicator function
+            """
+            inside = np.logical_and(np.all(np.greater_equal(outputs,
+                Q_ref-.5*bin_size), axis=1), np.all(np.less_equal(outputs,
+                    Q_ref+.5*bin_size), axis=1)) 
+            max_values = np.repeat(maximum, outputs.shape[0], 0)
+            return inside.astype('float64')*max_values
+
+        # create rhoD_kernel
+        kernel_rD = asam.rhoD_kernel(maximum, ifun)
+        kern_list = [kernel_rD]*2
+
+        # create t_set
+        t_set = asam.transition_set(.5, .5**5, 1.0) 
+
+        # run run_gen
+        output = sampler.run_gen(kern_list, ifun, maximum, param_min,
+                param_max, t_set, savefile)
+
+        results, r_step_size, results_rD, sort_ind, mean_ss = output
+
+        for out in output:
+            assert len(out) == 2
+
+        for samples, data in results:
+            assert samples.shape == (sampler.num_samples, len(param_min))
+            assert data.shape == (sampler.num_samples, len(QoI_range))
+        for step_sizes in r_step_size:
+            assert step_sizes.shape == (sampler.num_chains,
+                    sampler.chain_length) 
+        for num_hps in results_rD:
+            assert type(num_hps) == int
+        for inds in sort_ind:
+            assert np.issubdtype(type(inds), int)
+        for asr in mean_ss:
+            assert asr > t_set.min_ratio
+            assert asr < t_set.max_ratio
+    
     def test_run_tk(self):
+        """
+        Run :meth:`bet.sampling.adaptiveSampling.sampler.run_tk` and verify
+        that the output has the correct dimensions.
+        """
         # sampler.run_tk(init_ratio, min_raio, max_ratio, rho_D, maximum,
         # param_min, param_max, kernel, savefile, intial_sample_type)
         # returns list where each member is a tuple ((samples, data),
         # all_step_ratios, num_high_prob_samples,
         # sorted_indices_of_num_high_prob_samples, average_step_ratio)
-        pass
-    @unittest.skip("Implement me")
+        inputs = self.test_list[3]
+        _, QoI_range, sampler, param_min, param_max, savefile = inputs
+        
+        Q_ref = QoI_range*0.5
+        bin_size = 0.15*QoI_range
+        maximum = 1/np.product(bin_size)
+        def ifun(outputs):
+            """
+            Indicator function
+            """
+            inside = np.logical_and(np.all(np.greater_equal(outputs,
+                Q_ref-.5*bin_size), axis=1), np.all(np.less_equal(outputs,
+                    Q_ref+.5*bin_size), axis=1)) 
+            max_values = np.repeat(maximum, outputs.shape[0], 0)
+            return inside.astype('float64')*max_values
+
+        # create rhoD_kernel
+        kernel_rD = asam.rhoD_kernel(maximum, ifun)
+
+        # create t_set
+        init_ratio = [1.0, .5, .25]
+        min_ratio = [.5**2, .5**5, .5**7]
+        max_ratio = [1.0, .75, .5]
+
+        # run run_gen
+        output = sampler.run_tk(init_ratio, min_ratio, max_ratio, ifun, maximum, param_min,
+                param_max, kernel_rD, savefile)
+        
+        results, r_step_size, results_rD, sort_ind, mean_ss = output
+
+        for out in output:
+            assert len(out) == 3
+
+        for samples, data in results:
+            assert samples.shape == (sampler.num_samples, len(param_min))
+            assert data.shape == (sampler.num_samples, len(QoI_range))
+        for step_sizes in r_step_size:
+            assert step_sizes.shape == (sampler.num_chains,
+                    sampler.chain_length) 
+        for num_hps in results_rD:
+            assert type(num_hps) == int
+        for inds in sort_ind:
+            assert np.issubdtype(type(inds), int)
+        for asr, mir, mar in zip(mean_ss, min_ratio, max_ratio):
+            assert asr > mir
+            assert asr < mar
+
     def test_run_inc_dec(self):
+        """
+        Run :meth:`bet.sampling.adaptiveSampling.sampler.run_inc_dec` and verify
+        that the output has the correct dimensions.
+        """
         # sampler.run_inc_dec(increase, decrease, tolerance, rho_D, maximum,
         # param_min, param_max, t_set, savefile, initial_sample_type)
         # returns list where each member is a tuple ((samples, data),
         # all_step_ratios, num_high_prob_samples,
         # sorted_indices_of_num_high_prob_samples, average_step_ratio)
-        pass
+        inputs = self.test_list[3]
+        _, QoI_range, sampler, param_min, param_max, savefile = inputs
+        
+        Q_ref = QoI_range*0.5
+        bin_size = 0.15*QoI_range
+        maximum = 1/np.product(bin_size)
+        def ifun(outputs):
+            """
+            Indicator function
+            """
+            inside = np.logical_and(np.all(np.greater_equal(outputs,
+                Q_ref-.5*bin_size), axis=1), np.all(np.less_equal(outputs,
+                    Q_ref+.5*bin_size), axis=1)) 
+            max_values = np.repeat(maximum, outputs.shape[0], 0)
+            return inside.astype('float64')*max_values
+
+        # create rhoD_kernel
+        increase = [2.0, 3.0, 5.0]
+        decrease = [.7, .5, .2]
+        tolerance = [1e-3, 1e-4, 1e-7]
+
+        # create t_set
+        t_set = asam.transition_set(.5, .5**5, 1.0) 
+
+        # run run_gen
+        output = sampler.run_inc_dec(increase, decrease, tolerance, ifun, maximum, param_min,
+                param_max, t_set, savefile)
+
+        results, r_step_size, results_rD, sort_ind, mean_ss = output
+
+        for out in output:
+            assert len(out) == 3
+
+        for samples, data in results:
+            assert samples.shape == (sampler.num_samples, len(param_min))
+            assert data.shape == (sampler.num_samples, len(QoI_range))
+        for step_sizes in r_step_size:
+            assert step_sizes.shape == (sampler.num_chains,
+                    sampler.chain_length) 
+        for num_hps in results_rD:
+            assert type(num_hps) == int
+        for inds in sort_ind:
+            assert np.issubdtype(type(inds), int)
+        for asr in mean_ss:
+            assert asr > t_set.min_ratio
+            assert asr < t_set.max_ratio
 
     def test_generalized_chains(self):
         """
         Test :met:`bet.sampling.adaptiveSampling.sampler.generalized_chains`
         for three different QoI maps (1 to 1, 3 to 1, 3 to 2, 10 to 4).
         """
-        param_min_list = [self.param_min1, self.param_min1, self.param_min3,
-            self.param_min3, self.param_min10]
-        param_max_list = [self.param_max1, self.param_max1, self.param_max3,
-            self.param_max3, self.param_max10]
         # create a transition set
         t_set = asam.transition_set(.5, .5**5, 1.0) 
 
-        test_list = zip(self.models, self.QoI_range, self.samplers,
-                param_min_list, param_max_list, self.savefiles)
-
-        for model, QoI_range, sampler, param_min, param_max, savefile, rho_D in test_list:
+        for _, QoI_range, sampler, param_min, param_max, savefile, _ in self.test_list:
             for initial_sample_type in ["random", "r", "lhs"]:
-                        yield verify_samples, model, QoI_range, sampler,
-                        param_min, param_max, t_set, savefile, initial_sample_type
+                yield verify_samples, QoI_range, sampler, param_min, param_max, t_set, savefile, initial_sample_type
 
-@unittest.skip("Implement me")
-def test_kernels():
-        pass
+class test_kernels(unittest.TestCase):
+    """
+    Tests kernels for a 1d, 2d, 4d data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        self.QoI_range = [np.array([3.0]),
+                np.array([2.0, 1.0]), np.array([2.0, 2.0, 2.0, 4.0])]
+
+    def test_list(self):
+        """
+        Run test for a 1d, 2d, and 4d data space.
+        """
+        for QoI_range in self.QoI_range:
+            Q_ref = QoI_range*0.5
+            bin_size = 0.15*QoI_range
+            maximum = 1/np.product(bin_size)
+            def ifun(outputs):
+                """
+                Indicator function
+                """
+                inside = np.logical_and(np.all(np.greater_equal(outputs,
+                    Q_ref-.5*bin_size), axis=1), np.all(np.less_equal(outputs,
+                        Q_ref+.5*bin_size), axis=1)) 
+                max_values = np.repeat(maximum, outputs.shape[0], 0)
+                return inside.astype('float64')*max_values
+            self.verify_indiv(Q_ref, ifun, maximum)
+
+    def verify_indiv(self, Q_ref, rhoD, maximum):
+        """
+        Test that the list of kernels is correctly created.
+        """
+        kern_list = asam.kernels(Q_ref, rhoD, maximum)
+        assert len(kern_list) == 4
+        assert type(kern_list[0]) == asam.maxima_mean_kernel
+        assert type(kern_list[1]) == asam.rhoD_kernel
+        assert type(kern_list[2]) == asam.maxima_kernel
+        assert type(kern_list[3]) == asam.multi_dist_kernel
+
+class data_1D(object):
+    """
+    Sets up 1D data domain problem.
+    """
+    def createData(self):
+        """
+        Set up data.
+        """
+        self.data = np.random.random((100, 1))*10.0
+        self.Q_ref = np.array([5.0])
+        self.data_domain = np.expand_dims(np.array([0.0, 10.0]), axis=0)
+        self.mdim = 1
+        bin_size = 0.15*self.data_domain[:, 1]
+        self.maximum = 1/np.product(bin_size)
+        def ifun(outputs):
+            """
+            Indicator function
+            """
+            inside = np.logical_and(np.all(np.greater_equal(outputs,
+                self.Q_ref-.5*bin_size), axis=1), np.all(np.less_equal(outputs,
+                    self.Q_ref+.5*bin_size), axis=1)) 
+            max_values = np.repeat(self.maximum, outputs.shape[0], 0)
+            return inside.astype('float64')*max_values
+        self.rho_D = ifun
+
+class data_2D(object):
+    """
+    Sets up 2D data domain problem.
+    """
+    def createData(self):
+        """
+        Set up data.
+        """
+        self.data = np.random.random((100, 2))*10.0
+        self.Q_ref = np.array([5.0, 5.0])
+        self.data_domain = np.array([[0.0, 10.0], [0.0, 10.0]])
+        self.mdim = 2
+        bin_size = 0.15*self.data_domain[:, 1]
+        self.maximum = 1/np.product(bin_size)
+        def ifun(outputs):
+            """
+            Indicator function
+            """
+            inside = np.logical_and(np.all(np.greater_equal(outputs,
+                self.Q_ref-.5*bin_size), axis=1), np.all(np.less_equal(outputs,
+                    self.Q_ref+.5*bin_size), axis=1)) 
+            max_values = np.repeat(self.maximum, outputs.shape[0], 0)
+            return inside.astype('float64')*max_values
+        self.rho_D = ifun
+
+
+class data_3D(object):
+    """
+    Sets up 3D data domain problem.
+    """
+    def createData(self):
+        """
+        Set up data.
+        """
+        self.data = np.random.random((100, 3))*10.0
+        self.Q_ref = np.array([5.0, 5.0, 5.0])
+        self.data_domain = np.array([[0.0, 10.0], [0.0, 10.0], [0.0, 10.0]])
+        self.mdim = 3
+        bin_size = 0.15*self.data_domain[:, 1]
+        self.maximum = 1/np.product(bin_size)
+        def ifun(outputs):
+            """
+            Indicator function
+            """
+            inside = np.logical_and(np.all(np.greater_equal(outputs,
+                self.Q_ref-.5*bin_size), axis=1), np.all(np.less_equal(outputs,
+                    self.Q_ref+.5*bin_size), axis=1)) 
+            max_values = np.repeat(self.maximum, outputs.shape[0], 0)
+            return inside.astype('float64')*max_values
+        self.rho_D = ifun
+
+
+class kernel(object):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.kernel`
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        self.kernel = asam.kernel()
 
-@unittest.skip("Implement me")
-class test_transition_set(unittest.TestCase):
-    @unittest.skip("Implement me")
     def test_init(self):
-        pass
-    @unittest.skip("Implement me")
-    def test_step(self):
-        pass
+        """
+        Test the initalization of :class:`bet.sampling.adaptiveSampling.kernel`
+        """
+        assert self.kernel.TOL == 1e-8
+        assert self.kernel.increase == 1.0
+        assert self.kernel.decrease == 1.0
+
+    def test_delta_step(self):
+        """
+        Test the delta_step method of
+        :class:`bet.sampling.adaptiveSampling.kernel`
+        """
+        kern_new, proposal = self.kernel.delta_step(self.data)
+        assert kern_new == None
+        assert proposal.shape == (self.data.shape[0],)
+        
+
+class test_kernel_1D(kernel, data_1D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.kernel` on a 1D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_kernel_1D, self).createData()
+        super(test_kernel_1D, self).setUp()
+      
+class test_kernel_2D(kernel, data_2D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.kernel` on a 2D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_kernel_2D, self).createData()
+        super(test_kernel_2D, self).setUp()
+      
+class test_kernel_3D(kernel, data_3D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.kernel` on a 3D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_kernel_3D, self).createData()
+        super(test_kernel_3D, self).setUp()
+      
+
+class rhoD_kernel(kernel):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.rhoD_kernel`
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        self.kernel = asam.rhoD_kernel(self.maximum, self.rho_D)
 
-@unittest.skip("Implement me")
-class kernel(unittest.TestCase):
-    @unittest.skip("Implement me")
     def test_init(self):
-        pass
+        """
+        Test the initalization of :class:`bet.sampling.adaptiveSampling.kernel`
+        """
+        assert self.kernel.TOL == 1e-8
+        assert self.kernel.increase == 2.0
+        assert self.kernel.decrease == 0.5
+        assert self.kernel.MAX == self.maximum
+        assert self.kernel.rho_D == self.rho_D
+        assert self.kernel.sort_ascending == False
+
     def test_delta_step(self):
-        pass
+        """
+        Test the delta_step method of
+        :class:`bet.sampling.adaptiveSampling.kernel`
+        """
+        kern_new, proposal = self.kernel.delta_step(self.data)
+        nptest.assert_array_equal(kern_new, self.rho_D(self.data))
+        assert proposal == None 
+        
+        data = np.vstack([self.Q_ref+3.0, self.Q_ref, self.Q_ref-3.0])
+        data_new = np.vstack([self.Q_ref, self.Q_ref+3.0, self.Q_ref-3.0])
+        kern_old = self.rho_D(data)
+        kern_new, proposal = self.kernel.delta_step(data_new, kern_old)
+        nptest.assert_array_equal(proposal, [0.5, 2.0, 1.0])
+
+class test_rhoD_kernel_1D(rhoD_kernel, data_1D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.rhoD_kernel` on a 1D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_rhoD_kernel_1D, self).createData()
+        super(test_rhoD_kernel_1D, self).setUp()
+      
+class test_rhoD_kernel_2D(rhoD_kernel, data_2D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.rhoD_kernel` on a 2D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_rhoD_kernel_2D, self).createData()
+        super(test_rhoD_kernel_2D, self).setUp()
+      
+class test_rhoD_kernel_3D(rhoD_kernel, data_3D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.rhoD_kernel` on a 3D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_rhoD_kernel_3D, self).createData()
+        super(test_rhoD_kernel_3D, self).setUp()
 
 @unittest.skip("Implement me")
-class test_rhoD_kernel(kernel):
+class maxima_kernel(kernel):
+    """
+    Write me
+    """
     pass
 
 @unittest.skip("Implement me")
-class test_maxima_kernel(kernel):
+class maxima_mean_kernel(kernel):
+    """
+    Write me
+    """
     pass
 
 @unittest.skip("Implement me")
-class test_maxima_mean_kernel(kernel):
+class multi_dist_kernel(kernel):
+    """
+    Write me
+    """
     pass
 
+
 @unittest.skip("Implement me")
-class test_multi_dist_kernel(kernel):
-    pass
+class test_transition_set(unittest.TestCase):
+    """
+    Write me
+    """
+    @unittest.skip("Implement me")
+    def test_init(self):
+        """
+        Write me
+        """
+        pass
+    @unittest.skip("Implement me")
+    def test_step(self):
+        """
+        Write me
+        """
+        pass
 

From 88e28b7db6240c74c95b683abff95a62e5b0779a Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 22 Apr 2015 20:04:08 -0500
Subject: [PATCH 53/85] outlined maxima and maxima_mean kernel tests

---
 test/test_sampling/test_adaptiveSampling.py | 157 ++++++++++++++++++--
 1 file changed, 148 insertions(+), 9 deletions(-)

diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 19e6ecad..9e5421d0 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -602,7 +602,7 @@ def setUp(self):
 
     def test_init(self):
         """
-        Test the initalization of :class:`bet.sampling.adaptiveSampling.kernel`
+        Test the initalization of :class:`bet.sampling.adaptiveSampling.rhoD_kernel`
         """
         assert self.kernel.TOL == 1e-8
         assert self.kernel.increase == 2.0
@@ -614,7 +614,7 @@ def test_init(self):
     def test_delta_step(self):
         """
         Test the delta_step method of
-        :class:`bet.sampling.adaptiveSampling.kernel`
+        :class:`bet.sampling.adaptiveSampling.rhoD_kernel`
         """
         kern_new, proposal = self.kernel.delta_step(self.data)
         nptest.assert_array_equal(kern_new, self.rho_D(self.data))
@@ -659,19 +659,158 @@ def setUp(self):
         super(test_rhoD_kernel_3D, self).createData()
         super(test_rhoD_kernel_3D, self).setUp()
 
-@unittest.skip("Implement me")
 class maxima_kernel(kernel):
     """
-    Write me
+    Test :class:`bet.sampling.adaptiveSampling.maxima_kernel`
     """
-    pass
+    def setUp(self):
+        """
+        Set up
+        """
+        self.kernel = asam.maxima_kernel(np.vstack([self.Q_ref, self.Q_ref+.5]), self.rho_D)
 
-@unittest.skip("Implement me")
-class maxima_mean_kernel(kernel):
+    def test_init(self):
+        """
+        Test the initalization of :class:`bet.sampling.adaptiveSampling.maxima_kernel`
+        """
+        assert self.kernel.TOL == 1e-8
+        assert self.kernel.increase == 2.0
+        assert self.kernel.decrease == 0.5
+        nptest.assert_equal(self.kernel.MAXIMA, np.vstack([self.Q_ref,
+            self.Q_ref+.5]))
+        assert self.kernel.num_maxima == 2
+        nptest.assert_equal(self.kernel.rho_max,
+                self.rho_D(np.vstack([self.Q_ref, self.Q_ref+.5])))
+        assert self.kernel.sort_ascending == True
+
+    def test_delta_step(self):
+        """
+        Test the delta_step method of
+        :class:`bet.sampling.adaptiveSampling.maxima_kernel`
+        """
+        data_old = np.vstack([self.Q_ref+3.0, self.Q_ref, self.Q_ref-3.0])
+        kern_old, proposal = self.kernel.delta_step(data_old)
+
+        # TODO: check kern_old
+        #nptest.assert_array_equal(kern_old, np.zeros((self.data.shape[0],))
+        assert proposal == None 
+        
+        data_new = np.vstack([self.Q_ref, self.Q_ref+3.0, self.Q_ref-3.0])
+        kern_new, proposal = self.kernel.delta_step(data_new, kern_old)
+
+        #TODO: check kern_new
+        #nptest.assert_array_eqyal(kern_new, something)
+        nptest.assert_array_equal(proposal, [0.5, 2.0, 1.0])
+
+class test_maxima_kernel_1D(maxima_kernel, data_1D):
     """
-    Write me
+    Test :class:`bet.sampling.adaptiveSampling.maxima_kernel` on a 1D data space.
     """
-    pass
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_maxima_kernel_1D, self).createData()
+        super(test_maxima_kernel_1D, self).setUp()
+      
+class test_maxima_kernel_2D(maxima_kernel, data_2D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.maxima_kernel` on a 2D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_maxima_kernel_2D, self).createData()
+        super(test_maxima_kernel_2D, self).setUp()
+      
+class test_maxima_kernel_3D(maxima_kernel, data_3D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.maxima_kernel` on a 3D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_maxima_kernel_3D, self).createData()
+        super(test_maxima_kernel_3D, self).setUp()
+
+
+class maxima_mean_kernel(maxima_kernel):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.maxima_mean_kernel`
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        self.kernel = asam.maxima_mean_kernel(np.vstack([self.Q_ref, self.Q_ref+.5]), self.rho_D)
+
+    def test_init(self):
+        """
+        Test the initalization of
+        :class:`bet.sampling.adaptiveSampling.maxima_mean_kernel`
+        """
+        assert self.kernel.radius == None
+        assert self.kernel.mean == None
+        assert self.kernel.current_clength == 0
+        super(maxima_mean_kernel, self).test_init()
+
+    def test_reset(self):
+        """
+        Test the method
+        :meth:`bet.sampling.adaptiveSampling.maxima_mean_kernel.reset`
+        """
+        self.kernel.reset()
+        assert self.kernel.radius == None
+        assert self.kernel.mean == None
+        assert self.kernel.current_clength == 0
+
+    def test_delta_step(self):
+        """
+        Test the delta_step method of
+        :class:`bet.sampling.adaptiveSampling.maxima_mean_kernel`
+        """
+        super(maxima_mean_kernel, self).test_delta_step()
+        # TODO
+        # check self.current_clength
+        # check self.radius
+        # check self.mean
+        
+class test_maxima_mean_kernel_1D(maxima_mean_kernel, data_1D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.maxima_mean_kernel` on a 1D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_maxima_mean_kernel_1D, self).createData()
+        super(test_maxima_mean_kernel_1D, self).setUp()
+      
+class test_maxima_mean_kernel_2D(maxima_mean_kernel, data_2D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.maxima_mean_kernel` on a 2D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_maxima_mean_kernel_2D, self).createData()
+        super(test_maxima_mean_kernel_2D, self).setUp()
+      
+class test_maxima_mean_kernel_3D(maxima_mean_kernel, data_3D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.maxima_mean_kernel` on a 3D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_maxima_mean_kernel_3D, self).createData()
+        super(test_maxima_mean_kernel_3D, self).setUp()
+
+
 
 @unittest.skip("Implement me")
 class multi_dist_kernel(kernel):

From f692b0a8ebedccd846c64528b487cc6bb7612f39 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 23 Apr 2015 12:36:54 -0500
Subject: [PATCH 54/85] removed multi_dist_kernel

---
 bet/sampling/adaptiveSampling.py            | 105 --------------------
 test/test_sampling/test_adaptiveSampling.py |  12 +--
 2 files changed, 1 insertion(+), 116 deletions(-)

diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 6975ba03..cbad3bc7 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -17,8 +17,6 @@
 import math, os
 from bet.Comm import *
 
-size = comm.Get_size()
-rank = comm.Get_rank()
 
 def loadmat(save_file, lb_model=None):
     """
@@ -364,7 +362,6 @@ def kernels(Q_ref, rho_D, maximum):
     kern_list.append(maxima_mean_kernel(np.array([Q_ref]), rho_D))
     kern_list.append(rhoD_kernel(maximum, rho_D))
     kern_list.append(maxima_kernel(np.array([Q_ref]), rho_D))
-    kern_list.append(multi_dist_kernel())
     return kern_list
 
 class transition_set(object):
@@ -726,106 +723,4 @@ def delta_step(self, data_new, kern_old=None):
         return (kern_new, proposal)
 
 
-class multi_dist_kernel(kernel):
-    """
-    The goal is to make a sampling that is robust to different types of
-    distributions on QoI, i.e., we do not know a priori where the regions of
-    high probability are in D. This class provides a method for determining the
-    proposed step size as follows. We keep track of the change of the QoI
-    values from one sample to the next compared to the total range of QoI
-    values explored so far. If a big relative change is detected, then you know
-    that you have come across a region with larger derivatives and you should
-    place more samples around there to resolve the induced regions of
-    generalized contours, i.e., reduce the step size. If the change in QoI
-    values is relatively small, you are in a region where there is little
-    sensitivity, so take larger step sizes.
-
-    radius
-        current estimate of the radius of D (1/2 the diameter of D)
-    mean
-        current estimate of the mean QoI
-    current_clength
-        current batch number
-    TOL 
-        a tolerance used to determine if two different values are close
-    increase
-        the multiple to increase the step size by
-    decrease
-        the multiple to decrease the step size by
-
-    """
-
-    def __init__(self, tolerance=1E-08, increase=2.0, 
-            decrease=0.5):
-        """
-        Initialization
-        """
-        self.radius = None
-        self.mean = None
-        self.current_clength = 0
-        super(multi_dist_kernel, self).__init__(tolerance, increase,
-                decrease)
-
-    def reset(self):
-        """
-        Resets the the batch number and the estimates of the mean and maximum
-        distance from the mean.
-        """
-        self.radius = None
-        self.mean = None
-        self.current_clength = 0
-
-    def delta_step(self, data_new, kern_old=None):
-        """
-        This method determines the proposed change in step size. 
-        
-        :param data_new: QoI for a given batch of samples 
-        :type data_new: :class:`np.array` of shape (num_chains, mdim)
-        :param kern_old: QoI evaluated at previous step
-        :rtype: tuple
-        :returns: (kern_new, proposal)
-
-        """
-        # Evaluate kernel for new data.
-        kern_new = data_new
-        self.current_clength = self.current_clength + 1
-
-        if kern_old == None:
-            proposal = None
-            # calculate the mean
-            self.mean = np.mean(data_new, 0)
-            # calculate the distance from the mean
-            vec_from_mean = kern_new - np.repeat([self.mean],
-                    kern_new.shape[0], 0)
-            # estimate the radius of D
-            self.radius = np.max(np.linalg.norm(vec_from_mean, 2, 1)) 
-        else:
-            # update the estimate of the mean
-            self.mean = (self.current_clength-1)*self.mean + np.mean(data_new, 0)
-            self.mean = self.mean / self.current_clength
-            # calculate the distance from the mean
-            vec_from_mean = kern_new - np.repeat([self.mean],
-                    kern_new.shape[0], 0)
-            # esitmate the radius of D
-            self.radius = max(np.max(np.linalg.norm(vec_from_mean, 2, 1)),
-                    self.radius)
-            # calculate the relative change in QoI
-            kern_diff = (kern_new-kern_old)
-            # normalize by the radius of D
-            kern_diff = np.linalg.norm(vec_from_mean, 2, 1)#/self.radius
-            # Compare to kernel for old data.
-            # Is the kernel NOT close?
-            kern_close = np.logical_not(np.isclose(kern_diff, 0,
-                atol=self.TOL))
-            # Is the kernel greater/lesser?
-            kern_greater = np.logical_and(kern_diff > 0, kern_close)
-            kern_lesser = np.logical_and(kern_diff < 0, kern_close)
-            # Determine step size
-            proposal = np.ones(kern_diff.shape)
-            proposal[kern_greater] = self.decrease
-            proposal[kern_lesser] = self.increase
-        return (kern_new, proposal)
-
-
-
 
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 9e5421d0..30161c76 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -445,11 +445,10 @@ def verify_indiv(self, Q_ref, rhoD, maximum):
         Test that the list of kernels is correctly created.
         """
         kern_list = asam.kernels(Q_ref, rhoD, maximum)
-        assert len(kern_list) == 4
+        assert len(kern_list) == 3
         assert type(kern_list[0]) == asam.maxima_mean_kernel
         assert type(kern_list[1]) == asam.rhoD_kernel
         assert type(kern_list[2]) == asam.maxima_kernel
-        assert type(kern_list[3]) == asam.multi_dist_kernel
 
 class data_1D(object):
     """
@@ -811,15 +810,6 @@ def setUp(self):
         super(test_maxima_mean_kernel_3D, self).setUp()
 
 
-
-@unittest.skip("Implement me")
-class multi_dist_kernel(kernel):
-    """
-    Write me
-    """
-    pass
-
-
 @unittest.skip("Implement me")
 class test_transition_set(unittest.TestCase):
     """

From ca79016cf94058bebbeedd6fc5e6f7148c0c7579 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 23 Apr 2015 15:07:55 -0500
Subject: [PATCH 55/85] finished sampling tests, checking that tests run in
 parallel correctly

---
 test/test_sampling/test_adaptiveSampling.py | 84 ++++++++++++++++++---
 test/test_sampling/test_basicSampling.py    |  6 +-
 2 files changed, 79 insertions(+), 11 deletions(-)

diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 30161c76..74b23c3b 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -810,21 +810,87 @@ def setUp(self):
         super(test_maxima_mean_kernel_3D, self).setUp()
 
 
-@unittest.skip("Implement me")
-class test_transition_set(unittest.TestCase):
+class transition_set(object):
     """
-    Write me
+    Tests :class:`bet.sampling.adaptiveSamplinng.transition_set`
     """
-    @unittest.skip("Implement me")
+    def setUp(self):
+        """
+        Set Up
+        """
+        self.t_set = asam.transition_set(.5, .5**5, 1.0) 
+
     def test_init(self):
         """
-        Write me
+        Tests the initialization of :class:`bet.sampling.adaptiveSamplinng.transition_set`
         """
-        pass
-    @unittest.skip("Implement me")
+        assert self.t_set.init_ratio == .5
+        assert self.t_set.min_ratio == .5**5
+        assert self.t_set.max_ratio == 1.0
+
     def test_step(self):
         """
-        Write me
+        Tests the method
+        :meth:`bet.sampling.adaptiveSampling.transition_set.step`
+        """
+        # define step_ratio, param_width, param_left, param_right, samples_old
+        # from data
+        param_left = np.repeat([self.data_domain[:, 0]], self.data.shape[0], 0)
+        param_right = np.repeat([self.data_domain[:, 1]], self.data.shape[0], 0)
+        param_width = param_right - param_left
+
+        step_ratio = 0.5*np.ones(self.data.shape[0],)
+        step_ratio[self.data.shape[0]/2:] = .1
+        step_size = np.repeat([step_ratio], param_width.shape[1],
+                0).transpose()*param_width
+        # take a step
+        samples_new = self.t_set.step(step_ratio, param_width, param_left,
+                param_right, self.data)
+
+        # make sure the proposed steps are inside the domain
+        # check dimensions of samples
+        assert samples_new.shape == self.data.shape
+
+        # are the samples in bounds?
+        assert np.all(samples_new <= param_right)
+        assert np.all(samples_new >= param_left)
+
+        # make sure the proposed steps are inside the box defined around their
+        # generating old samples
+        assert np.all(samples_new <= self.data+0.5*step_size)
+        assert np.all(samples_new >= self.data-0.5*step_size)
+
+
+class test_transition_set_1D(transition_set, data_1D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.transition_set` on a 1D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_transition_set_1D, self).createData()
+        super(test_transition_set_1D, self).setUp()
+      
+class test_transition_set_2D(transition_set, data_2D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.transition_set` on a 2D data space.
+    """
+    def setUp(self):
+        """
+        Set up
+        """
+        super(test_transition_set_2D, self).createData()
+        super(test_transition_set_2D, self).setUp()
+      
+class test_transition_set_3D(transition_set, data_3D):
+    """
+    Test :class:`bet.sampling.adaptiveSampling.transition_set` on a 3D data space.
+    """
+    def setUp(self):
+        """
+        Set up
         """
-        pass
+        super(test_transition_set_3D, self).createData()
+        super(test_transition_set_3D, self).setUp()
 
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index 135fcf8f..73727a3d 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -64,13 +64,15 @@ def test_loadmat():
     sio.savemat(os.path.join(local_path, 'testfile1'), mdat1)
     sio.savemat(os.path.join(local_path, 'testfile2'), mdat2)
 
-    (loaded_sampler1, samples1, data1) = bsam.loadmat('testfile1')
+    (loaded_sampler1, samples1, data1) = bsam.loadmat(os.path.join(local_path,
+        'testfile1'))
     nptest.assert_array_equal(samples1, mdat1['samples'])
     nptest.assert_array_equal(data1, mdat1['data'])
     assert loaded_sampler1.num_samples == 5
     assert loaded_sampler1.lb_model == None
 
-    (loaded_sampler2, samples2, data2) = bsam.loadmat('testfile2', model)
+    (loaded_sampler2, samples2, data2) = bsam.loadmat(os.path.join(local_path,
+        'testfile2'), model)
     nptest.assert_array_equal(samples2, mdat2['samples'])
     nptest.assert_array_equal(data2, None)
     assert loaded_sampler2.num_samples == 6

From 36a9de1ee8f9c887a7e64774f4872ffa9daf8614 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 23 Apr 2015 15:37:39 -0500
Subject: [PATCH 56/85] fixed error in util get_global_arrays when shape is
 provided

---
 bet/util.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/bet/util.py b/bet/util.py
index 4cd9d188..9bbe7f80 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -70,7 +70,6 @@ def get_global_values(array, shape=None):
     # Figure out the subtype of the elements of the array
     dtype = array.dtype
     mpi_dtype = False
-    print dtype
     for ptype in possible_types.iterkeys():
         if np.issubdtype(dtype, ptype):
             mpi_dtype = True
@@ -87,10 +86,9 @@ def get_global_values(array, shape=None):
     else:
         # do an uppercase Allgather
         whole_a = np.empty(shape, dtype=dtype)
-        print dtype
         comm.Allgather([array, possible_types[dtype]], [whole_a,
             possible_types[dtype]])
-        return array
+        return whole_a
 
 def fix_dimensions_vector(vector):
     """
@@ -136,10 +134,7 @@ def fix_dimensions_domain(domain):
     """
     if not isinstance(domain, np.ndarray):
         if len(domain) == 2:
-            print domain
             domain = np.expand_dims(domain, axis=0)
-            print domain
-            print domain.shape
         else:
             raise TypeError("The length must be at least 2.")
     elif len(domain.shape) == 1 and domain.shape[0] == 2:

From 3403b8bc8904eb56666f982a948ecf7b1bf09579 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 23 Apr 2015 17:19:45 -0500
Subject: [PATCH 57/85] fixed loadmat issues, need to make sure all *.mat files
 are properly cleaned up from test_adaptiveSampling

---
 bet/sampling/adaptiveSampling.py            |  4 +++-
 test/test_sampling/test_adaptiveSampling.py | 14 +++++++++++---
 test/test_sampling/test_basicSampling.py    |  4 ++++
 3 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index cbad3bc7..029e9d6a 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -35,15 +35,17 @@ def loadmat(save_file, lb_model=None):
     # load the samples
     if mdat.has_key('samples'):
         samples = mdat['samples']
+        num_samples = samples.shape[0]
     else:
         samples = None
+        num_samples = np.squeeze(mdat['num_samples'])
     # load the data
     if mdat.has_key('data'):
         data = mdat['data']
     else:
         data = None
     # recreate the sampler
-    new_sampler = sampler(mdat['num_samples'],
+    new_sampler = sampler(num_samples,
             np.squeeze(mdat['chain_length']), lb_model)
     
     return (new_sampler, samples, data)
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 74b23c3b..b31b6383 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -16,16 +16,18 @@
 local_path = os.path.join(os.path.dirname(bet.__file__),
     "../test/test_sampling")
 
+@unittest.skipIf(size > 1, 'Only run in serial')
 def test_loadmat_init():
     """
     Tests :meth:`bet.sampling.adaptiveSampling.loadmat` and
     :meth:`bet.sampling.adaptiveSampling.sampler.init`.
     """
+    np.random.seed(1)
     chain_length = 10
-    mdat1 = {'samples':np.random.random((5, 1)),
-            'data':np.random.random((5, 1)), 'num_samples':50,
+    mdat1 = {'samples':np.random.random((50, 1)),
+            'data':np.random.random((50, 1)), 'num_samples':50,
             'chain_length':chain_length} 
-    mdat2 = {'samples':np.random.random((6, 1)),
+    mdat2 = {'samples':np.random.random((60, 1)),
                     'num_samples':60, 'chain_length':chain_length}
     model = "this is not a model"
     
@@ -211,8 +213,14 @@ def tearDown(self):
             for f in self.savefiles:
                 proc_savefile = os.path.join(local_path, os.path.dirname(f),
                         "proc{}{}".format(rank, os.path.basename(f)))
+                print proc_savefile
                 if os.path.exists(proc_savefile):
                     os.remove(proc_savefile)
+                proc_savefile = os.path.join(local_path, os.path.dirname(f),
+                        "p{}proc{}{}".format(rank, rank, os.path.basename(f)))
+                if os.path.exists(proc_savefile):
+                    os.remove(proc_savefile)
+                print proc_savefile
 
     def test_update(self):
         """
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index 73727a3d..b51cd3f5 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -9,6 +9,7 @@
 import bet.sampling.basicSampling as bsam
 import scipy.io as sio
 import bet
+from bet.Comm import size
 
 local_path = os.path.join(os.path.dirname(bet.__file__), "../test/test_sampling")
 
@@ -52,10 +53,13 @@ def rho_D(my_data):
     nptest.assert_array_equal(np.array([3, 3, 3, 3]),
             bsam.in_high_prob_multi(results_list, rho_D, maximum))
 
+
+@unittest.skipIf(size > 1, 'Only run in serial')
 def test_loadmat():
     """
     Tests :meth:`bet.sampling.basicSampling.loadmat`
     """
+    np.random.seed(1)
     mdat1 = {'samples':np.random.random((5,1)),
             'data':np.random.random((5,1)), 'num_samples':5}
     mdat2 = {'samples':np.random.random((6,1)), 'num_samples':6}

From 284790215ecc167c12406748a73a57f317f1a9d7 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 27 Apr 2015 12:23:56 -0500
Subject: [PATCH 58/85] now files from test_adaptiveSampling.py are being
 properly cleaned up

---
 test/test_sampling/test_adaptiveSampling.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index b31b6383..a1310efd 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -212,12 +212,12 @@ def tearDown(self):
         if size > 1:
             for f in self.savefiles:
                 proc_savefile = os.path.join(local_path, os.path.dirname(f),
-                        "proc{}{}".format(rank, os.path.basename(f)))
+                        "proc{}{}.mat".format(rank, os.path.basename(f)))
                 print proc_savefile
                 if os.path.exists(proc_savefile):
                     os.remove(proc_savefile)
                 proc_savefile = os.path.join(local_path, os.path.dirname(f),
-                        "p{}proc{}{}".format(rank, rank, os.path.basename(f)))
+                        "p{}proc{}{}.mat".format(rank, rank, os.path.basename(f)))
                 if os.path.exists(proc_savefile):
                     os.remove(proc_savefile)
                 print proc_savefile

From 11408b21fae8c707a41a8b79e9789d56445b598e Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 27 Apr 2015 14:55:52 -0500
Subject: [PATCH 59/85] updated docstrings to make sure matches method/function
 calls etc

---
 bet/calculateP/__init__.py         | 16 ++++----
 bet/calculateP/calculateP.py       | 14 +++----
 bet/calculateP/simpleFunP.py       | 63 ++++++++++++++----------------
 bet/calculateP/voronoiHistogram.py | 32 +++++++++------
 bet/postProcess/postTools.py       |  2 +-
 bet/sampling/adaptiveSampling.py   | 61 +++++++++++++++++++++++++----
 bet/sampling/basicSampling.py      | 12 ++++--
 doc/bet.rst                        |  1 -
 8 files changed, 128 insertions(+), 73 deletions(-)

diff --git a/bet/calculateP/__init__.py b/bet/calculateP/__init__.py
index 62c859d3..5bd16370 100644
--- a/bet/calculateP/__init__.py
+++ b/bet/calculateP/__init__.py
@@ -1,14 +1,14 @@
-r"""
-This subpackage provides classes and methods for calulating the probability
-measure :math:`P_{\Lambda}`.
+r""" 
+This subpackage provides classes and methods for calulating the
+probability measure :math:`P_{\Lambda}`.
 
-* :mod:`~bet.calculateP.calculateP` provides methods for approximating probability
-    densities 
-* :mod:`~bet.calculateP.simpleFunP` provides methods for creating simple function
-    approximations of probability densisties
+* :mod:`~bet.calculateP.calculateP` provides methods for approximating
+     probability densities 
+* :mod:`~bet.calculateP.simpleFunP` provides methods for creating simple
+    function approximations of probability densisties
 * :mod:`~bet.calculateP.voronoiHistogram` provides methods for creating the
     generating points for cells that define a regular grid for use by
     :meth:`numpy.histogramdd` and for determining their volumes, etc. This
-    module is only for use by :mod:`~bet.calculateP.simpleFunP`.
+    module is only for use by :mod:`~bet.calculateP.simpleFunP`.  
 """
 __all__ = ['calculateP', 'simpleFunP', 'voronoiHistogram']
diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index 73aeebca..232205a1 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -1,6 +1,6 @@
-r"""
-This module provides methods for calulating the probability
-measure :math:`P_{\Lambda}`.
+r""" 
+This module provides methods for calulating the probability measure
+:math:`P_{\Lambda}`.
 
 * :mod:`~bet.calculateP.prob_emulated` provides a skeleton class and calculates
     the probability for a set of emulation points.
@@ -17,8 +17,8 @@
 
 def emulate_iid_lebesgue(lam_domain, num_l_emulate):
     """
-    Parition the parameter space using emulated samples into many voronoi cells.
-    These samples are iid so that we can apply the standard MC                                       
+    Parition the parameter space using emulated samples into many voronoi
+    cells. These samples are iid so that we can apply the standard MC                                       
     assumuption/approximation
 
     :param lam_domain: The domain for each parameter for the model.
@@ -170,8 +170,8 @@ def prob_mc(samples, data, rho_D_M, d_distr_samples,
         parition of D to for the simple function approximation
     :type d_distr_samples: :class:`~numpy.ndarray` of shape  (M, mdim) 
     :param d_Tree: :class:`~scipy.spatial.KDTree` for d_distr_samples
-    :param int num_l_emulate: The number of iid samples used to parition the
-        parameter space
+    :param lambda_emulate: Samples used to partition the parameter space
+
     :rtype: tuple of :class:`~numpy.ndarray` of sizes (num_samples,),
         (num_samples,), (ndim, num_l_emulate), (num_samples,), (num_l_emulate,)
     :returns: (P, lam_vol, lambda_emulate, io_ptr, emulate_ptr) where P is the
diff --git a/bet/calculateP/simpleFunP.py b/bet/calculateP/simpleFunP.py
index 70469a72..9819ffde 100644
--- a/bet/calculateP/simpleFunP.py
+++ b/bet/calculateP/simpleFunP.py
@@ -50,30 +50,29 @@ def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
     the :class:`~scipy.spatial.KDTree` for d_distr_samples
     """
     data = util.fix_dimensions_data(data)
-    bin_size = (np.max(data, 0) - np.min(data,0))*bin_ratio
+    bin_size = (np.max(data, 0) - np.min(data, 0))*bin_ratio
 
     r'''
     Create M samples defining M Voronoi cells (i.e., "bins") in D used to 
     define the simple function approximation :math:`\rho_{\mathcal{D},M}`.
     
     This does not have to be random, but here we assume this to be the case.
-    We can choose these samples deterministically but that fails to scale 
-    with dimension efficiently.
+    We can choose these samples deterministically but that fails to scale with
+    dimension efficiently.
     
     Note that these M samples are chosen for the sole purpose of determining
     the bins used to create the approximation to :math:`rho_{\mathcal{D}}`.
     
-    We call these M samples "d_distr_samples" because they are samples
-    on the data space and the distr implies these samples are chosen
-    to create the approximation to the probability measure (distribution) 
-    on D.
+    We call these M samples "d_distr_samples" because they are samples on the
+    data space and the distr implies these samples are chosen to create the
+    approximation to the probability measure (distribution) on D.
     
-    Note that we create these samples in a set containing the hyperrectangle
-    in order to get output cells with zero probability. If all of the
-    d_dstr_samples were taken from within the support of :math:`\rho_{\mathcal{D}}`
-    then each of the M bins would have positive probability. This would
-    in turn imply that the support of :math:`\rho_{\Lambda}` is all of
-    :math:`\Lambda`.
+    Note that we create these samples in a set containing the hyperrectangle in
+    order to get output cells with zero probability. If all of the
+    d_dstr_samples were taken from within the support of
+    :math:`\rho_{\mathcal{D}}` then each of the M bins would have positive
+    probability. This would in turn imply that the support of
+    :math:`\rho_{\Lambda}` is all of :math:`\Lambda`.
     '''
     if rank == 0:
         d_distr_samples = 1.5*bin_size*(np.random.random((M,
@@ -128,7 +127,6 @@ def normal_normal(Q_ref, M, std, num_d_emulate=1E6):
         :math:`\rho_{\mathcal{D},M}` The choice of M is something of an "art" -
         play around with it and you can get reasonable results with a
         relatively small number here like 50. 
-    :param:w
     int num_d_emulate: Number of samples used to emulate using an MC
         assumption 
     :param Q_ref: :math:`Q(\lambda_{reference})`
@@ -226,7 +224,6 @@ def unif_normal(Q_ref, M, std, num_d_emulate=1E6):
     the :class:`~scipy.spatial.KDTree` for d_distr_samples
 
     """
-    import scipy.stats as stats
     r'''Create M smaples defining M bins in D used to define
     :math:`\rho_{\mathcal{D},M}` rho_D is assumed to be a multi-variate normal
     distribution with mean Q_ref and standard deviation std.'''
@@ -310,29 +307,28 @@ def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1)
     r"""
     Creates a simple function approximation of :math:`\rho_{\mathcal{D},M}`
     where :math:`\rho_{\mathcal{D},M}` is a uniform probability density
-    centered at Q_ref with bin_size of the width
-    of D.
+    centered at Q_ref with bin_size of the width of D.
 
     Since rho_D is a uniform distribution on a hyperrectanlge we should be able
     to represent it exactly with ``M = 3^mdim`` or rather
     ``len(d_distr_samples) == 3^mdim``.
 
-    :param bin_size: The size used to determine the width of the
-        uniform distribution 
-    :type bin_size: double or list()
-    :param int num_d_emulate: Number of samples used to emulate using an MC
+    :param bin_size: The size used to determine the width of the uniform
+        distribution 
+    :type bin_size: double or list() 
+    :param int num_d_emulate: Number of samples used to emulate using an MC 
         assumption 
-    :param data: Array containing QoI data where the QoI is mdim diminsional
-    :type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
-    :param Q_ref: :math:`Q(\lambda_{reference})`
-    :type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
-    :param list() center_pts_per_edge: number of center points per edge and
-        additional two points will be added to create the bounding layer
-
-    :rtype: tuple
-    :returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
-        ``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
-        is the :class:`~scipy.spatial.KDTree` for d_distr_samples
+    :param data: Array containing QoI data where the QoI is mdim diminsional 
+    :type data: :class:`~numpy.ndarray` of size (num_samples, mdim) 
+    :param Q_ref: :math:`Q(\lambda_{reference})` 
+    :type Q_ref: :class:`~numpy.ndarray` of size (mdim,) 
+    :param list() center_pts_per_edge: number of center points per edge
+        and additional two points will be added to create the bounding layer
+
+    :rtype: tuple :returns: (rho_D_M, d_distr_samples, d_Tree) where
+    ``rho_D_M`` is (M,) and ``d_distr_samples`` are (M, mdim)
+    :class:`~numpy.ndarray` and `d_Tree` is the :class:`~scipy.spatial.KDTree`
+    for d_distr_samples
 
     """
     data = util.fix_dimensions_data(data)
@@ -351,7 +347,7 @@ def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1)
     if np.any(np.less(bin_size, 0)):
             print 'Warning: center_pts_per_edge must be greater than 0'
 
-    sur_domain = np.array([np.min(data, 0),np.max(data, 0)]).transpose()
+    sur_domain = np.array([np.min(data, 0), np.max(data, 0)]).transpose()
 
     points, _, rect_domain = vHist.center_and_layer1_points_binsize(center_pts_per_edge, 
             Q_ref, bin_size, sur_domain)
@@ -393,7 +389,6 @@ def uniform_hyperrectangle(data, Q_ref, bin_ratio, center_pts_per_edge=1):
     if not isinstance(bin_ratio, collections.Iterable):
         bin_ratio = bin_ratio*np.ones((data.shape[1], ))
 
-    sur_domain = np.array([np.min(data, 0),np.max(data, 0)]).transpose()
     bin_size = (np.max(data, 0) - np.min(data, 0))*bin_ratio 
     return uniform_hyperrectangle_binsize(data, Q_ref, bin_size,
             center_pts_per_edge)
diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index a9641d23..b7932168 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -1,9 +1,16 @@
 # -*- coding: utf-8 -*-
+"""
+This module provides methods for creating the edges and generating point for
+regular (hyperrectangle) multidimensional voronoi cells and for determining the
+volumes of these cells.
+"""
+
 import numpy as np
 from scipy import spatial
 import bet.util as util
 
-def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_domain):
+def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size,
+        sur_domain): 
     """
     Generates a regular grid of center points that define the voronoi
     tesselation of exactly the interior of a hyperrectangle centered at
@@ -27,15 +34,15 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size, sur_do
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
 
     :rtype: tuple
-    :returns: (points, interior_and_layer1, rect_domain) where where points is an
-        :class:`numpy.ndarray` of shape (num_points, dim), interior_and_layer1
-        is a list() of dim :class:`numpy.ndarray`s of shape
+    :returns: (points, interior_and_layer1, rect_domain) where where points is
+        an :class:`numpy.ndarray` of shape (num_points, dim),
+        interior_and_layer1 is a list() of dim :class:`numpy.ndarray`s of shape
         (center_pts_per_edge+2,), rect_domain is a :class:`numpy.ndarray` of
         shape (mdim, 2)
 
     """
     # determine the hyperrectangle (rect_domain) defined by center and r_size
-    rect_width = r_size*np.ones(sur_domain[:,0].shape)
+    rect_width = r_size*np.ones(sur_domain[:, 0].shape)
     rect_domain = np.column_stack([center - .5*rect_width,
         center + .5*rect_width])
     if np.all(np.greater(r_size, rect_width)):
@@ -82,9 +89,9 @@ def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
     :type sur_domain: :class:`numpy.ndarray` of shape (mdim, 2)
 
     :rtype: tuple
-    :returns: (points, interior_and_layer1, rect_domain) where where points is an
-        :class:`numpy.ndarray` of shape (num_points, dim), interior_and_layer1
-        is a list() of dim :class:`numpy.ndarray`s of shape
+    :returns: (points, interior_and_layer1, rect_domain) where where points is
+        an :class:`numpy.ndarray` of shape (num_points, dim),
+        interior_and_layer1 is a list() of dim :class:`numpy.ndarray`s of shape
         (center_pts_per_edge+2,), rect_domain is a :class:`numpy.ndarray` of
         shape (mdim, 2).
 
@@ -191,8 +198,9 @@ def histogramdd_volumes(edges, points):
 
     :rtype: tuple of (H, volume, edges)
     :returns: H is the result of :meth:`np.histogramdd(points, edges,
-        normed=True)`, volumes is a :class:`numpy.ndarray` of shape (len(points),)
-        continaing the  finite volumes associated with ``points``
+        normed=True)`, volumes is a :class:`numpy.ndarray` of shape
+        (len(points),) continaing the  finite volumes associated with
+        ``points``
 
     """
     # adjust edges
@@ -244,8 +252,8 @@ def simple_fun_uniform(points, volumes, rect_domain):
         axis=1), np.all(np.less_equal(points, rect_domain[:, 1]), axis=1)) 
 
     rho_D_M = np.zeros(volumes.shape)
-    rho_D_M[inside] = volumes[inside]/np.sum(volumes[inside]) # normalize on Lambda not D
-
+    # normalize on Lambda not D
+    rho_D_M[inside] = volumes[inside]/np.sum(volumes[inside]) 
     d_Tree = spatial.KDTree(points)
     return (rho_D_M, points, d_Tree)
 
diff --git a/bet/postProcess/postTools.py b/bet/postProcess/postTools.py
index e1e9c842..6f9dd65d 100644
--- a/bet/postProcess/postTools.py
+++ b/bet/postProcess/postTools.py
@@ -40,7 +40,7 @@ def sort_by_rho(P_samples, samples, lam_vol=None, data=None):
     if data != None:
         if len(data.shape) == 1:
             data = np.expand_dims(data, axis=1)
-        data = data[indices,:]
+        data = data[indices, :]
 
     return (P_samples, samples, lam_vol, data)
 
diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 029e9d6a..3ef7378d 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -24,8 +24,8 @@ def loadmat(save_file, lb_model=None):
     :class:`~bet.sampling.adaptiveSampling.sampler` object.
 
     :param string save_file: file name
-    :param model: runs the model at a given set of parameter samples and
-        returns data 
+    :param lb_model: runs the model at a given set of parameter samples, (N,
+        ndim), and returns data (N, mdim)
     :rtype: tuple
     :returns: (sampler, samples, data)
 
@@ -65,6 +65,11 @@ class sampler(bsam.sampler):
     def __init__(self, num_samples, chain_length, lb_model):
         """
         Initialization
+
+        :param int num_samples: Total number of samples
+        :param int chain_length: Number of samples per chain
+        :param lb_model: runs the model at a given set of parameter samples, (N,
+            ndim), and returns data (N, mdim)
         """
         super(sampler, self).__init__(lb_model, num_samples)
         self.chain_length = chain_length
@@ -356,8 +361,7 @@ def kernels(Q_ref, rho_D, maximum):
         a class:`np.ndarray`
     :param double maximum: maximum value of rho_D
     :rtype: list()
-    :returns: [maxima_mean_kernel, rhoD_kernel, maxima_kernel,
-        multi_dist_kernel]
+    :returns: [maxima_mean_kernel, rhoD_kernel, maxima_kernel]
 
     """
     kern_list = list()
@@ -387,6 +391,11 @@ class transition_set(object):
     def __init__(self, init_ratio, min_ratio, max_ratio):
         """
         Initialization
+
+        :param double init_ratio: initial step ratio
+        :param double min_ratio: minimum step_ratio
+        :param double max_ratio: maximum step_ratio
+
         """
         self.init_ratio = init_ratio
         self.min_ratio = min_ratio
@@ -402,7 +411,13 @@ def step(self, step_ratio, param_width, param_left, param_right,
         :param step_ratio: define maximum step_size = ``step_ratio*param_width``
         :type step_ratio: :class:`np.array` of shape (num_samples,)
         :param param_width: width of the parameter domain
-        :type param_width: np.array (ndim,)
+        :type param_width: :class:`np.ndarray` of shape (ndim,)
+        :param param_left: minimum boundary of the parameter domain
+        :type param_left: :class:`np.ndarray` of shape (ndim, N) where N is the
+            length of ``step_ratio``
+        :param param_right: maximum boundary of the parameter domain
+        :type param_right: :class:`np.ndarray` of shape (ndim, N) where N is the
+            length of ``step_ratio``
         :param samples_old: Parameter samples from the previous step.
         :type samples_old: :class:`~numpy.ndarray` of shape (num_samples,
             ndim)
@@ -438,7 +453,7 @@ class kernel(object):
     this is simply a skeleton parent class it does not change the step size at
     all.
     
-    tolerance
+    TOL
         a tolerance used to determine if two different values are close
     increase
         the multiple to increase the step size by
@@ -449,6 +464,11 @@ class kernel(object):
     def __init__(self, tolerance=1E-08, increase=1.0, decrease=1.0):
         """
         Initialization
+
+        :param double tolerance: Tolerance for comparing two values
+        :param double increase: The multiple to increase the step size by
+        :param double decrease: The multiple to decrease the step size by
+
         """
         self.TOL = tolerance
         self.increase = increase
@@ -496,6 +516,13 @@ def __init__(self, maximum, rho_D, tolerance=1E-08, increase=2.0,
             decrease=0.5):
         """
         Initialization
+
+        :param double maximum: maximum value of rho_D
+        :param function rho_D: probability density on D
+        :param double tolerance: Tolerance for comparing two values
+        :param double increase: The multiple to increase the step size by
+        :param double decrease: The multiple to decrease the step size by
+
         """
         self.MAX = maximum
         self.rho_D = rho_D
@@ -549,7 +576,7 @@ class maxima_kernel(kernel):
 
     maxima
         locations of the maxima of rho_D on D
-        np.array of shape (num_maxima, mdim)
+        :class:`np.nsarray` of shape (num_maxima, mdim)
     rho_max
         rho_D(maxima), list of maximum values of rho_D
     tolerance 
@@ -565,6 +592,16 @@ def __init__(self, maxima, rho_D, tolerance=1E-08, increase=2.0,
             decrease=0.5):
         """
         Initialization
+
+        :param maxima: locations of the maxima of rho_D on D 
+        :type maxima: :class:`np.ndarray` of chape (num_maxima, mdim)
+        :param rho_D: probability density on D
+        :type rho_D: callable function that takes a :class:`np.array` and returns
+            a class:`np.ndarray`
+        :param double tolerance: Tolerance for comparing two values
+        :param double increase: The multiple to increase the step size by
+        :param double decrease: The multiple to decrease the step size by
+
         """
         self.MAXIMA = maxima
         self.num_maxima = maxima.shape[0]
@@ -644,6 +681,16 @@ def __init__(self, maxima, rho_D, tolerance=1E-08, increase=2.0,
             decrease=0.5):
         """
         Initialization
+
+        :param maxima: locations of the maxima of rho_D on D 
+        :type maxima: :class:`np.ndarray` of chape (num_maxima, mdim)
+        :param rho_D: probability density on D
+        :type rho_D: callable function that takes a :class:`np.array` and returns
+            a class:`np.ndarray`
+        :param double tolerance: Tolerance for comparing two values
+        :param double increase: The multiple to increase the step size by
+        :param double decrease: The multiple to decrease the step size by
+
         """
         self.radius = None
         self.mean = None
diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index 7c2439dd..43cee957 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -28,6 +28,7 @@ def compare_yield(sort_ind, sample_quality, run_param, column_headings=None):
         samples are sorted
     :param list() run_param: zipped list of :class:`~numpy.ndarray`s containing
         information used to generate the sets of samples to be displayed
+    :param list() column_headings: Column headings to print to screen
 
     """
     if column_headings == None:
@@ -47,6 +48,7 @@ def in_high_prob(data, rho_D, maximum, sample_nos=None):
     :param rho_D: probability density on D
     :type rho_D: callable function that takes a :class:`np.array` and returns a
         :class:`np.ndarray`
+    :param float maximum: maximum (or average) value of ``rho_D``
     :param list sample_nos: sample numbers to plot
 
     :rtype: int
@@ -74,6 +76,7 @@ def in_high_prob_multi(results_list, rho_D, maximum, sample_nos_list=None):
     :param rho_D: probability density on D
     :type rho_D: callable function that takes a :class:`np.array` and returns a
         :class:`np.ndarray`
+    :param float maximum: maximum (or average) value of ``rho_D``
     :param list sample_nos_list: list of sample numbers to plot (list of lists)
 
     :rtype: list of int
@@ -134,6 +137,10 @@ class sampler(object):
     def __init__(self, lb_model, num_samples=None):
         """
         Initialization
+        
+        :param lb_model: Interface to physics-based model takes an input of
+            shape (N, ndim) and returns an output of shape (N, mdim)
+        :param int num_samples: N, number of samples (optional)
         """
         self.num_samples = num_samples
         self.lb_model = lb_model
@@ -178,6 +185,7 @@ def random_samples(self, sample_type, param_min, param_max,
         :param param_max: maximum value for each parameter dimension
         :type param_max: np.array (ndim,)
         :param string savefile: filename to save samples and data
+        :param int num_samples: N, number of samples (optional)
         :param string criterion: latin hypercube criterion see 
             `PyDOE <http://pythonhosted.org/pyDOE/randomized.html>`_
         :param boolean parallel: Flag for parallel implementation. Uses
@@ -227,14 +235,12 @@ def user_samples(self, samples, savefile, parallel=False):
         
         # Update the number of samples
         self.num_samples = samples.shape[0]
-        size = comm.Get_size()
-        rank = comm.Get_rank()
 
         # Solve the model at the samples
         if not(parallel) or size == 1:
             data = self.lb_model(samples)
         elif parallel and self.num_samples%size == 0:
-            if len(samples.shape)  == 1:
+            if len(samples.shape) == 1:
                 my_samples = np.empty((samples.shape[0]/size, ))
             else:
                 my_samples = np.empty((samples.shape[0]/size, samples.shape[1]))
diff --git a/doc/bet.rst b/doc/bet.rst
index cd1d45fe..971c45d9 100644
--- a/doc/bet.rst
+++ b/doc/bet.rst
@@ -7,7 +7,6 @@ Subpackages
 .. toctree::
 
     bet.calculateP
-    bet.loadBalance
     bet.postProcess
     bet.sampling
 

From 6cd97a3bb6b88252c698054c7541e97bc9d86344 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 27 Apr 2015 15:31:03 -0500
Subject: [PATCH 60/85] updateing docmentation to address issue #10

---
 doc/index.rst    |  1 +
 doc/overview.rst | 22 +++++++++-------------
 doc/parallel.rst | 49 ++++++++++++++++++++++++++++++++++++++++++++++++
 setup.py         |  5 ++++-
 4 files changed, 63 insertions(+), 14 deletions(-)
 create mode 100644 doc/parallel.rst

diff --git a/doc/index.rst b/doc/index.rst
index 82bd345c..3b852567 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -13,6 +13,7 @@ Contents:
    :glob:
 
    overview
+   parallel
    examples/*
    examples/linearMap/*
    examples/fromFileMap/*
diff --git a/doc/overview.rst b/doc/overview.rst
index 579a324e..26ea4e36 100644
--- a/doc/overview.rst
+++ b/doc/overview.rst
@@ -8,9 +8,9 @@ Installation
 ------------
 
 The code currently resides at `GitHub
-<https://github.com/smattis/BET>`_.
+<https://github.com/UT-CHG/BET>`_.
 If you have a 
-`zip file <https://github.com/smattis/BET/archive/master.zip>`_ you can install
+`zip file <https://github.com/UT-CHG/BET/archive/master.zip>`_ you can install
 BET using::
 
     python setup.py install
@@ -28,20 +28,16 @@ The package layout is as follows::
 
   bet/
     calculateP/
-      calculateP.py  
-      simpleFunP.py  
-      voronoiHistogram.py
+      calculateP 
+      simpleFunP
+      voronoiHistogram
     sampling/
-      basicSampling.py  
-      adaptiveSampling.py
+      basicSampling 
+      adaptiveSampling
     postProcess/
-      plotP.py  
-      plotDomains.py 
+      plotP
+      plotDomains
       postTools
-    loadBalance/        # !!! IN ACTIVE DEVELOPMENT !!!
-      lb_PADCIRC.py  
-      load_balance.py  
-      lb_DIAMOND.py
 
 Code Overview
 --------------
diff --git a/doc/parallel.rst b/doc/parallel.rst
new file mode 100644
index 00000000..c67d7a06
--- /dev/null
+++ b/doc/parallel.rst
@@ -0,0 +1,49 @@
+.. _parallel:
+
+========
+Parallel
+========
+
+Installation
+------------
+
+Running this code in parallel requires the installation of `MPI for Python
+<http://mpi4py.scipy.org/>`_ which requires that your system has mpi installed.
+
+Affected Packages
+-----------------
+
+The modules that have parallel capabilities are as follows::
+
+  bet/
+    util
+    calculateP/
+      calculateP
+      simpleFunP
+    sampling/
+      basicSampling 
+      adaptiveSampling
+    postProcess/
+      plotP  
+      postTools
+
+util
+~~~~
+The module :mod:`~bet.util` provides the method
+:meth:`~bet.util.get_global_values` to globalize local arrays into an array of
+global values on all processors.
+
+calculateP
+~~~~~~~~~~
+All methods in the module :mod:`~bet.calculateP.calculateP` benifit from
+parallel execution. Only local arrays are returned for ``P``, use
+:meth:`~bet.util.get_global_values` to globalize local arrays.
+
+In the module :mod:`~bet.calculateP.simpleFunP` the methods
+:meth:`~bet.calculateP.simpleFunP.unif_unif`,
+:meth:`~bet.calculateP.simpleFunP.normal_normal`, and 
+:meth:`~bet.calculateP.simpleFunP.unif_normal` benifit from parallel
+execution.
+
+sampling
+~~~~~~~~
diff --git a/setup.py b/setup.py
index 5a4cedd8..f45b989b 100644
--- a/setup.py
+++ b/setup.py
@@ -9,5 +9,8 @@
       description='Butler, Estep, Tavener method',
       author='Steven Mattis',
       author_email='steve.a.mattis@gmail.com',
+      liscense='GNU LGPL',
       url='https://github.com/UT-CHG/BET',
-      packages=['bet', 'bet.sampling', 'bet.calculateP', 'bet.postProcess'])
+      packages=['bet', 'bet.sampling', 'bet.calculateP', 'bet.postProcess'],
+      install_requires=['matplotlib', 'mpl_toolkits', 'pyDOE', 'scipy',
+          'numpy'])

From ceafa638f59e255dc2ad0476b16de703b7b8e43b Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 27 Apr 2015 16:18:42 -0500
Subject: [PATCH 61/85] updated finished writing parallel instructions

---
 doc/parallel.rst | 51 +++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 48 insertions(+), 3 deletions(-)

diff --git a/doc/parallel.rst b/doc/parallel.rst
index c67d7a06..0a5d5b73 100644
--- a/doc/parallel.rst
+++ b/doc/parallel.rst
@@ -8,7 +8,24 @@ Installation
 ------------
 
 Running this code in parallel requires the installation of `MPI for Python
-<http://mpi4py.scipy.org/>`_ which requires that your system has mpi installed.
+<http://mpi4py.scipy.org/>`_ which requires that your system has mpi
+installed.
+
+Running in parallel
+-------------------
+
+Depending on what modules, methods, and/or functions from BET your script uses
+small or no changes will need to be made to run your script in parallel. To run
+your script in parallel you will have to use::
+
+    $ mpirun -np NUM_PROCS python YOUR_SCRIPT.py
+
+instead of ::
+    
+    $ python YOUR_SCRIPT.py
+
+You might need to make sure your globalize your arrays or make sure to choose
+the ``parallel`` flag on some functions to ensure correct execution.
 
 Affected Packages
 -----------------
@@ -35,15 +52,43 @@ global values on all processors.
 
 calculateP
 ~~~~~~~~~~
-All methods in the module :mod:`~bet.calculateP.calculateP` benifit from
+All methods in the module :mod:`~bet.calculateP.calculateP` benefit from
 parallel execution. Only local arrays are returned for ``P``, use
 :meth:`~bet.util.get_global_values` to globalize local arrays.
 
 In the module :mod:`~bet.calculateP.simpleFunP` the methods
 :meth:`~bet.calculateP.simpleFunP.unif_unif`,
 :meth:`~bet.calculateP.simpleFunP.normal_normal`, and 
-:meth:`~bet.calculateP.simpleFunP.unif_normal` benifit from parallel
+:meth:`~bet.calculateP.simpleFunP.unif_normal` benefit from parallel
 execution.
 
 sampling
 ~~~~~~~~
+If you are using a model with parallel capabilities we recommend that you write
+your own python interface to handle running multiple parallel copies of your
+model simulatenously. If your model is serial then you might benefit from
+parallel execution of scripts that use
+:class:`bet.sampling.basicSampling.sampler` or
+:class:`bet.sampling.adaptiveSampling.sampler`.  The method
+:meth:`~bet.sampling.basicSampling.sampler.user_samples` has a parallel option
+(must be specified in the method call) which will partition the samples over
+several processors and return a globalized set of results.  The method
+:meth:`~bet.sampling.adaptiveSampling.sampler.generalized_chains` divides up
+the chains among the availiable processors and returns a globalized result.
+
+postProcess
+~~~~~~~~~~~
+In :mod:`~bet.postProcess.plotP` the methods
+:meth:`~bet.postProcess.plotP.calculate_1D_marginal_probs` and
+:meth:`~bet.postProcess.plotP.calculate_2D_marginal_probs` benefit from
+parallel execution. The methods :meth:`~bet.postProcess.plotP.plot_1D_marginal_probs` and
+:meth:`~bet.postProcess.plotP.plot_2D_marginal_probs` will only execute on the
+rank 0 processor.
+
+In :mod:`~bet.postProcess.postTools` the methods
+:meth:`~bet.postProcess.postTools.save_parallel_probs_csv`,
+:meth:`~bet.postProcess.postTools.collect_parallel_probs_csv`,
+:meth:`~bet.postProcess.postTools.save_parallel_probs_mat`, and
+:meth:`~bet.postProcess.postTools.collect_parallel_probs_mat` provide tools to
+save and collect probabitlies on separate processors as appropriately named files.
+

From 3a9d6070ddcfa4770292a00e10a7fbe5ed411d13 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 27 Apr 2015 18:50:32 -0500
Subject: [PATCH 62/85] Addressed comment in pull request #56, minor changes

---
 bet/sampling/adaptiveSampling.py | 2 +-
 doc/conf.py                      | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 3ef7378d..a9ab06c1 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -576,7 +576,7 @@ class maxima_kernel(kernel):
 
     maxima
         locations of the maxima of rho_D on D
-        :class:`np.nsarray` of shape (num_maxima, mdim)
+        :class:`np.ndarray` of shape (num_maxima, mdim)
     rho_max
         rho_D(maxima), list of maximum values of rho_D
     tolerance 
diff --git a/doc/conf.py b/doc/conf.py
index 7b276b48..dd62cedf 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -32,7 +32,9 @@
 intersphinx_mapping = {'python': ('https://docs.python.org/2.7', None),
         'polyadcirc' : ('http://ut-chg.github.io/PolyADCIRC', None),
          'matplotlib':('http://matplotlib.sourceforge.net', None),
-                  'numpy':('http://docs.scipy.org/doc/numpy',None)
+                  'numpy':('http://docs.scipy.org/doc/numpy',None),
+                  'np':('http://docs.scipy.org/doc/numpy',None),
+                  'scipy':('http://docs.scipy.org/doc/scipy',None)
         }
 
 todo_include_todos = True

From e5035f9e7ae018423d56607dd2b8465c6ea4efd7 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 27 Apr 2015 19:20:35 -0500
Subject: [PATCH 63/85] updated version in conf.py and setup.py. no tagging on
 non-master branches

---
 doc/conf.py | 4 ++--
 setup.py    | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/doc/conf.py b/doc/conf.py
index dd62cedf..c7de5658 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -60,9 +60,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '0.0'
+version = '0.2'
 # The full version, including alpha/beta/rc tags.
-release = '0.0'
+release = '0.2.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/setup.py b/setup.py
index f45b989b..222021e6 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 from distutils.core import setup
 
 setup(name='bet',
-      version='0.0',
+      version='0.2.0',
       description='Butler, Estep, Tavener method',
       author='Steven Mattis',
       author_email='steve.a.mattis@gmail.com',

From e5a29176e735cfb9de866f52647735a5bd0433eb Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Mon, 27 Apr 2015 21:20:42 -0500
Subject: [PATCH 64/85] Moved compare_yield, in_high_prob, and
 in_high_prob_multi from sampling to postProcess. Updated tests accordingly

---
 bet/postProcess/postTools.py             | 76 +++++++++++++++++++++++
 bet/sampling/basicSampling.py            | 79 ------------------------
 test/test_postProcess/test_postTools.py  | 79 +++++++++++++++++++++++-
 test/test_sampling/test_basicSampling.py | 40 ------------
 4 files changed, 154 insertions(+), 120 deletions(-)

diff --git a/bet/postProcess/postTools.py b/bet/postProcess/postTools.py
index 6f9dd65d..c79b1b4e 100644
--- a/bet/postProcess/postTools.py
+++ b/bet/postProcess/postTools.py
@@ -199,3 +199,79 @@ def collect_parallel_probs_mat(file_prefix, num_files, save=False,
         sio.savemat(file_prefix + "all", file_dict, do_compression=compress)
 
     return (P, lam)
+
+def compare_yield(sort_ind, sample_quality, run_param, column_headings=None):
+    """
+
+    Compare the quality of samples where ``sample_quality`` is the measure of
+    quality by which the sets of samples have been indexed and ``sort_ind`` is
+    an array of the sorted indicies.
+
+    :param list() sort_ind: indicies that index ``sample_quality`` in sorted
+        order
+    :param list() sample_quality: a measure of quality by which the sets of 
+        samples are sorted
+    :param list() run_param: zipped list of :class:`~numpy.ndarray`s containing
+        information used to generate the sets of samples to be displayed
+    :param list() column_headings: Column headings to print to screen
+
+    """
+    if column_headings == None:
+        column_headings = "Run parameters"
+    print "Sample Set No., Quality, "+ str(column_headings)
+    for i in reversed(sort_ind):
+        print i, sample_quality[i], np.round(run_param[i], 3)
+
+def in_high_prob(data, rho_D, maximum, sample_nos=None):
+    """
+
+    Estimates the number of samples in high probability regions of D.
+
+    :param data: Data associated with ``samples``
+    :type data: :class:`np.ndarray`
+    :param rho_D: probability density on D
+    :type rho_D: callable function that takes a :class:`np.array` and returns a
+        :class:`np.ndarray`
+    :param float maximum: maximum (or average) value of ``rho_D``
+    :param list sample_nos: sample numbers to plot
+
+    :rtype: int
+    :returns: Estimate of number of samples in the high probability area.
+
+    """
+    if sample_nos == None:
+        sample_nos = range(data.shape[0])
+    if len(data.shape) == 1:
+        rD = rho_D(data[sample_nos])
+    else:
+        rD = rho_D(data[sample_nos, :])
+    adjusted_total_prob = int(sum(rD)/maximum)
+    print "Samples in box "+str(adjusted_total_prob)
+    return adjusted_total_prob
+
+def in_high_prob_multi(results_list, rho_D, maximum, sample_nos_list=None):
+    """
+
+    Estimates the number of samples in high probability regions of D for a list
+    of results.
+
+    :param list results_list: list of (results, data) tuples
+    :param rho_D: probability density on D
+    :type rho_D: callable function that takes a :class:`np.array` and returns a
+        :class:`np.ndarray`
+    :param float maximum: maximum (or average) value of ``rho_D``
+    :param list sample_nos_list: list of sample numbers to plot (list of lists)
+
+    :rtype: list of int
+    :returns: Estimate of number of samples in the high probability area.
+
+    """
+    adjusted_total_prob = list()
+    if sample_nos_list:
+        for result, sample_nos in zip(results_list, sample_nos_list):
+            adjusted_total_prob.append(in_high_prob(result[1], rho_D, maximum,
+                sample_nos))
+    else:
+        for result in results_list:
+            adjusted_total_prob.append(in_high_prob(result[1], rho_D, maximum))
+    return adjusted_total_prob
diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index 43cee957..6cc8ecbe 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -14,85 +14,6 @@
 import bet.util as util
 from bet.Comm import *
 
-def compare_yield(sort_ind, sample_quality, run_param, column_headings=None):
-    """
-    TODO: Maybe move to bet.postProcessing.postTools
-
-    Compare the quality of samples where ``sample_quality`` is the measure of
-    quality by which the sets of samples have been indexed and ``sort_ind`` is
-    an array of the sorted indicies.
-
-    :param list() sort_int: indicies that index ``sample_quality`` in sorted
-        order
-    :param list() sample_quality: a measure of quality by which the sets of 
-        samples are sorted
-    :param list() run_param: zipped list of :class:`~numpy.ndarray`s containing
-        information used to generate the sets of samples to be displayed
-    :param list() column_headings: Column headings to print to screen
-
-    """
-    if column_headings == None:
-        column_headings = "Run parameters"
-    print "Sample Set No., Quality, "+ column_headings
-    for i in reversed(sort_ind):
-        print i, sample_quality[i], np.round(run_param[i], 3)
-
-def in_high_prob(data, rho_D, maximum, sample_nos=None):
-    """
-    TODO: Maybe move to bet.postProcessing.postTools
-
-    Estimates the number of samples in high probability regions of D.
-
-    :param data: Data associated with ``samples``
-    :type data: :class:`np.ndarray`
-    :param rho_D: probability density on D
-    :type rho_D: callable function that takes a :class:`np.array` and returns a
-        :class:`np.ndarray`
-    :param float maximum: maximum (or average) value of ``rho_D``
-    :param list sample_nos: sample numbers to plot
-
-    :rtype: int
-    :returns: Estimate of number of samples in the high probability area.
-
-    """
-    if sample_nos == None:
-        sample_nos = range(data.shape[0])
-    if len(data.shape) == 1:
-        rD = rho_D(data[sample_nos])
-    else:
-        rD = rho_D(data[sample_nos, :])
-    adjusted_total_prob = int(sum(rD)/maximum)
-    print "Samples in box "+str(adjusted_total_prob)
-    return adjusted_total_prob
-
-def in_high_prob_multi(results_list, rho_D, maximum, sample_nos_list=None):
-    """
-    TODO: Maybe move to bet.postProcessing.postTools
-
-    Estimates the number of samples in high probability regions of D for a list
-    of results.
-
-    :param list results_list: list of (results, data) tuples
-    :param rho_D: probability density on D
-    :type rho_D: callable function that takes a :class:`np.array` and returns a
-        :class:`np.ndarray`
-    :param float maximum: maximum (or average) value of ``rho_D``
-    :param list sample_nos_list: list of sample numbers to plot (list of lists)
-
-    :rtype: list of int
-    :returns: Estimate of number of samples in the high probability area.
-
-    """
-    adjusted_total_prob = list()
-    if sample_nos_list:
-        for result, sample_nos in zip(results_list, sample_nos_list):
-            adjusted_total_prob.append(in_high_prob(result[1], rho_D, maximum,
-                sample_nos))
-    else:
-        for result in results_list:
-            adjusted_total_prob.append(in_high_prob(result[1], rho_D, maximum))
-    return adjusted_total_prob
-
 def loadmat(save_file, model=None):
     """
     Loads data from ``save_file`` into a
diff --git a/test/test_postProcess/test_postTools.py b/test/test_postProcess/test_postTools.py
index e29e42ef..59ba6bce 100644
--- a/test/test_postProcess/test_postTools.py
+++ b/test/test_postProcess/test_postTools.py
@@ -14,9 +14,86 @@
 import bet.util as util
 from bet.Comm import *
 
+def test_in_high_prob():
+    """
+
+    Tests :meth:`bet.postProcess.postTools.in_high_prob`
+    """
+    def rho_D(my_data):
+        return my_data/4.0
+    data = np.array([0, 1, 0, 1, 1, 1])
+    maximum = np.max(rho_D(data))
+    print "maximum", maximum
+    assert 4 == postTools.in_high_prob(data, rho_D, maximum)
+    assert 3 == postTools.in_high_prob(data, rho_D, maximum, [3, 4, 5])
+    assert 2 == postTools.in_high_prob(data, rho_D, maximum, [0, 1, 2, 3])
+    assert 1 == postTools.in_high_prob(data, rho_D, maximum, [0, 2, 4])
+    assert 0 == postTools.in_high_prob(data, rho_D, maximum, [0, 2])
+
+def test_in_high_prob_multi():
+    """
+
+    Tests :meth:`bet.postProcess.postTools.in_high_prob_multi`
+    
+    """
+    def rho_D(my_data):
+        return my_data/4.0
+    data1 = np.array([0, 1, 0, 1, 1, 0])
+    data2 = np.ones(data1.shape)-data1
+    maximum = np.max(rho_D(data1))
+
+    print "maximum", maximum
+    results_list = [[None, data1], [None, data2], [None, data1], [None, data2]]
+    sample_nos_list = [[3, 4, 5], [3, 4, 5], [0, 2, 4], [0, 2, 4]]
+
+    nptest.assert_array_equal(np.array([2, 1, 1, 2]),
+            postTools.in_high_prob_multi(results_list, rho_D, maximum,
+                sample_nos_list))
+    nptest.assert_array_equal(np.array([3, 3, 3, 3]),
+            postTools.in_high_prob_multi(results_list, rho_D, maximum))
+
+def test_compare_yield_CH():
+    """
+
+    Tests :meth:`bet.postProcess.postTools.compare_yield` with column headings
+
+    """
+    sample_quality = np.random.random((10,))
+    sort_ind = np.argsort(sample_quality)
+    run_param = []
+    for i in range(10):
+        run_param.append(np.random.random((4,)))
+    column_headings = ['swallow', 'coconut', 'ni', 'shrubbery']
+    try:
+        postTools.compare_yield(sort_ind, sample_quality, run_param,
+                column_headings)
+        go = True
+    except (RuntimeError, TypeError, NameError):
+        go = False
+    nptest.assert_equal(go, True)
+
+def test_compare_yield():
+    """
+
+    Tests :meth:`bet.postProcess.postTools.compare_yield` without column headings
+
+    """
+    sample_quality = np.random.random((10,))
+    sort_ind = np.argsort(sample_quality)
+    run_param = []
+    for i in range(10):
+        run_param.append(np.random.random((4,)))
+    try:
+        postTools.compare_yield(sort_ind, sample_quality, run_param)
+        go = True
+    except (RuntimeError, TypeError, NameError):
+        go = False
+    nptest.assert_equal(go, True)
+
+
 class Test_PostTools(unittest.TestCase):
     """
-    Test :meth:`bet.postProcess.postTools`.
+    Test :mod:`bet.postProcess.postTools`.
     """
     def setUp(self):
         """
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index b51cd3f5..107f0903 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -13,46 +13,6 @@
 
 local_path = os.path.join(os.path.dirname(bet.__file__), "../test/test_sampling")
 
-def test_in_high_prob():
-    """
-    TODO :maybe move to test.test_postProcessing.test_postTools
-
-    Tests :meth:`bet.sampling.basicSampling.in_high_prob`
-    """
-    def rho_D(my_data):
-        return my_data/4.0
-    data = np.array([0, 1, 0, 1, 1, 1])
-    maximum = np.max(rho_D(data))
-    print "maximum", maximum
-    assert 4 == bsam.in_high_prob(data, rho_D, maximum)
-    assert 3 == bsam.in_high_prob(data, rho_D, maximum, [3, 4, 5])
-    assert 2 == bsam.in_high_prob(data, rho_D, maximum, [0, 1, 2, 3])
-    assert 1 == bsam.in_high_prob(data, rho_D, maximum, [0, 2, 4])
-    assert 0 == bsam.in_high_prob(data, rho_D, maximum, [0, 2])
-
-def test_in_high_prob_multi():
-    """
-    TODO :maybe move to test.test_postProcessing.test_postTools
-
-    Tests :meth:`bet.sampling.basicSampling.in_high_prob_multi`
-    
-    """
-    def rho_D(my_data):
-        return my_data/4.0
-    data1 = np.array([0, 1, 0, 1, 1, 0])
-    data2 = np.ones(data1.shape)-data1
-    maximum = np.max(rho_D(data1))
-
-    print "maximum", maximum
-    results_list = [[None, data1], [None, data2], [None, data1], [None, data2]]
-    sample_nos_list = [[3, 4, 5], [3, 4, 5], [0, 2, 4], [0, 2, 4]]
-
-    nptest.assert_array_equal(np.array([2, 1, 1, 2]),
-            bsam.in_high_prob_multi(results_list, rho_D, maximum,
-                sample_nos_list))
-    nptest.assert_array_equal(np.array([3, 3, 3, 3]),
-            bsam.in_high_prob_multi(results_list, rho_D, maximum))
-
 
 @unittest.skipIf(size > 1, 'Only run in serial')
 def test_loadmat():

From 5f638aa89f578fb2b7e68ad243b416d079d70b75 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 29 Apr 2015 15:58:13 -0500
Subject: [PATCH 65/85] fixed errors in sampling tests and basicSampling

---
 bet/sampling/basicSampling.py               | 21 ++----
 test/test_sampling/test_adaptiveSampling.py | 13 ++--
 test/test_sampling/test_basicSampling.py    | 77 +++++++++++++--------
 3 files changed, 60 insertions(+), 51 deletions(-)

diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index 6cc8ecbe..8abfc698 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -124,6 +124,7 @@ def random_samples(self, sample_type, param_min, param_max,
         param_left = np.repeat([param_min], num_samples, 0)
         param_right = np.repeat([param_max], num_samples, 0)
         samples = (param_right-param_left)
+        print samples.shape
          
         if sample_type == "lhs":
             samples = samples * lhs(param_min.shape[-1],
@@ -160,28 +161,14 @@ def user_samples(self, samples, savefile, parallel=False):
         # Solve the model at the samples
         if not(parallel) or size == 1:
             data = self.lb_model(samples)
-        elif parallel and self.num_samples%size == 0:
-            if len(samples.shape) == 1:
-                my_samples = np.empty((samples.shape[0]/size, ))
-            else:
-                my_samples = np.empty((samples.shape[0]/size, samples.shape[1]))
-            comm.Scatter([samples, MPI.DOUBLE], [my_samples, MPI.DOUBLE])
-            my_data = self.lb_model(my_samples)
-            if len(my_data.shape) == 1:
-                data = np.empty((self.num_samples,),
-                        dtype=np.float64)
-            else:
-                data = np.empty((self.num_samples, my_data.shape[1]),
-                        dtype=np.float64)
-            comm.Allgather([my_data, MPI.DOUBLE], [data, MPI.DOUBLE])
         elif parallel:
             my_len = self.num_samples/size
             if rank != size-1:
                 my_index = range(0+rank*my_len, (rank+1)*my_len)
             else:
                 my_index = range(0+rank*my_len, self.num_samples)
-            if len(my_samples.shape) == 1:
-                my_samples = samples[my_index, :]
+            if len(samples.shape) == 1:
+                my_samples = samples[my_index]
             else:
                 my_samples = samples[my_index, :]
             my_data = self.lb_model(my_samples)
@@ -194,6 +181,8 @@ def user_samples(self, samples, savefile, parallel=False):
         if len(data.shape) == 1:
             data = np.expand_dims(data, axis=1)
 
+        print samples.shape
+
         mdat = dict()
         self.update_mdict(mdat)
         mdat['samples'] = samples
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index a1310efd..5412740b 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -88,8 +88,8 @@ def ifun(outputs):
         right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0)
         left = np.all(np.greater_equal(outputs, left), axis=1)
         right = np.all(np.less_equal(outputs, right), axis=1)
-        inside = np.logial_and(left, right)
-        max_values = np.repeate(maximum, outputs.shape[0], 0)
+        inside = np.logical_and(left, right)
+        max_values = np.repeat(maximum, outputs.shape[0], 0)
         return inside.astype('float64')*max_values
         
     # create rhoD_kernel
@@ -132,7 +132,7 @@ def ifun(outputs):
     assert sampler.num_samples == mdat['num_samples']
     assert sampler.num_chains == mdat['num_chains']
     nptest.assert_array_equal(sampler.sample_batch_no,
-            mdat['sampler_batch_no'])
+            np.squeeze(mdat['sample_batch_no']))
 
 class Test_adaptive_sampler(unittest.TestCase):
     """
@@ -159,7 +159,7 @@ def map_3t1(x):
             """
             3 to 1 map
             """
-            return np.sum(x, 1)
+            return np.expand_dims(np.sum(x, 1), axis=1)
         # create 3-2 map
         def map_3t2(x):
             """
@@ -414,9 +414,10 @@ def test_generalized_chains(self):
         # create a transition set
         t_set = asam.transition_set(.5, .5**5, 1.0) 
 
-        for _, QoI_range, sampler, param_min, param_max, savefile, _ in self.test_list:
+        for _, QoI_range, sampler, param_min, param_max, savefile in self.test_list:
             for initial_sample_type in ["random", "r", "lhs"]:
-                yield verify_samples, QoI_range, sampler, param_min, param_max, t_set, savefile, initial_sample_type
+                verify_samples(QoI_range, sampler, param_min, param_max, t_set,
+                        savefile, initial_sample_type)
 
 class test_kernels(unittest.TestCase):
     """
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index 107f0903..1b9a7d42 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -3,13 +3,12 @@
 This module contains unittests for :mod:`~bet.sampling.basicSampling:`
 """
 
-import unittest, os
+import unittest, os, bet, pyDOE
 import numpy.testing as nptest
 import numpy as np
 import bet.sampling.basicSampling as bsam
 import scipy.io as sio
-import bet
-from bet.Comm import size
+from bet.Comm import *
 
 local_path = os.path.join(os.path.dirname(bet.__file__), "../test/test_sampling")
 
@@ -57,7 +56,7 @@ def verify_user_samples(model, sampler, samples, savefile, parallel):
     if len(data.shape) == 1:
         data = np.expand_dims(data, axis=1)
     if len(samples.shape) == 1:
-        samples = np.expan_dims(samples, axis=1)
+        samples = np.expand_dims(samples, axis=1)
     
     # compare the samples
     nptest.assert_array_equal(samples, my_samples)
@@ -67,24 +66,25 @@ def verify_user_samples(model, sampler, samples, savefile, parallel):
     assert samples.shape[0] == sampler.num_samples
     # did the file get correctly saved?
 
-    mdat = sio.loadmat(savefile)
-    nptest.assert_array_equal(samples, mdat['samples'])
-    nptest.assert_array_equal(data, mdat['data'])
-    assert samples.shape[0] == sampler.num_samples
+    if rank == 0:
+        mdat = sio.loadmat(savefile)
+        nptest.assert_array_equal(samples, mdat['samples'])
+        nptest.assert_array_equal(data, mdat['data'])
+    comm.Barrier()
 
 def verify_random_samples(model, sampler, sample_type, param_min, param_max,
         num_samples, savefile, parallel):
     # recreate the samples
     if num_samples == None:
-        num_samples = self.num_samples
+        num_samples = sampler.num_samples
     param_left = np.repeat([param_min], num_samples, 0)
     param_right = np.repeat([param_max], num_samples, 0)
     samples = (param_right-param_left)
     if sample_type == "lhs":
-        samples = samples * lhs(param_min.shape[-1], num_samples, criterion)
+        samples = samples * pyDOE.lhs(param_min.shape[-1], num_samples)
     elif sample_type == "random" or "r":
         np.random.seed(1)
-        samples = samples * np.random(param_left.shape)
+        samples = samples * np.random.random(param_left.shape)
     samples = samples + param_left
     # evalulate the model at the samples directly
     data = model(samples)
@@ -111,12 +111,14 @@ def verify_random_samples(model, sampler, sample_type, param_min, param_max,
     nptest.assert_array_equal(data, my_data)
     # did num_samples get updated?
     assert samples.shape[0] == sampler.num_samples
+    assert num_samples == sampler.num_samples
     # did the file get correctly saved?
-
-    mdat = sio.loadmat(savefile)
-    nptest.assert_array_equal(samples, mdat['samples'])
-    nptest.assert_array_equal(data, mdat['data'])
-    assert samples.shape[0] == sampler.num_samples
+    
+    if rank == 0:
+        mdat = sio.loadmat(savefile)
+        nptest.assert_array_equal(samples, mdat['samples'])
+        nptest.assert_array_equal(data, mdat['data'])
+    comm.Barrier()
 
 
 class Test_basic_sampler(unittest.TestCase):
@@ -140,7 +142,7 @@ def map_3t2(x):
             return np.vstack(([x[:, 0]+x[:, 1], x[:, 2]])).transpose()
         # create 10-4 map
         self.param_min10 = np.zeros((10, ))
-        self.param_min10 = np.ones((10, ))
+        self.param_max10 = np.ones((10, ))
         def map_10t4(x):
             x1 = x[:, 0] + x[:, 1]
             x2 = x[:, 2] + x[:, 3]
@@ -155,9 +157,25 @@ def map_10t4(x):
             self.samplers.append(bsam.sampler(model, num_samples))
 
     def tearDown(self):
-        for f in self.savefiles:
-            if os.path.exists(os.path.join(local_path, f, ".mat")):
-                os.remove(os.path.join(local_path, f, ".mat"))
+        """
+        Clean up extra files
+        """
+        if rank == 0:
+            for f in self.savefiles:
+                if os.path.exists(f+".mat"):
+                    os.remove(f+".mat")
+        if size > 1:
+            for f in self.savefiles:
+                proc_savefile = os.path.join(local_path, os.path.dirname(f),
+                        "proc{}{}.mat".format(rank, os.path.basename(f)))
+                print proc_savefile
+                if os.path.exists(proc_savefile):
+                    os.remove(proc_savefile)
+                proc_savefile = os.path.join(local_path, os.path.dirname(f),
+                        "p{}proc{}{}.mat".format(rank, rank, os.path.basename(f)))
+                if os.path.exists(proc_savefile):
+                    os.remove(proc_savefile)
+                print proc_savefile
 
     def test_init(self):
         """
@@ -177,7 +195,7 @@ def test_update(self):
 
     def test_user_samples(self):
         """
-        Test :meth:`bet.sampling.basicSampling.sampler.random_samples` for
+        Test :meth:`bet.sampling.basicSampling.sampler.user_samples` for
         three different QoI maps (1 to 1, 3 to 1, 3 to 2, 10 to 4).
         """
         # create a list of different sets of samples
@@ -189,11 +207,12 @@ def test_user_samples(self):
         
         for model, sampler, samples, savefile in test_list: 
             for parallel in [False, True]:
-                yield verify_user_samples, model, sampler, samples, savefile, parallel
-
+                verify_user_samples(model, sampler, samples, savefile,
+                        parallel)
+   
     def test_random_samples(self):
         """
-        Test :met:`bet.sampling.basicSampling.sampler.random_samples` for three
+        Test :meth:`bet.sampling.basicSampling.sampler.random_samples` for three
         different QoI maps (1 to 1, 3 to 1, 3 to 2, 10 to 4).
         """
         param_min_list = [self.param_min1, self.param_min1, self.param_min3,
@@ -201,14 +220,14 @@ def test_random_samples(self):
         param_max_list = [self.param_max1, self.param_max1, self.param_max3,
             self.param_max3, self.param_max10]
 
+
         test_list = zip(self.models, self.samplers, param_min_list,
-                param_max_list,
-                self.savefiles)
+                param_max_list, self.savefiles)
 
         for model, sampler, param_min, param_max, savefile in test_list:
             for sample_type in ["random", "r", "lhs"]:
                 for num_samples in [None, 25]:
                     for parallel in [False, True]:
-                        yield verify_random_samples, model, sampler,
-                        param_min, param_max, savefile, num_samples,
-                        parallel
+                        verify_random_samples(model, sampler, sample_type,
+                                param_min, param_max, num_samples, savefile,
+                                parallel)

From ee0eecfd15b381104b2cacd0f02ce60135197c49 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 29 Apr 2015 15:59:13 -0500
Subject: [PATCH 66/85] removed extra print statements

---
 bet/sampling/basicSampling.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index 8abfc698..b7c4c0e3 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -124,7 +124,6 @@ def random_samples(self, sample_type, param_min, param_max,
         param_left = np.repeat([param_min], num_samples, 0)
         param_right = np.repeat([param_max], num_samples, 0)
         samples = (param_right-param_left)
-        print samples.shape
          
         if sample_type == "lhs":
             samples = samples * lhs(param_min.shape[-1],
@@ -181,7 +180,6 @@ def user_samples(self, samples, savefile, parallel=False):
         if len(data.shape) == 1:
             data = np.expand_dims(data, axis=1)
 
-        print samples.shape
 
         mdat = dict()
         self.update_mdict(mdat)

From 4db0e437373a610473439ae49b6f2abaf9641080 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 29 Apr 2015 17:22:48 -0500
Subject: [PATCH 67/85] working on getting tests that were using yield to run
 properly and fixing associated errors

---
 bet/postProcess/plotDomains.py            | 13 ++++++---
 test/test_postProcess/test_plotDomains.py | 32 ++++++++++++++---------
 2 files changed, 29 insertions(+), 16 deletions(-)

diff --git a/bet/postProcess/plotDomains.py b/bet/postProcess/plotDomains.py
index 82ed80ea..5ecb8a44 100644
--- a/bet/postProcess/plotDomains.py
+++ b/bet/postProcess/plotDomains.py
@@ -9,6 +9,7 @@
 import matplotlib.pyplot as plt
 from matplotlib.lines import Line2D
 from itertools import combinations
+from mpl_toolkits.mplot3d import Axes3D
 
 markers = []
 for m in Line2D.markers:
@@ -132,6 +133,8 @@ def show_param(samples, data, rho_D=None, p_ref=None, sample_nos=None,
    
     if rho_D != None:
         rD = rho_D(data)
+    else:
+        rD = np.ones(data.shape[0])
     if lnums == None:
         lnums = 1+np.array(range(samples.shape[1]))
     xlabel = r'$\lambda_{'+str(lnums[0])+'}$'
@@ -149,7 +152,7 @@ def show_param(samples, data, rho_D=None, p_ref=None, sample_nos=None,
             xlabel = r'$\lambda_{'+str(x)+'}$'
             ylabel = r'$\lambda_{'+str(y)+'}$'
             savename = 'param_samples_l'+str(x)+'l'+str(y)+'_cs.eps'
-            scatter_2D(samples[:, [x, y]], sample_nos, rD, p_ref, save,
+            scatter_2D(samples[:, [x-1, y-1]], sample_nos, rD, p_ref, save,
                     interactive, xlabel, ylabel, savename)
     elif samples.shape[1] > 3 and showdim == 3:
         for x, y, z in combinations(lnums, 3):
@@ -157,7 +160,7 @@ def show_param(samples, data, rho_D=None, p_ref=None, sample_nos=None,
             ylabel = r'$\lambda_{'+str(y)+'}$'
             zlabel = r'$\lambda_{'+str(z)+'}$'
             savename = 'param_samples_l'+str(x)+'l'+str(y)+'l'+str(z)+'_cs.eps'
-            scatter_3D(samples[:, [x, y, z]], sample_nos, rD, p_ref, save,
+            scatter_3D(samples[:, [x-1, y-1, z-1]], sample_nos, rD, p_ref, save,
                     interactive, xlabel, ylabel, zlabel, savename)
 
 def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
@@ -297,7 +300,7 @@ def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
     :param data: Data associated with ``samples``
     :type data: :class:`np.ndarray`
     :param Q_ref: reference data value
-    :type Q_ref: :class:`np.ndarray`
+    :type Q_ref: :class:`np.ndarray` of shape (M, 2)
     :param list ref_markers: list of marker types for :math:`Q_{ref}`
     :param list ref_colors: list of colors for :math:`Q_{ref}`
     :param string xlabel: x-axis label
@@ -328,6 +331,10 @@ def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
             pad_inches=0)
     # Add truth markers
     for i in xrange(Q_ref.shape[0]):
+        print i
+        print Q_ref.shape
+        print len(ref_colors)
+        print len(ref_markers)
         plt.scatter(Q_ref[i, 0], Q_ref[i, 1], s=60, c=ref_colors[i],
                 marker=ref_markers[i])
     if save:
diff --git a/test/test_postProcess/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
index 978723e8..d83cab0b 100644
--- a/test/test_postProcess/test_plotDomains.py
+++ b/test/test_postProcess/test_plotDomains.py
@@ -38,7 +38,7 @@ def setUp(self):
         self.P_samples = (1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
         self.filename = "testfigure"
 
-        QoI_range = np.array([3.0, 3.0, 3.0])
+        QoI_range = np.array([3.0, 3.0, 3.0, 3.0])
         Q_ref = QoI_range*0.5
         bin_size = 0.15*QoI_range
         maximum = 1/np.product(bin_size)
@@ -54,8 +54,8 @@ def ifun(outputs):
             right = np.repeat([Q_ref+.5*bin_size], outputs.shape[0], 0)
             left = np.all(np.greater_equal(outputs, left), axis=1)
             right = np.all(np.less_equal(outputs, right), axis=1)
-            inside = np.logial_and(left, right)
-            max_values = np.repeate(maximum, outputs.shape[0], 0)
+            inside = np.logical_and(left, right)
+            max_values = np.repeat(maximum, outputs.shape[0], 0)
             return inside.astype('float64')*max_values
         self.rho_D = ifun
         self.lnums = [1, 2, 3]
@@ -94,7 +94,7 @@ def test_scatter_2D(self):
         for sn in sample_nos:
             for pr in p_ref:
                 for s in save:
-                    yield self.check_scatter_2D, sn, pr, s
+                    self.check_scatter_2D(sn, pr, s)
 
     def check_scatter_2D(self, sample_nos, p_ref, save):
         """
@@ -120,7 +120,7 @@ def test_scatter_3D(self):
         for sn in sample_nos:
             for pr in p_ref:
                 for s in save:
-                    yield self.check_scatter_3D, sn, pr, s
+                    self.check_scatter_3D(sn, pr, s)
 
     def check_scatter_3D(self, sample_nos, p_ref, save):
         """
@@ -158,7 +158,8 @@ def test_show_param(self):
                         p_ref = [None, sample[4, :]]
                         for pr in p_ref:
                             for s in save:
-                                yield self.check_show_param, samples, pr, sn, s, ln, sd
+                                self.check_show_param(sample, sn, pr, s, ln,
+                                        sd)
 
     def check_show_param(self, samples, sample_nos, p_ref, save, lnums,
             showdim):
@@ -195,7 +196,7 @@ def test_show_data(self):
                         Q_ref = [None, data[4, :]]
                         for qr in Q_ref:
                             for s in save:
-                                yield self.check_show_data, data, sn, qr, s, qn, sd
+                                self.check_show_data(data, sn, qr, s, qn, sd)
 
     def check_show_data(self, data, sample_nos, q_ref, save, qnums, showdim):
         """
@@ -203,7 +204,11 @@ def check_show_data(self, data, sample_nos, q_ref, save, qnums, showdim):
         without generating an error.
         """
         try:
-            plotDomains.show_data(data, self.rho_D, q_ref,
+            if data.shape[1] == 4:
+                plotDomains.show_data(data, self.rho_D, q_ref,
+                    sample_nos, save, False, qnums, showdim) 
+            else:
+                plotDomains.show_data(data, None, q_ref,
                     sample_nos, save, False, qnums, showdim) 
             go = True
         except (RuntimeError, TypeError, NameError):
@@ -226,7 +231,7 @@ def test_show_data_domain_2D(self):
                 for t in triangles:
                     for s in save:
                         for fn in filenames:
-                            yield self.check_show_data_domain_2D, rm, rc, t, s, fn
+                            self.check_show_data_domain_2D(rm, rc, t, s, fn)
 
     def check_show_data_domain_2D(self, ref_markers, ref_colors, triangles,
             save, filenames):
@@ -235,7 +240,7 @@ def check_show_data_domain_2D(self, ref_markers, ref_colors, triangles,
         :meth:`bet.postTools.plotDomains.show_data_domain_2D` ran
         without generating an error.
         """
-        Q_ref = self.data[4, [0, 1]]
+        Q_ref = self.data[[1, 4], [0, 1]]
         data = self.data[:, [0, 1]]
         try:
             plotDomains.show_data_domain_2D(self.samples, data, Q_ref,
@@ -250,7 +255,8 @@ def test_show_data_domain_multi(self):
         """
         Test :meth:`bet.postProcess.plotDomains.show_data_domain_multi`
         """
-        os.mkdir('figs/')
+        if not os.path.exists('figs/'):
+            os.mkdir('figs/')
         Q_nums = [None, [1, 2], [1, 2, 3]]
         ref_markers = [None, self.markers]
         ref_colors = [None, self.colors]
@@ -258,10 +264,10 @@ def test_show_data_domain_multi(self):
             for rc in ref_colors:
                 for qn in Q_nums:
                     showdim = [None, 1]
-                    if len(qn) > 2:
+                    if qn and len(qn) > 2:
                         showdim.append('all', 'ALL')
                     for sd in showdim:
-                        yield self.check_show_data_domain_multi, rm, rc, qn, sd
+                        self.check_show_data_domain_multi(rm, rc, qn, sd)
 
     def check_show_data_domain_multi(self, ref_markers, ref_colors, Q_nums,
             showdim):

From 3fc287706de2b5a107eb68e62a4cfdb8368d89b9 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 29 Apr 2015 20:21:42 -0500
Subject: [PATCH 68/85] fixed plotDomains tests that relied on yield

---
 bet/postProcess/plotDomains.py            | 51 +++++++++++++++--------
 test/test_postProcess/test_plotDomains.py | 19 ++++++---
 2 files changed, 47 insertions(+), 23 deletions(-)

diff --git a/bet/postProcess/plotDomains.py b/bet/postProcess/plotDomains.py
index 5ecb8a44..02eea9a4 100644
--- a/bet/postProcess/plotDomains.py
+++ b/bet/postProcess/plotDomains.py
@@ -10,6 +10,7 @@
 from matplotlib.lines import Line2D
 from itertools import combinations
 from mpl_toolkits.mplot3d import Axes3D
+import bet.util as util
 
 markers = []
 for m in Line2D.markers:
@@ -176,7 +177,7 @@ def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
     :type rho_D: callable function that takes a :class:`np.array` and returns a
         :class:`np.ndarray`
     :param Q_ref: reference data value
-    :type Q_ref: :class:`np.ndarray`
+    :type Q_ref: :class:`np.ndarray` of shape (mdim,)
     :param boolean save: flag whether or not to save the figure
     :param boolean interactive: flag whether or not to show the figure
     :param list Q_nums: integers representing data domain coordinates
@@ -186,32 +187,45 @@ def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
     """   
     if rho_D != None:
         rD = rho_D(data)
+    else:
+        rD = np.ones(data.shape[0])
     if  Q_nums == None:
         Q_nums = range(data.shape[1])
     xlabel = r'$q_{'+str(Q_nums[0]+1)+'}$'
     ylabel = r'$q_{'+str(Q_nums[1]+1)+'}$'
     savename = 'data_samples_cs.eps'
     if data.shape[1] == 2:
-        scatter_2D(data, sample_nos, rD, Q_ref, save, interactive, xlabel,
+        q_ref = None
+        if type(Q_ref) == np.ndarray:
+            q_ref = Q_ref[Q_nums[:2]]
+        scatter_2D(data, sample_nos, rD, q_ref, save, interactive, xlabel,
                 ylabel, savename)
     elif data.shape[1] == 3:
         zlabel = r'$q_{'+str(Q_nums[2]+1)+'}$'
-        scatter_3D(data, sample_nos, rD, Q_ref, save, interactive, xlabel,
+        if type(Q_ref) == np.ndarray:
+            q_ref = Q_ref[Q_nums[:3]]
+        scatter_3D(data, sample_nos, rD, q_ref, save, interactive, xlabel,
                 ylabel, zlabel, savename)
     elif data.shape[1] > 2 and showdim == 2:
         for x, y in combinations(Q_nums, 2):
-            xlabel = r'$q_{'+str(x)+'}$'
-            ylabel = r'$q_{'+str(y)+'}$'
-            savename = 'data_sampqes_q'+str(x)+'q'+str(y)+'_cs.eps'
-            scatter_2D(data[:, [x, y]], sample_nos, rD, Q_ref, save,
+            xlabel = r'$q_{'+str(x+1)+'}$'
+            ylabel = r'$q_{'+str(y+1)+'}$'
+            savename = 'data_sampqes_q'+str(x+1)+'q'+str(y+1)+'_cs.eps'
+            q_ref = None
+            if type(Q_ref) == np.ndarray:
+                q_ref = Q_ref[[x, y]]
+            scatter_2D(data[:, [x, y]], sample_nos, rD, q_ref, save,
                     interactive, xlabel, ylabel, savename)
     elif data.shape[1] > 3 and showdim == 3:
         for x, y, z in combinations(Q_nums, 3):
-            xlabel = r'$q_{'+str(x)+'}$'
-            ylabel = r'$q_{'+str(y)+'}$'
-            zlabel = r'$q_{'+str(z)+'}$'
-            savename = 'data_sampqes_q'+str(x)+'q'+str(y)+'q'+str(z)+'_cs.eps'
-            scatter_3D(data[:, [x, y, z]], sample_nos, rD, Q_ref, save,
+            xlabel = r'$q_{'+str(x+1)+'}$'
+            ylabel = r'$q_{'+str(y+1)+'}$'
+            zlabel = r'$q_{'+str(z+1)+'}$'
+            q_ref = None
+            if type(Q_ref) == np.ndarray:
+                q_ref = Q_ref[[x, y, z]]
+            savename = 'data_sampqes_q'+str(x+1)+'q'+str(y+1)+'q'+str(z+1)+'_cs.eps'
+            scatter_3D(data[:, [x, y, z]], sample_nos, rD, q_ref, save,
                     interactive, xlabel, ylabel, zlabel, savename)
 
 def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
@@ -228,7 +242,7 @@ def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
     :param data: Data associated with ``samples``
     :type data: :class:`np.ndarray`
     :param Q_ref: reference data value
-    :type Q_ref: :class:`np.ndarray`
+    :type Q_ref: :class:`np.ndarray` of shape (M, mdim)`
     :param list Q_nums: dimensions of the QoI to plot
     :param string img_folder: folder to save the plots to
     :param list ref_markers: list of marker types for :math:`Q_{ref}`
@@ -247,6 +261,8 @@ def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
     if showdim == None:
         showdim = 1
 
+    Q_ref = util.fix_dimensions_data(Q_ref, data.shape[1])
+
     triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
     triangles = triangulation.triangles
 
@@ -289,7 +305,7 @@ def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
 
 def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
         ref_colors=None, xlabel=r'$q_1$', ylabel=r'$q_2',
-        triangles=None, save=True, interactive=True, filenames=None):
+        triangles=None, save=True, interactive=False, filenames=None):
     r"""
     Plot the data domain D using a triangulation based on the generating
     samples with a marker for various :math:`Q_{ref}`. Assumes that the first
@@ -321,6 +337,9 @@ def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
         triangles = triangulation.triangles
     if filenames == None:
         filenames = ['domain_q1_q2_cs.eps', 'q1_q2_domain_Q_cs.eps']
+
+    Q_ref = util.fix_dimensions_data(Q_ref, 2)
+
     # Create figure
     plt.tricontourf(data[:, 0], data[:, 1], np.zeros((data.shape[0],)),
             triangles=triangles, colors='grey') 
@@ -331,10 +350,6 @@ def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
             pad_inches=0)
     # Add truth markers
     for i in xrange(Q_ref.shape[0]):
-        print i
-        print Q_ref.shape
-        print len(ref_colors)
-        print len(ref_markers)
         plt.scatter(Q_ref[i, 0], Q_ref[i, 1], s=60, c=ref_colors[i],
                 marker=ref_markers[i])
     if save:
diff --git a/test/test_postProcess/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
index d83cab0b..56e19de1 100644
--- a/test/test_postProcess/test_plotDomains.py
+++ b/test/test_postProcess/test_plotDomains.py
@@ -182,7 +182,7 @@ def test_show_data(self):
         sample_nos = [None, 25]
         save = [True, False]
         data_sets = [self.data, self.data[:, [0, 1]]]
-        qnums = [None, self.lnums]
+        qnums = [None, [0, 1, 2]]#self.lnums]
 
         for data in data_sets:
             showdim = [None]
@@ -212,8 +212,15 @@ def check_show_data(self, data, sample_nos, q_ref, save, qnums, showdim):
                     sample_nos, save, False, qnums, showdim) 
             go = True
         except (RuntimeError, TypeError, NameError):
+            print "ERROR"
+            print data.shape
+            print q_ref
+            print sample_nos
+            print save
+            print qnums
+            print showdim
             go = False
-        nptest.assert_equal(go, True) 
+        nptest.assert_equal(go, True)
 
     def test_show_data_domain_2D(self):
         """
@@ -240,7 +247,9 @@ def check_show_data_domain_2D(self, ref_markers, ref_colors, triangles,
         :meth:`bet.postTools.plotDomains.show_data_domain_2D` ran
         without generating an error.
         """
-        Q_ref = self.data[[1, 4], [0, 1]]
+        Q_ref = self.data[:, [0, 1]]
+        Q_ref = Q_ref[[1,4],:]
+        print Q_ref.shape
         data = self.data[:, [0, 1]]
         try:
             plotDomains.show_data_domain_2D(self.samples, data, Q_ref,
@@ -265,7 +274,7 @@ def test_show_data_domain_multi(self):
                 for qn in Q_nums:
                     showdim = [None, 1]
                     if qn and len(qn) > 2:
-                        showdim.append('all', 'ALL')
+                        showdim.extend(['all', 'ALL'])
                     for sd in showdim:
                         self.check_show_data_domain_multi(rm, rc, qn, sd)
 
@@ -276,7 +285,7 @@ def check_show_data_domain_multi(self, ref_markers, ref_colors, Q_nums,
         :meth:`bet.postTools.plotDomains.show_data_domain_multi` ran
         without generating an error.
         """
-        Q_ref = self.data[4, :]
+        Q_ref = self.data[[4, 2], :]
         try:
             plotDomains.show_data_domain_multi(self.samples, self.data,
                     Q_ref, Q_nums, ref_markers=ref_markers,

From 31f235603bb53c0e50ab6e07b0f5dbd2c2130394 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 30 Apr 2015 20:58:39 -0500
Subject: [PATCH 69/85] fixed file removal after tests

---
 bet/postProcess/plotDomains.py              |  4 ++--
 test/test_postProcess/test_plotDomains.py   | 19 ++++++++++++++++++-
 test/test_postProcess/test_plotP.py         |  9 ++++++---
 test/test_sampling/test_adaptiveSampling.py | 21 +++++++--------------
 4 files changed, 33 insertions(+), 20 deletions(-)

diff --git a/bet/postProcess/plotDomains.py b/bet/postProcess/plotDomains.py
index 02eea9a4..4fe1f84a 100644
--- a/bet/postProcess/plotDomains.py
+++ b/bet/postProcess/plotDomains.py
@@ -210,7 +210,7 @@ def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
         for x, y in combinations(Q_nums, 2):
             xlabel = r'$q_{'+str(x+1)+'}$'
             ylabel = r'$q_{'+str(y+1)+'}$'
-            savename = 'data_sampqes_q'+str(x+1)+'q'+str(y+1)+'_cs.eps'
+            savename = 'data_samples_q'+str(x+1)+'q'+str(y+1)+'_cs.eps'
             q_ref = None
             if type(Q_ref) == np.ndarray:
                 q_ref = Q_ref[[x, y]]
@@ -224,7 +224,7 @@ def show_data(data, rho_D=None, Q_ref=None, sample_nos=None,
             q_ref = None
             if type(Q_ref) == np.ndarray:
                 q_ref = Q_ref[[x, y, z]]
-            savename = 'data_sampqes_q'+str(x+1)+'q'+str(y+1)+'q'+str(z+1)+'_cs.eps'
+            savename = 'data_samples_q'+str(x+1)+'q'+str(y+1)+'q'+str(z+1)+'_cs.eps'
             scatter_3D(data[:, [x, y, z]], sample_nos, rD, q_ref, save,
                     interactive, xlabel, ylabel, zlabel, savename)
 
diff --git a/test/test_postProcess/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
index 56e19de1..e8bdbfbd 100644
--- a/test/test_postProcess/test_plotDomains.py
+++ b/test/test_postProcess/test_plotDomains.py
@@ -13,11 +13,12 @@
 from matplotlib.lines import Line2D
 import numpy as np
 import numpy.testing as nptest
-from bet.Comm import *
+from bet.Comm import size
 
 local_path = os.path.join(os.path.dirname(bet.__file__),
         "../test/test_sampling")
 
+@unittest.skipIf(size > 1, 'Only run in serial')
 class test_plotDomains(unittest.TestCase):
     """
     Test :meth:`bet.postProcess.plotP.calculate_1D_marginal_probs` and  
@@ -76,13 +77,28 @@ def tearDown(self):
         filenames = glob.glob(self.filename+".*")
         filenames.extend(glob.glob('param_samples_*cs.eps'))
         filenames.extend(glob.glob('data_samples_*cs.eps'))
+
+        filenames.extend(glob.glob(self.filename+".*"))
+        filenames.extend(glob.glob( 'param_samples_*cs.eps'))
+        filenames.extend(glob.glob(os.path.join(local_path,
+            'data_samples_*cs.eps')))
+
+
+        filenames.append('domain_q1_q2_cs.eps')
         filenames.append('domain_q1_q1_cs.eps')
         filenames.append('q1_q2_domain_Q_cs.eps')
+        filenames.append('q1_q1_domain_Q_cs.eps')
         figfiles = glob.glob('figs/*')
+        figfiles.extend(glob.glob(os.path.join(local_path, 'figs/*')))
         filenames.extend(figfiles)
+
         for f in filenames:
             if os.path.exists(os.path.join(local_path, f)):
                 os.remove(os.path.join(local_path, f))
+            if os.path.exists(f):
+                os.remove(f)
+        if os.path.exists("figs"):
+            os.rmdir("figs")
 
     def test_scatter_2D(self):
         """
@@ -98,6 +114,7 @@ def test_scatter_2D(self):
 
     def check_scatter_2D(self, sample_nos, p_ref, save):
         """
+
         Check to see that the :meth:`bet.postTools.plotDomains.scatter_2D` ran
         without generating an error.
         """
diff --git a/test/test_postProcess/test_plotP.py b/test/test_postProcess/test_plotP.py
index ff0c85ce..69a1d25f 100644
--- a/test/test_postProcess/test_plotP.py
+++ b/test/test_postProcess/test_plotP.py
@@ -163,8 +163,10 @@ def test_plot_marginals_1D(self):
         try:
             plotP.plot_1D_marginal_probs(marginals, bins,self.lam_domain, filename = "file", interactive=False)
             go = True
-            os.remove("file_1D_0.eps")
-            os.remove("file_1D_1.eps")
+            if os.path.exists("file_1D_0.eps"):
+                os.remove("file_1D_0.eps")
+            if os.path.exists("file_1D_1.eps"):
+                os.remove("file_1D_1.eps")
         except (RuntimeError, TypeError, NameError):
             go = False
         nptest.assert_equal(go, True)
@@ -182,7 +184,8 @@ def test_plot_marginals_2D(self):
         try:
             plotP.plot_2D_marginal_probs(marginals, bins,self.lam_domain, filename = "file", interactive=False)
             go = True
-            os.remove("file_2D_0_1.eps")
+            if os.path.exists("file_2D_0_1.eps"):
+                os.remove("file_2D_0_1.eps")
         except (RuntimeError, TypeError, NameError):
             go = False
         nptest.assert_equal(go, True)
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 5412740b..1ad88fbb 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -5,7 +5,7 @@
 This module contains unittests for :mod:`~bet.sampling.adaptiveSampling`
 """
 
-import unittest, os
+import unittest, os, glob
 import numpy.testing as nptest
 import numpy as np
 import bet.sampling.adaptiveSampling as asam
@@ -121,7 +121,7 @@ def ifun(outputs):
     # did the savefiles get created? (proper number, contain proper keys)
     mdat = {}
     if size > 1:
-        mdat = sio.loadmat(os.path.join(local_path, os.path.dirname(savefile),
+        mdat = sio.loadmat(os.path.join(os.path.dirname(savefile),
             "proc{}{}".format(rank, os.path.basename(savefile))))
     else:
         mdat = sio.loadmat(savefile)
@@ -209,18 +209,11 @@ def tearDown(self):
         for f in self.savefiles:
             if os.path.exists(f+".mat"):
                 os.remove(f+".mat")
-        if size > 1:
-            for f in self.savefiles:
-                proc_savefile = os.path.join(local_path, os.path.dirname(f),
-                        "proc{}{}.mat".format(rank, os.path.basename(f)))
-                print proc_savefile
-                if os.path.exists(proc_savefile):
-                    os.remove(proc_savefile)
-                proc_savefile = os.path.join(local_path, os.path.dirname(f),
-                        "p{}proc{}{}.mat".format(rank, rank, os.path.basename(f)))
-                if os.path.exists(proc_savefile):
-                    os.remove(proc_savefile)
-                print proc_savefile
+        proc_savefiles = glob.glob("p{}*.mat".format(rank))
+        proc_savefiles.extend(glob.glob("proc{}*.mat".format(rank)))
+        for pf in proc_savefiles:
+            if os.path.exists(pf):
+                os.remove(pf)
 
     def test_update(self):
         """

From 1e402be2de4893ce7d2d9d7759abbd965082f731 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 7 May 2015 16:06:55 -0500
Subject: [PATCH 70/85] updating examples

---
 examples/fromFileMap/sandbox_test_2D.py        |  10 +++++-----
 examples/linearMap/linearMapUniformSampling.py |   7 +++++--
 examples/matfiles/Q_2D.mat                     | Bin 0 -> 41481 bytes
 examples/matfiles/Q_3D.mat                     | Bin 0 -> 767744 bytes
 examples/matfiles/sandbox2d.mat                | Bin 0 -> 297330 bytes
 examples/matfiles/sandbox3d.mat                | Bin 0 -> 451161 bytes
 6 files changed, 10 insertions(+), 7 deletions(-)
 create mode 100644 examples/matfiles/Q_2D.mat
 create mode 100644 examples/matfiles/Q_3D.mat
 create mode 100644 examples/matfiles/sandbox2d.mat
 create mode 100644 examples/matfiles/sandbox3d.mat

diff --git a/examples/fromFileMap/sandbox_test_2D.py b/examples/fromFileMap/sandbox_test_2D.py
index 86d02132..e1fc663e 100755
--- a/examples/fromFileMap/sandbox_test_2D.py
+++ b/examples/fromFileMap/sandbox_test_2D.py
@@ -4,6 +4,7 @@
 import polyadcirc.pyADCIRC.basic as basic
 import bet.sampling.adaptiveSampling as asam
 import bet.sampling.basicSampling as bsam
+import bet.postProcess.postTools as ptools
 import scipy.io as sio
 from scipy.interpolate import griddata
 
@@ -71,8 +72,7 @@ def rho_D(outputs):
 kernel_mm = asam.maxima_mean_kernel(np.array([Q_ref]), rho_D)
 kernel_rD = asam.rhoD_kernel(maximum, rho_D)
 kernel_m = asam.maxima_kernel(np.array([Q_ref]), rho_D)
-kernel_md = asam.multi_dist_kernel()
-kern_list = [kernel_mm, kernel_rD, kernel_m, kernel_md]
+kern_list = [kernel_mm, kernel_rD, kernel_m]
 
 # Create sampler
 chain_length = 125
@@ -104,11 +104,11 @@ def rho_D(outputs):
 
 # Compare the quality of several sets of samples
 print "Compare yield of sample sets with various kernels"
-bsam.compare_yield(gen_results[3], gen_results[2], gen_results[4])
+ptools.compare_yield(gen_results[3], gen_results[2], gen_results[4])
 print "Compare yield of sample sets with various transition sets bounds"
-bsam.compare_yield(tk_results[3], tk_results[2], tk_results[4])
+ptools.compare_yield(tk_results[3], tk_results[2], tk_results[4])
 print "Compare yield of sample sets with variouos increase/decrease ratios"
-bsam.compare_yield(incdec_results[3], incdec_results[2], incdec_results[4])
+ptools.compare_yield(incdec_results[3], incdec_results[2], incdec_results[4])
 
 # Read in points_ref and plot results
 p_ref = mdat['points_true']
diff --git a/examples/linearMap/linearMapUniformSampling.py b/examples/linearMap/linearMapUniformSampling.py
index 553704c4..743f4858 100644
--- a/examples/linearMap/linearMapUniformSampling.py
+++ b/examples/linearMap/linearMapUniformSampling.py
@@ -127,8 +127,11 @@
 
 # calculate probablities
 (P,  lambda_emulate, io_ptr, emulate_ptr) = calculateP.prob_emulated(samples=samples,
-                                                                     data=data, rho_D_M = d_distr_prob, d_distr_samples = d_distr_samples,
-                                                                     lam_domain=lam_domain, lambda_emulate=lambda_emulate, d_Tree=d_Tree)
+                                                                     data=data,
+                                                                     rho_D_M=d_distr_prob,
+                                                                     d_distr_samples=d_distr_samples,
+                                                                     lambda_emulate=lambda_emulate,
+                                                                     d_Tree=d_Tree)
 # calculate 2d marginal probs
 '''
 Suggested changes for user:
diff --git a/examples/matfiles/Q_2D.mat b/examples/matfiles/Q_2D.mat
new file mode 100644
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From f7e69518eb5d1fd16f30f360b11aeaf227de8918 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 7 May 2015 21:00:12 -0500
Subject: [PATCH 71/85] fromFile and linearMap examples work

---
 examples/fromFileMap/sandbox_test_3D.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/examples/fromFileMap/sandbox_test_3D.py b/examples/fromFileMap/sandbox_test_3D.py
index 3137fd9a..93f67e80 100755
--- a/examples/fromFileMap/sandbox_test_3D.py
+++ b/examples/fromFileMap/sandbox_test_3D.py
@@ -5,6 +5,7 @@
 import polyadcirc.pyADCIRC.basic as basic
 import bet.sampling.adaptiveSampling as asam
 import bet.sampling.basicSampling as bsam
+import bet.postProcess.postTools as ptools
 import scipy.io as sio
 from scipy.interpolate import griddata
 
@@ -71,8 +72,7 @@ def rho_D(outputs):
 kernel_mm = asam.maxima_mean_kernel(np.array([Q_ref]), rho_D)
 kernel_rD = asam.rhoD_kernel(maximum, rho_D)
 kernel_m = asam.maxima_kernel(np.array([Q_ref]), rho_D)
-kernel_md = asam.multi_dist_kernel()
-heur_list = [kernel_mm, kernel_rD, kernel_m, kernel_md]
+heur_list = [kernel_mm, kernel_rD, kernel_m]
 
 # Create sampler
 chain_length = 125
@@ -106,11 +106,11 @@ def rho_D(outputs):
 result_list = [gen_results, tk_results, incdec_results]
 
 print "Compare yield of sample sets with various kernels"
-bsam.compare_yield(gen_results[3], gen_results[2], gen_results[4])
+ptools.compare_yield(gen_results[3], gen_results[2], gen_results[4])
 print "Compare yield of sample sets with various transition sets bounds"
-bsam.compare_yield(tk_results[3], tk_results[2], tk_results[4])
+ptools.compare_yield(tk_results[3], tk_results[2], tk_results[4])
 print "Compare yield of sample sets with variouos increase/decrease ratios"
-bsam.compare_yield(incdec_results[3], incdec_results[2], incdec_results[4])
+ptools.compare_yield(incdec_results[3], incdec_results[2], incdec_results[4])
 
 # Read in points_ref and plot results
 p_ref = mdat['points_true']

From 14fcc0b24301e274a00e9da345455b487146b3fd Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Thu, 7 May 2015 21:11:19 -0500
Subject: [PATCH 72/85] updating fromADCIRC examples

---
 examples/fromADCIRCMap/Q_1D_serial.py | 6 +++---
 examples/fromADCIRCMap/adaptive_2D.py | 3 ++-
 examples/fromADCIRCMap/adaptive_3D.py | 3 ++-
 3 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/examples/fromADCIRCMap/Q_1D_serial.py b/examples/fromADCIRCMap/Q_1D_serial.py
index 5afbf2a8..19d19784 100644
--- a/examples/fromADCIRCMap/Q_1D_serial.py
+++ b/examples/fromADCIRCMap/Q_1D_serial.py
@@ -43,7 +43,7 @@ def postprocess(station_nums, ref_num):
 
     # Calculate P on lambda emulate
     (P0, lem0, io_ptr0, emulate_ptr0) = calcP.prob_emulated(samples, data,
-            rho_D_M, d_distr_samples, lam_domain, lambda_emulate, d_Tree)
+            rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
     print "Calculating prob_emulated"
     mdict['P0'] = P0
     mdict['lem0'] = lem0
@@ -53,7 +53,7 @@ def postprocess(station_nums, ref_num):
     # Calclate P on the actual samples with assumption that voronoi cells have
     # equal size
     (P1, lam_vol1, io_ptr1, emulate_ptr1) = calcP.prob(samples, data,
-            rho_D_M, d_distr_samples, lam_domain, d_Tree)
+            rho_D_M, d_distr_samples, d_Tree)
     print "Calculating prob"
     mdict['P1'] = P1
     mdict['lam_vol1'] = lam_vol1
@@ -64,7 +64,7 @@ def postprocess(station_nums, ref_num):
     # Calculate P on the actual samples estimating voronoi cell volume with MC
     # integration
     (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
-            data, rho_D_M, d_distr_samples, lam_domain, lambda_emulate, d_Tree)
+            data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
     print "Calculating prob_mc"
     mdict['P3'] = P3
     mdict['lam_vol3'] = lam_vol3
diff --git a/examples/fromADCIRCMap/adaptive_2D.py b/examples/fromADCIRCMap/adaptive_2D.py
index 9cc5d255..3a2fb5d4 100755
--- a/examples/fromADCIRCMap/adaptive_2D.py
+++ b/examples/fromADCIRCMap/adaptive_2D.py
@@ -7,6 +7,7 @@
 import bet.sampling.adaptiveSampling as asam
 import bet.sampling.basicSampling as bsam
 import scipy.io as sio
+import bet.postProcess.postTools as ptools
 
 adcirc_dir = '/work/01837/lcgraham/v50_subdomain/work'
 grid_dir = adcirc_dir + '/ADCIRC_landuse/Inlet_b2/inputs/poly_walls'
@@ -114,7 +115,7 @@ def rho_D(outputs):
         param_max, transition_set, kernel, sample_save_file,
         initial_sample_type)
 
-bsam.in_high_prob(data, rho_D, maximum)
+ptools.in_high_prob(data, rho_D, maximum)
 
 print np.mean(all_step_ratios)
 
diff --git a/examples/fromADCIRCMap/adaptive_3D.py b/examples/fromADCIRCMap/adaptive_3D.py
index 8766974f..8310ece7 100755
--- a/examples/fromADCIRCMap/adaptive_3D.py
+++ b/examples/fromADCIRCMap/adaptive_3D.py
@@ -7,6 +7,7 @@
 import bet.sampling.adaptiveSampling as asam
 import bet.sampling.basicSampling as bsam
 import scipy.io as sio
+import bet.postProcess.postTools as ptools
 
 adcirc_dir = '/work/01837/lcgraham/v50_subdomain/work'
 grid_dir = adcirc_dir + '/ADCIRC_landuse/Inlet_b2/inputs/poly_walls'
@@ -112,7 +113,7 @@ def rho_D(outputs):
 (samples, data, step_sizes) = sampler.generalized_chains(param_min, param_max,
         transition_set, kernel_rD, sample_save_file,
         initial_sample_type)
-bsam.in_high_prob(data, rho_D, maximum)
+ptools.in_high_prob(data, rho_D, maximum)
 
 
 

From 90d3b794475b482c6f25627180f993aedbdd8705 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Fri, 8 May 2015 15:34:37 -0500
Subject: [PATCH 73/85] updating examples

---
 examples/fromADCIRCMap/Q_1D_parallel.py       | 92 +++++++++++++++++++
 examples/fromADCIRCMap/Q_1D_serial.py         |  5 +-
 examples/fromADCIRCMap/Q_2D_parallel.py       | 90 ++++++++++++++++++
 .../fromADCIRCMap/{Q_2D.py => Q_2D_serial.py} | 11 +--
 examples/fromADCIRCMap/Q_3D_parallel.py       | 28 +++---
 examples/fromADCIRCMap/Q_3D_serial.py         |  2 +-
 6 files changed, 204 insertions(+), 24 deletions(-)
 create mode 100644 examples/fromADCIRCMap/Q_1D_parallel.py
 create mode 100644 examples/fromADCIRCMap/Q_2D_parallel.py
 rename examples/fromADCIRCMap/{Q_2D.py => Q_2D_serial.py} (87%)

diff --git a/examples/fromADCIRCMap/Q_1D_parallel.py b/examples/fromADCIRCMap/Q_1D_parallel.py
new file mode 100644
index 00000000..958c5632
--- /dev/null
+++ b/examples/fromADCIRCMap/Q_1D_parallel.py
@@ -0,0 +1,92 @@
+import bet.calculateP.calculateP as calcP
+import bet.calculateP.simpleFunP as sfun
+import numpy as np
+import scipy.io as sio
+from bet.Comm import rank
+import bet.util as util
+
+# Import "Truth"
+mdat = sio.loadmat('Q_2D')
+Q = mdat['Q']
+Q_ref = mdat['Q_true']
+
+# Import Data
+samples = mdat['points'].transpose()
+lam_domain = np.array([[0.07, .15], [0.1, 0.2]])
+
+print "Finished loading data"
+
+def postprocess(station_nums, ref_num):
+    
+    filename = 'P_q'+str(station_nums[0]+1)+'_q'
+    if len(station_nums) == 3:
+        filename += '_q'+str(station_nums[2]+1)
+    filename += '_ref_'+str(ref_num+1)
+
+    data = Q[:, station_nums]
+    q_ref = Q_ref[ref_num, station_nums]
+
+    # Create Simple function approximation
+    # Save points used to parition D for simple function approximation and the
+    # approximation itself (this can be used to make close comparisions...)
+    (rho_D_M, d_distr_samples, d_Tree) = sfun.uniform_hyperrectangle(data,
+            q_ref, bin_ratio=0.15,
+            center_pts_per_edge=np.ones((data.shape[1],)))
+
+    num_l_emulate = 1e6
+    lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
+    print "Finished emulating lambda samples"
+
+    if rank == 0:
+        mdict = dict()
+        mdict['rho_D_M'] = rho_D_M
+        mdict['d_distr_samples'] = d_distr_samples 
+        mdict['num_l_emulate'] = num_l_emulate
+        #mdict['lambda_emulate'] = lambda_emulate
+
+    # Calculate P on lambda emulate
+    (P0, lem0, io_ptr0, emulate_ptr0) = calcP.prob_emulated(samples, data,
+            rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
+
+    if rank == 0:
+        print "Calculating prob_emulated"
+        mdict['P0'] = util.get_global_values(P0)
+        mdict['lem0'] = lem0
+        mdict['io_ptr0'] = io_ptr0
+        mdict['emulate_ptr0'] = emulate_ptr0
+
+    # Calclate P on the actual samples with assumption that voronoi cells have
+    # equal size
+    (P1, lam_vol1, io_ptr1) = calcP.prob(samples, data,
+            rho_D_M, d_distr_samples, d_Tree)
+    if rank == 0:
+        print "Calculating prob"
+        mdict['P1'] = util.get_global_values(P1)
+        mdict['lam_vol1'] = lam_vol1
+        mdict['lem1'] = samples
+        mdict['io_ptr1'] = io_ptr1
+
+    # Calculate P on the actual samples estimating voronoi cell volume with MC
+    # integration
+    (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
+            data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
+    if rank == 0:
+        print "Calculating prob_mc"
+        mdict['P3'] = util.get_global_values(P3)
+        mdict['lam_vol3'] = lam_vol3
+        mdict['io_ptr3'] = io_ptr3
+        mdict['emulate_ptr3'] = emulate_ptr3
+        # Export P and compare to MATLAB solution visually
+        sio.savemat(filename, mdict, do_compression=True)
+
+# Post-process and save P and emulated points
+ref_nums = [6, 11, 15] # 7, 12, 16
+stations = [1, 4, 5] # 2, 5, 6
+
+ref_nums, stations = np.meshgrid(ref_nums, stations)
+ref_nums = ref_nums.ravel()
+stations = stations.ravel()
+
+for tnum, stat in zip(ref_nums, stations):
+    postprocess([0], tnum)
+
diff --git a/examples/fromADCIRCMap/Q_1D_serial.py b/examples/fromADCIRCMap/Q_1D_serial.py
index 19d19784..48f28072 100644
--- a/examples/fromADCIRCMap/Q_1D_serial.py
+++ b/examples/fromADCIRCMap/Q_1D_serial.py
@@ -19,7 +19,7 @@ def postprocess(station_nums, ref_num):
     filename = 'P_q'+str(station_nums[0]+1)+'_q'
     if len(station_nums) == 3:
         filename += '_q'+str(station_nums[2]+1)
-    filename += '_truth_'+str(ref_num+1)
+    filename += '_ref_'+str(ref_num+1)
 
     data = Q[:, station_nums]
     q_ref = Q_ref[ref_num, station_nums]
@@ -52,14 +52,13 @@ def postprocess(station_nums, ref_num):
 
     # Calclate P on the actual samples with assumption that voronoi cells have
     # equal size
-    (P1, lam_vol1, io_ptr1, emulate_ptr1) = calcP.prob(samples, data,
+    (P1, lam_vol1, io_ptr1) = calcP.prob(samples, data,
             rho_D_M, d_distr_samples, d_Tree)
     print "Calculating prob"
     mdict['P1'] = P1
     mdict['lam_vol1'] = lam_vol1
     mdict['lem1'] = samples
     mdict['io_ptr1'] = io_ptr1
-    mdict['emulate_ptr1'] = emulate_ptr1
 
     # Calculate P on the actual samples estimating voronoi cell volume with MC
     # integration
diff --git a/examples/fromADCIRCMap/Q_2D_parallel.py b/examples/fromADCIRCMap/Q_2D_parallel.py
new file mode 100644
index 00000000..da1d665b
--- /dev/null
+++ b/examples/fromADCIRCMap/Q_2D_parallel.py
@@ -0,0 +1,90 @@
+import bet.calculateP.calculateP as calcP
+import bet.calculateP.simpleFunP as sfun
+import numpy as np
+import scipy.io as sio
+import bet.util as util
+from bet.Comm import rank
+
+# Import "Truth"
+mdat = sio.loadmat('Q_2D')
+Q = mdat['Q']
+Q_ref = mdat['Q_true']
+
+# Import Data
+samples = mdat['points'].transpose()
+lam_domain = np.array([[0.07, .15], [0.1, 0.2]])
+
+print "Finished loading data"
+
+def postprocess(station_nums, ref_num):
+    
+    filename = 'P_q'+str(station_nums[0]+1)+'_q'+str(station_nums[1]+1)
+    if len(station_nums) == 3:
+        filename += '_q'+str(station_nums[2]+1)
+    filename += '_ref_'+str(ref_num+1)
+
+    data = Q[:, station_nums]
+    q_ref = Q_ref[ref_num, station_nums]
+
+    # Create Simple function approximation
+    # Save points used to parition D for simple function approximation and the
+    # approximation itself (this can be used to make close comparisions...)
+    (rho_D_M, d_distr_samples, d_Tree) = sfun.uniform_hyperrectangle(data,
+            q_ref, bin_ratio=0.15,
+            center_pts_per_edge=np.ones((data.shape[1],)))
+
+    num_l_emulate = 1e6
+    lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
+    
+    if rank == 0:
+        print "Finished emulating lambda samples"
+        mdict = dict()
+        mdict['rho_D_M'] = rho_D_M
+        mdict['d_distr_samples'] = d_distr_samples 
+        mdict['num_l_emulate'] = num_l_emulate
+
+    # Calculate P on lambda emulate
+    (P0, lem0, io_ptr0, emulate_ptr0) = calcP.prob_emulated(samples, data,
+            rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
+    if rank == 0:
+        print "Calculating prob_emulated"
+        mdict['P0'] = P0
+        mdict['lem0'] = lem0
+        mdict['io_ptr0'] = io_ptr0
+        mdict['emulate_ptr0'] = emulate_ptr0
+
+    # Calclate P on the actual samples with assumption that voronoi cells have
+    # equal size
+    (P1, lam_vol1, io_ptr1) = calcP.prob(samples, data,
+            rho_D_M, d_distr_samples, d_Tree)
+    if rank == 0:
+        print "Calculating prob"
+        mdict['P1'] = P1
+        mdict['lam_vol1'] = lam_vol1
+        mdict['lem1'] = samples
+        mdict['io_ptr1'] = io_ptr1
+
+    # Calculate P on the actual samples estimating voronoi cell volume with MC
+    # integration
+    (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
+            data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
+    if rank == 0:
+        print "Calculating prob_mc"
+        mdict['P3'] = P3
+        mdict['lam_vol3'] = lam_vol3
+        mdict['io_ptr3'] = io_ptr3
+        mdict['emulate_ptr3'] = emulate_ptr3
+        # Export P and compare to MATLAB solution visually
+        sio.savemat(filename, mdict, do_compression=True)
+
+# Post-process and save P and emulated points
+ref_nums = [6, 11, 15] # 7, 12, 16
+stations = [1, 4, 5] # 2, 5, 6
+
+ref_nums, stations = np.meshgrid(ref_nums, stations)
+ref_nums = ref_nums.ravel()
+stations = stations.ravel()
+
+for tnum, stat in zip(ref_nums, stations):
+    postprocess([0, stat], tnum)
+
diff --git a/examples/fromADCIRCMap/Q_2D.py b/examples/fromADCIRCMap/Q_2D_serial.py
similarity index 87%
rename from examples/fromADCIRCMap/Q_2D.py
rename to examples/fromADCIRCMap/Q_2D_serial.py
index d701e618..acd802a2 100644
--- a/examples/fromADCIRCMap/Q_2D.py
+++ b/examples/fromADCIRCMap/Q_2D_serial.py
@@ -19,7 +19,7 @@ def postprocess(station_nums, ref_num):
     filename = 'P_q'+str(station_nums[0]+1)+'_q'+str(station_nums[1]+1)
     if len(station_nums) == 3:
         filename += '_q'+str(station_nums[2]+1)
-    filename += '_truth_'+str(ref_num+1)
+    filename += '_ref_'+str(ref_num+1)
 
     data = Q[:, station_nums]
     q_ref = Q_ref[ref_num, station_nums]
@@ -43,7 +43,7 @@ def postprocess(station_nums, ref_num):
 
     # Calculate P on lambda emulate
     (P0, lem0, io_ptr0, emulate_ptr0) = calcP.prob_emulated(samples, data,
-            rho_D_M, d_distr_samples, lam_domain, lambda_emulate, d_Tree)
+            rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
     print "Calculating prob_emulated"
     mdict['P0'] = P0
     mdict['lem0'] = lem0
@@ -52,19 +52,18 @@ def postprocess(station_nums, ref_num):
 
     # Calclate P on the actual samples with assumption that voronoi cells have
     # equal size
-    (P1, lam_vol1, io_ptr1, emulate_ptr1) = calcP.prob(samples, data,
-            rho_D_M, d_distr_samples, lam_domain, d_Tree)
+    (P1, lam_vol1, io_ptr1) = calcP.prob(samples, data,
+            rho_D_M, d_distr_samples, d_Tree)
     print "Calculating prob"
     mdict['P1'] = P1
     mdict['lam_vol1'] = lam_vol1
     mdict['lem1'] = samples
     mdict['io_ptr1'] = io_ptr1
-    mdict['emulate_ptr1'] = emulate_ptr1
 
     # Calculate P on the actual samples estimating voronoi cell volume with MC
     # integration
     (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
-            data, rho_D_M, d_distr_samples, lam_domain, lambda_emulate, d_Tree)
+            data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
     print "Calculating prob_mc"
     mdict['P3'] = P3
     mdict['lam_vol3'] = lam_vol3
diff --git a/examples/fromADCIRCMap/Q_3D_parallel.py b/examples/fromADCIRCMap/Q_3D_parallel.py
index a90659fc..bf73f2d1 100644
--- a/examples/fromADCIRCMap/Q_3D_parallel.py
+++ b/examples/fromADCIRCMap/Q_3D_parallel.py
@@ -2,11 +2,9 @@
 import bet.calculateP.simpleFunP as sfun
 import numpy as np
 import scipy.io as sio
-from mpi4py import MPI
 from bet import util
 
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
+from bet.Comm import comm, rank
 
 # Import "Truth"
 mdat = sio.loadmat('Q_3D')
@@ -43,19 +41,21 @@ def postprocess(station_nums, ref_num):
     # Calculate P on the actual samples estimating voronoi cell volume with MC
     # integration
     (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
-            data, rho_D_M, d_distr_samples, lam_domain, lambda_emulate, d_Tree)
+            data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
     print "Calculating prob_mc"
-    mdict = dict()
-    mdict['rho_D_M'] = rho_D_M
-    mdict['d_distr_samples'] = d_distr_samples 
-    mdict['lambda_emulate'] = util.get_global_values(lambda_emulate)   
-    mdict['num_l_emulate'] = mdict['lambda_emulate'].shape[1]
-    mdict['P3'] = util.get_global_values(P3)
-    mdict['lam_vol3'] = util.get_global_values(lam_vol3)
-    mdict['io_ptr3'] = util.get_global_values(io_ptr3)
-    mdict['emulate_ptr3'] = emulate_ptr3
-        
+
     if rank == 0:
+        mdict = dict()
+        mdict['rho_D_M'] = rho_D_M
+        mdict['d_distr_samples'] = d_distr_samples 
+        mdict['lambda_emulate'] = util.get_global_values(lambda_emulate)   
+        mdict['num_l_emulate'] = mdict['lambda_emulate'].shape[1]
+        mdict['P3'] = util.get_global_values(P3)
+        mdict['lam_vol3'] = lam_vol3
+        mdict['io_ptr3'] = io_ptr3
+        mdict['emulate_ptr3'] = emulate_ptr3
+            
+
         # Export P and compare to MATLAB solution visually
         sio.savemat(filename, mdict, do_compression=True)
 
diff --git a/examples/fromADCIRCMap/Q_3D_serial.py b/examples/fromADCIRCMap/Q_3D_serial.py
index 4efdeba4..34dfbdbc 100644
--- a/examples/fromADCIRCMap/Q_3D_serial.py
+++ b/examples/fromADCIRCMap/Q_3D_serial.py
@@ -37,7 +37,7 @@ def postprocess(station_nums, ref_num):
     # Calclate P on the actual samples with assumption that voronoi cells have
     # equal size
     (P1, lam_vol1, io_ptr1, emulate_ptr1) = calcP.prob(samples, data,
-            rho_D_M, d_distr_samples, lam_domain, d_Tree)
+            rho_D_M, d_distr_samples, d_Tree)
     print "Calculating prob"
     mdict['P1'] = P1
     mdict['lam_vol1'] = lam_vol1

From 6870b67e5c90f2218cc764cf5ea3f83fdfe0a174 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Fri, 8 May 2015 15:50:27 -0500
Subject: [PATCH 74/85] changed truth to reference

---
 examples/fromADCIRCMap/Q_3D_parallel.py | 2 +-
 examples/fromADCIRCMap/Q_3D_serial.py   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/fromADCIRCMap/Q_3D_parallel.py b/examples/fromADCIRCMap/Q_3D_parallel.py
index bf73f2d1..c1a1ed5a 100644
--- a/examples/fromADCIRCMap/Q_3D_parallel.py
+++ b/examples/fromADCIRCMap/Q_3D_parallel.py
@@ -22,7 +22,7 @@ def postprocess(station_nums, ref_num):
     filename = 'P_q'+str(station_nums[0]+1)+'_q'+str(station_nums[1]+1)
     if len(station_nums) == 3:
         filename += '_q'+str(station_nums[2]+1)
-    filename += '_truth_'+str(ref_num+1)
+    filename += '_ref_'+str(ref_num+1)
 
     data = Q[:, station_nums]
     q_ref = Q_ref[ref_num, station_nums]
diff --git a/examples/fromADCIRCMap/Q_3D_serial.py b/examples/fromADCIRCMap/Q_3D_serial.py
index 34dfbdbc..4840c815 100644
--- a/examples/fromADCIRCMap/Q_3D_serial.py
+++ b/examples/fromADCIRCMap/Q_3D_serial.py
@@ -19,7 +19,7 @@ def postprocess(station_nums, ref_num):
     filename = 'P_q'+str(station_nums[0]+1)+'_q'+str(station_nums[1]+1)
     if len(station_nums) == 3:
         filename += '_q'+str(station_nums[2]+1)
-    filename += '_truth_'+str(ref_num+1)
+    filename += '_ref_'+str(ref_num+1)
 
     data = Q[:, station_nums]
     q_ref = Q_ref[ref_num, station_nums]

From be726ebf5f65f62e6b9a42ebe7d8f425b90df6d5 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Fri, 8 May 2015 16:47:15 -0500
Subject: [PATCH 75/85] simplified filenames

---
 bet/sampling/adaptiveSampling.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index a9ab06c1..9557e1f3 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -256,7 +256,7 @@ def generalized_chains(self, param_min, param_max, t_set, kern,
 
         """
         if size > 1:
-            savefile = os.path.join(os.path.dirname(savefile),
+            psavefile = os.path.join(os.path.dirname(savefile),
                     "proc{}{}".format(rank, os.path.basename(savefile)))
 
         # Initialize Nx1 vector Step_size = something reasonable (based on size
@@ -275,7 +275,7 @@ def generalized_chains(self, param_min, param_max, t_set, kern,
         # hypercube/space-filling curve to fully explore parameter space - not
         # necessarily random). Call these Samples_old.
         (samples_old, data_old) = super(sampler, self).random_samples(
-                initial_sample_type, param_min, param_max, savefile,
+                initial_sample_type, param_min, param_max, psavefile,
                 self.num_chains, criterion)
         self.num_samples = self.chain_length * self.num_chains
         comm.Barrier()
@@ -324,7 +324,7 @@ def generalized_chains(self, param_min, param_max, t_set, kern,
             mdat['step_ratios'] = all_step_ratios
             mdat['samples'] = samples
             mdat['data'] = data
-            super(sampler, self).save(mdat, "p"+str(rank)+savefile)
+            super(sampler, self).save(mdat, psavefile)
 
             MYsamples_old = samples_new
 

From 93725af472c03886ab8a9625223983acf2126da4 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 13 May 2015 13:16:26 -0500
Subject: [PATCH 76/85] shortened example

---
 examples/fromADCIRCMap/Q_3D_parallel.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/examples/fromADCIRCMap/Q_3D_parallel.py b/examples/fromADCIRCMap/Q_3D_parallel.py
index c1a1ed5a..f8841629 100644
--- a/examples/fromADCIRCMap/Q_3D_parallel.py
+++ b/examples/fromADCIRCMap/Q_3D_parallel.py
@@ -67,19 +67,19 @@ def postprocess(station_nums, ref_num):
 postprocess(station_nums, ref_num)
 
 # q1, q5 ref 15
-station_nums = [0, 4] # 1, 5
-postprocess(station_nums, ref_num)
+#station_nums = [0, 4] # 1, 5
+#postprocess(station_nums, ref_num)
 
 # q1, q5, q12 ref 16
-station_nums = [0, 4, 11] # 1, 5, 12
-postprocess(station_nums, ref_num)
+#station_nums = [0, 4, 11] # 1, 5, 12
+#postprocess(station_nums, ref_num)
 
 
-station_nums = [0, 8, 6] # 1, 5, 12
-postprocess(station_nums, ref_num)
+#station_nums = [0, 8, 6] # 1, 5, 12
+#postprocess(station_nums, ref_num)
 
 
-station_nums = [0, 8, 11] # 1, 5, 12
-postprocess(station_nums, ref_num)
+#station_nums = [0, 8, 11] # 1, 5, 12
+#postprocess(station_nums, ref_num)
 
 

From c86c53654b7aff8c703a69ff2b8e6b8fb0714607 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Wed, 13 May 2015 20:01:35 -0500
Subject: [PATCH 77/85] updated adaptive sampling tests

---
 examples/fromADCIRCMap/Q_1D_parallel.py     |  6 ++++--
 test/test_sampling/test_adaptiveSampling.py | 23 +++++++++------------
 2 files changed, 14 insertions(+), 15 deletions(-)

diff --git a/examples/fromADCIRCMap/Q_1D_parallel.py b/examples/fromADCIRCMap/Q_1D_parallel.py
index 958c5632..48e1e155 100644
--- a/examples/fromADCIRCMap/Q_1D_parallel.py
+++ b/examples/fromADCIRCMap/Q_1D_parallel.py
@@ -87,6 +87,8 @@ def postprocess(station_nums, ref_num):
 ref_nums = ref_nums.ravel()
 stations = stations.ravel()
 
-for tnum, stat in zip(ref_nums, stations):
-    postprocess([0], tnum)
+postprocess([0],11) 
+
+#for tnum, stat in zip(ref_nums, stations):
+#    postprocess([0], tnum)
 
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 1ad88fbb..9bd72918 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -120,19 +120,16 @@ def ifun(outputs):
     
     # did the savefiles get created? (proper number, contain proper keys)
     mdat = {}
-    if size > 1:
-        mdat = sio.loadmat(os.path.join(os.path.dirname(savefile),
-            "proc{}{}".format(rank, os.path.basename(savefile))))
-    else:
+    if rank == 0:
         mdat = sio.loadmat(savefile)
-    nptest.assert_array_equal(samples, mdat['samples'])
-    nptest.assert_array_equal(data, mdat['data'])
-    nptest.assert_array_equal(all_step_ratios, mdat['step_ratios'])
-    assert sampler.chain_length == mdat['chain_length']
-    assert sampler.num_samples == mdat['num_samples']
-    assert sampler.num_chains == mdat['num_chains']
-    nptest.assert_array_equal(sampler.sample_batch_no,
-            np.squeeze(mdat['sample_batch_no']))
+        nptest.assert_array_equal(samples, mdat['samples'])
+        nptest.assert_array_equal(data, mdat['data'])
+        nptest.assert_array_equal(all_step_ratios, mdat['step_ratios'])
+        assert sampler.chain_length == mdat['chain_length']
+        assert sampler.num_samples == mdat['num_samples']
+        assert sampler.num_chains == mdat['num_chains']
+        nptest.assert_array_equal(sampler.sample_batch_no,
+                np.squeeze(mdat['sample_batch_no']))
 
 class Test_adaptive_sampler(unittest.TestCase):
     """
@@ -207,7 +204,7 @@ def map_10t4(x):
 
     def tearDown(self):
         for f in self.savefiles:
-            if os.path.exists(f+".mat"):
+            if rank == 0 and os.path.exists(f+".mat"):
                 os.remove(f+".mat")
         proc_savefiles = glob.glob("p{}*.mat".format(rank))
         proc_savefiles.extend(glob.glob("proc{}*.mat".format(rank)))

From 473f896f68f8cb05a98968a0f62bd56d77aae0c9 Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Thu, 14 May 2015 22:38:50 -0500
Subject: [PATCH 78/85] Fixes todos in plotP and postTools

---
 bet/postProcess/plotP.py                      | 101 ++++--------------
 bet/postProcess/postTools.py                  |   6 +-
 .../linearMap/linearMapUniformSampling.py     |   6 +-
 test/test_postProcess/test_plotP.py           |   4 +-
 4 files changed, 28 insertions(+), 89 deletions(-)

diff --git a/bet/postProcess/plotP.py b/bet/postProcess/plotP.py
index 9209e937..30331548 100644
--- a/bet/postProcess/plotP.py
+++ b/bet/postProcess/plotP.py
@@ -2,42 +2,11 @@
 This module provides methods for plotting probabilities. 
 """
 
-# NOTE: We need nbins+1 because of plotting considerations.
-""" 
-TODO: Make sure plots are transparent when saving them. Copy or emulate code
-for saving the plots from plotDomains.py
-"""
-
 from bet.Comm import *
 import matplotlib.pyplot as plt
 import numpy as np
 import copy, math
 
-def plot_voronoi_probs(P_samples, samples, lam_domain, nbins=20,
-        plot_surface=False):
-    """
-    This makes plots of the joint probabilies of input probability measure
-    defined by P_samples for 2d cases. post_process - is an input that only
-    applies to the 2d case  w.r.t. the Voronoi cells.
-
-    :param P_samples: Probabilities.
-    :type P_samples: :class:'~numpy.ndarray' of shape (num_samples,)
-    :param samples: The samples in parameter space for which the model was run.
-    :type samples: :class:'~numpy.ndarray' of shape (num_samples, ndim)
-    :param lam_domain: The domain for each parameter for the model.
-    :type lam_domain: :class:'~numpy.ndarray' of shape (ndim, 2)
-    :param nbins: Number of bins in each direction.
-    :type nbins: :int
-
-    """
-    lam_dim = lam_domain.shape[0]
-    
-    if lam_dim == 2: # Plot Voronoi tesselations, otherwise plot 2d 
-        #projections/marginals of the joint inverse measure
-        num_samples = samples.shape[0]
-        #Add fake samples outside of lam_domain to close Voronoi 
-        #tesselations at infinity
-    # TODO: finish this method
 
 def calculate_1D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
         
@@ -66,35 +35,18 @@ def calculate_1D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
     if isinstance(nbins, int):
         nbins = nbins*np.ones(num_dim, dtype=np.int)
  
-    """TODO: determine if np.histogram or np.searchsorted is faster and change
-    to appropiate method. 
-    """
-
     # Create bins
     bins = []
     for i in range(num_dim):
-        bins.append(np.linspace(lam_domain[i][0]-np.finfo(float).eps, lam_domain[i][1]+np.finfo(float).eps,
-            nbins[i]+1))
-    bin_ptr = np.zeros((num_samples, num_dim), dtype=np.int)
-    # Bin samples
-    for j in range(num_dim):
-        bin_ptr[:, j] = np.searchsorted(bins[j], samples[:, j])
-    bin_ptr -= 1
-    bins = []
-    for i in range(num_dim):
-        bins.append(np.linspace(lam_domain[i][0], lam_domain[i][1],
-                                nbins[i]+1))
-         
-    # Calculate marginal probabilities 
+        bins.append(np.linspace(lam_domain[i][0], lam_domain[i][1], nbins[i]+1))
+        
+    # Calculate marginals
     marginals = {}
     for i in range(num_dim):
-        marg = np.zeros((nbins[i]+1,))
-        # This may be sped up with logical indices
-        for k in range(num_samples):
-            marg[bin_ptr[k][i]] += P_samples[k]
+        [marg, _] = np.histogram(samples[:,i], bins=bins[i], weights=P_samples)
         marg_temp = np.copy(marg)
         comm.Allreduce([marg, MPI.DOUBLE], [marg_temp, MPI.DOUBLE], op=MPI.SUM)
-        marginals[i] = marg_temp[:-1]
+        marginals[i] = marg_temp
 
     return (bins, marginals)
 
@@ -116,47 +68,35 @@ def calculate_2D_marginal_probs(P_samples, samples, lam_domain, nbins=20):
     :returns: (bins, marginals)
 
     """
-
+    if len(samples.shape) == 1:
+        samples = np.expand_dims(samples, axis=1)
     num_samples = samples.shape[0]
     num_dim = samples.shape[1]
 
     # Make list of bins if only an integer is given
     if isinstance(nbins, int):
         nbins = nbins*np.ones(num_dim, dtype=np.int)
-    """TODO: determine if np.histogram or np.searchsorted is faster and change
-    to appropiate method. 
-    """
 
     # Create bins
-    bins = []
-    for i in range(num_dim):
-        bins.append(np.linspace(lam_domain[i][0]-np.finfo(float).eps, lam_domain[i][1]+np.finfo(float).eps, nbins[i]+1))
-    bin_ptr = np.zeros((num_samples, num_dim), dtype=np.int)
-    # Bin samples
-    for j in range(num_dim):
-        bin_ptr[:, j] = np.searchsorted(bins[j], samples[:, j])
-    bin_ptr -= 1
-
     bins = []
     for i in range(num_dim):
         bins.append(np.linspace(lam_domain[i][0], lam_domain[i][1], nbins[i]+1))
-         
-    # Calculate marginal probabilities 
+    
+    # Calculate marginals
     marginals = {}
     for i in range(num_dim):
         for j in range(i+1, num_dim):
-            marg = np.zeros((nbins[i]+1, nbins[j]+1))
-            # TODO: This may be sped up with logical indices
-            for k in range(num_samples):
-                marg[bin_ptr[k][i]][bin_ptr[k][j]] += P_samples[k]
+            (marg, _) = np.histogramdd(samples[:,[i,j]], bins = [bins[i], bins[j]], weights = P_samples)
+            marg=np.ascontiguousarray(marg)
             marg_temp = np.copy(marg)
             comm.Allreduce([marg, MPI.DOUBLE], [marg_temp, MPI.DOUBLE],
                     op=MPI.SUM) 
-            marginals[(i, j)] = marg_temp[:-1, :-1]
+            marginals[(i, j)] = marg_temp
+
     return (bins, marginals)
 
 def plot_1D_marginal_probs(marginals, bins, lam_domain,
-        filename="file", lam_ref=None, interactive=True,
+        filename="file", lam_ref=None, interactive=False,
         lambda_label=None):
         
     """
@@ -197,14 +137,15 @@ def plot_1D_marginal_probs(marginals, bins, lam_domain,
                 label1 = lambda_label[i]
             ax.set_xlabel(label1) 
             ax.set_ylabel(r'$\rho$')
-            fig.savefig(filename + "_1D_" + str(i) + ".eps")
+            fig.savefig(filename + "_1D_" + str(i) + ".eps", transparent=True)
             if interactive:
                 plt.show()
             else:
                 plt.close()
+            plt.clf()
 
 def plot_2D_marginal_probs(marginals, bins, lam_domain,
-        filename="file", lam_ref=None, plot_surface=False, interactive=True,
+        filename="file", lam_ref=None, plot_surface=False, interactive=False,
         lambda_label=None):
         
     """
@@ -260,7 +201,7 @@ def plot_2D_marginal_probs(marginals, bins, lam_domain,
             fig.colorbar(quadmesh, ax=ax, label=label_cbar)
             plt.axis([lam_domain[i][0], lam_domain[i][1], lam_domain[j][0],
                 lam_domain[j][1]]) 
-            fig.savefig(filename + "_2D_" + str(i) + "_" + str(j) + ".eps")
+            fig.savefig(filename + "_2D_" + str(i) + "_" + str(j) + ".eps", transparent=True)
             if interactive:
                 plt.show()
             else:
@@ -283,11 +224,12 @@ def plot_2D_marginal_probs(marginals, bins, lam_domain,
                 ax.set_zlabel(r'$P$')
                 plt.backgroundcolor = 'w'
                 fig.colorbar(surf, shrink=0.5, aspect=5, label=r'$P$')
-                fig.savefig(filename + "_surf_"+str(i)+"_"+str(j)+".eps")
+                fig.savefig(filename + "_surf_"+str(i)+"_"+str(j)+".eps", transparent=True)
                 if interactive:
                     plt.show()
                 else:
                     plt.close()
+                plt.clf()
 
 def smooth_marginals_1D(marginals, bins, sigma=10.0):
     """
@@ -351,8 +293,7 @@ def smooth_marginals_2D(marginals, bins, sigma=10.0):
     marginals_smooth = {}
     pairs = copy.deepcopy(marginals.keys())
     pairs.sort()
-    #TODO: k is an unused variable
-    for k, (i, j) in enumerate(pairs):   
+    for (i, j) in pairs:   
         nx = len(bins[i])-1
         ny = len(bins[j])-1
         dx = bins[i][1] - bins[i][0]
diff --git a/bet/postProcess/postTools.py b/bet/postProcess/postTools.py
index c79b1b4e..8c648298 100644
--- a/bet/postProcess/postTools.py
+++ b/bet/postProcess/postTools.py
@@ -1,7 +1,5 @@
 """
-TODO: rewrite to match what the module acutally does
-
-This module provides methods for plotting probabilities. 
+This module provides methods for postprocessing probabilities and data. 
 """
 from bet.Comm import rank
 import numpy as np
@@ -70,8 +68,6 @@ def sample_highest_prob(top_percentile, P_samples, samples, lam_vol=None,
     """
     if len(samples.shape) == 1:
         samples = np.expand_dims(samples, axis=1)
-    # TODO: should we trust the user or should we do a quick check of part of
-    # the array to see if it's already sorted?
     if sort:
         (P_samples, samples, lam_vol, data) = sort_by_rho(P_samples, samples,
                 lam_vol, data)
diff --git a/examples/linearMap/linearMapUniformSampling.py b/examples/linearMap/linearMapUniformSampling.py
index 553704c4..c2825adc 100644
--- a/examples/linearMap/linearMapUniformSampling.py
+++ b/examples/linearMap/linearMapUniformSampling.py
@@ -69,6 +69,8 @@
 
 # calc data
 data= np.dot(samples,Q_map)
+np.savetxt('3to2_samples.txt.gz', samples)
+np.savetxt('3to2_data.txt.gz', data)
 
 '''
 Suggested changes for user:
@@ -122,13 +124,13 @@
     Monte Carlo (it converges like 1/sqrt(n_samples)).
 '''
 #lambda_emulate = samples
-lambda_emulate = calculateP.emulate_iid_lebesgue(lam_domain=lam_domain, num_l_emulate = 1E5)
+lambda_emulate = calculateP.emulate_iid_lebesgue(lam_domain=lam_domain, num_l_emulate = 1E4)
 
 
 # calculate probablities
 (P,  lambda_emulate, io_ptr, emulate_ptr) = calculateP.prob_emulated(samples=samples,
                                                                      data=data, rho_D_M = d_distr_prob, d_distr_samples = d_distr_samples,
-                                                                     lam_domain=lam_domain, lambda_emulate=lambda_emulate, d_Tree=d_Tree)
+                                                                      lambda_emulate=lambda_emulate, d_Tree=d_Tree)
 # calculate 2d marginal probs
 '''
 Suggested changes for user:
diff --git a/test/test_postProcess/test_plotP.py b/test/test_postProcess/test_plotP.py
index 69a1d25f..3ee69efe 100644
--- a/test/test_postProcess/test_plotP.py
+++ b/test/test_postProcess/test_plotP.py
@@ -30,7 +30,7 @@ def setUp(self):
         self.lam_domain=np.array([[0.0,1.0]])
         num_samples=1000
         self.samples = np.linspace(self.lam_domain[0][0], self.lam_domain[0][1], num_samples+1)
-        self.P_samples = (1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
+        self.P_samples = 1.0/float(size)*(1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
         
     def test_1_bin(self):
         """
@@ -65,7 +65,7 @@ def setUp(self):
         """
         self.lam_domain=np.array([[0.0,1.0],[0.0,1.0]])
         self.samples=util.meshgrid_ndim((np.linspace(self.lam_domain[0][0], self.lam_domain[0][1], 10),np.linspace(self.lam_domain[1][0], self.lam_domain[1][1], 10)))
-        self.P_samples = (1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
+        self.P_samples = 1.0/float(size)*(1.0/float(self.samples.shape[0]))*np.ones((self.samples.shape[0],))
         
     def test_1_bin_1D(self):
         """ 

From 8791bd7a0e1c42bf967051b568c4c555614f2e00 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Fri, 15 May 2015 10:59:07 -0500
Subject: [PATCH 79/85] updating examples

---
 examples/fromADCIRCMap/Q_1D_parallel.py | 47 +++++++++++++++----------
 examples/fromADCIRCMap/Q_1D_serial.py   |  2 +-
 examples/fromADCIRCMap/Q_2D_parallel.py |  2 +-
 examples/fromADCIRCMap/Q_2D_serial.py   |  2 +-
 4 files changed, 31 insertions(+), 22 deletions(-)

diff --git a/examples/fromADCIRCMap/Q_1D_parallel.py b/examples/fromADCIRCMap/Q_1D_parallel.py
index 48e1e155..229c6bd0 100644
--- a/examples/fromADCIRCMap/Q_1D_parallel.py
+++ b/examples/fromADCIRCMap/Q_1D_parallel.py
@@ -32,52 +32,61 @@ def postprocess(station_nums, ref_num):
     (rho_D_M, d_distr_samples, d_Tree) = sfun.uniform_hyperrectangle(data,
             q_ref, bin_ratio=0.15,
             center_pts_per_edge=np.ones((data.shape[1],)))
+    
+    # Calclate P on the actual samples with assumption that voronoi cells have
+    # equal size
+    (P1, lam_vol1, io_ptr1) = calcP.prob(samples, data,
+            rho_D_M, d_distr_samples, d_Tree)
+    if rank == 0:
+        print "Consolidating prob"
+        mdict = dict()
+        mdict['P1'] = util.get_global_values(P1)
+        mdict['lam_vol1'] = lam_vol1
+        mdict['lem1'] = samples
+        mdict['io_ptr1'] = io_ptr1
+        # Export P
+        sio.savemat(filename, mdict, do_compression=True)
 
-    num_l_emulate = 1e6
+    """    
+    num_l_emulate = 1e4
     lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
     print "Finished emulating lambda samples"
 
     if rank == 0:
-        mdict = dict()
         mdict['rho_D_M'] = rho_D_M
         mdict['d_distr_samples'] = d_distr_samples 
         mdict['num_l_emulate'] = num_l_emulate
-        #mdict['lambda_emulate'] = lambda_emulate
-
+        # Export P
+        sio.savemat(filename, mdict, do_compression=True)
+    """
+    """
     # Calculate P on lambda emulate
     (P0, lem0, io_ptr0, emulate_ptr0) = calcP.prob_emulated(samples, data,
             rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
 
     if rank == 0:
-        print "Calculating prob_emulated"
+        print "Consolidating prob_emulated"
         mdict['P0'] = util.get_global_values(P0)
         mdict['lem0'] = lem0
         mdict['io_ptr0'] = io_ptr0
         mdict['emulate_ptr0'] = emulate_ptr0
-
-    # Calclate P on the actual samples with assumption that voronoi cells have
-    # equal size
-    (P1, lam_vol1, io_ptr1) = calcP.prob(samples, data,
-            rho_D_M, d_distr_samples, d_Tree)
-    if rank == 0:
-        print "Calculating prob"
-        mdict['P1'] = util.get_global_values(P1)
-        mdict['lam_vol1'] = lam_vol1
-        mdict['lem1'] = samples
-        mdict['io_ptr1'] = io_ptr1
-
+        # Export P
+        sio.savemat(filename, mdict, do_compression=True)
+    """
+    """
     # Calculate P on the actual samples estimating voronoi cell volume with MC
     # integration
     (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
             data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
     if rank == 0:
-        print "Calculating prob_mc"
+        print "Consolidating prob_mc"
         mdict['P3'] = util.get_global_values(P3)
         mdict['lam_vol3'] = lam_vol3
         mdict['io_ptr3'] = io_ptr3
         mdict['emulate_ptr3'] = emulate_ptr3
-        # Export P and compare to MATLAB solution visually
+        # Export P
         sio.savemat(filename, mdict, do_compression=True)
+    """
 
 # Post-process and save P and emulated points
 ref_nums = [6, 11, 15] # 7, 12, 16
diff --git a/examples/fromADCIRCMap/Q_1D_serial.py b/examples/fromADCIRCMap/Q_1D_serial.py
index 48f28072..1d16c16d 100644
--- a/examples/fromADCIRCMap/Q_1D_serial.py
+++ b/examples/fromADCIRCMap/Q_1D_serial.py
@@ -69,7 +69,7 @@ def postprocess(station_nums, ref_num):
     mdict['lam_vol3'] = lam_vol3
     mdict['io_ptr3'] = io_ptr3
     mdict['emulate_ptr3'] = emulate_ptr3
-    # Export P and compare to MATLAB solution visually
+    # Export P
     sio.savemat(filename, mdict, do_compression=True)
 
 # Post-process and save P and emulated points
diff --git a/examples/fromADCIRCMap/Q_2D_parallel.py b/examples/fromADCIRCMap/Q_2D_parallel.py
index da1d665b..4ee4cec6 100644
--- a/examples/fromADCIRCMap/Q_2D_parallel.py
+++ b/examples/fromADCIRCMap/Q_2D_parallel.py
@@ -74,7 +74,7 @@ def postprocess(station_nums, ref_num):
         mdict['lam_vol3'] = lam_vol3
         mdict['io_ptr3'] = io_ptr3
         mdict['emulate_ptr3'] = emulate_ptr3
-        # Export P and compare to MATLAB solution visually
+        # Export P
         sio.savemat(filename, mdict, do_compression=True)
 
 # Post-process and save P and emulated points
diff --git a/examples/fromADCIRCMap/Q_2D_serial.py b/examples/fromADCIRCMap/Q_2D_serial.py
index acd802a2..8a18f3c3 100644
--- a/examples/fromADCIRCMap/Q_2D_serial.py
+++ b/examples/fromADCIRCMap/Q_2D_serial.py
@@ -69,7 +69,7 @@ def postprocess(station_nums, ref_num):
     mdict['lam_vol3'] = lam_vol3
     mdict['io_ptr3'] = io_ptr3
     mdict['emulate_ptr3'] = emulate_ptr3
-    # Export P and compare to MATLAB solution visually
+    # Export P 
     sio.savemat(filename, mdict, do_compression=True)
 
 # Post-process and save P and emulated points

From a5325144ad5fcb12641f171e54ae5c6728c3c3bf Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Fri, 15 May 2015 11:06:05 -0500
Subject: [PATCH 80/85] updated 3D example

---
 examples/fromADCIRCMap/Q_3D_parallel.py | 26 ++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/examples/fromADCIRCMap/Q_3D_parallel.py b/examples/fromADCIRCMap/Q_3D_parallel.py
index f8841629..cc3bd407 100644
--- a/examples/fromADCIRCMap/Q_3D_parallel.py
+++ b/examples/fromADCIRCMap/Q_3D_parallel.py
@@ -40,9 +40,9 @@ def postprocess(station_nums, ref_num):
 
     # Calculate P on the actual samples estimating voronoi cell volume with MC
     # integration
+    print "Calculating prob_mc"
     (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
             data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
-    print "Calculating prob_mc"
 
     if rank == 0:
         mdict = dict()
@@ -54,9 +54,8 @@ def postprocess(station_nums, ref_num):
         mdict['lam_vol3'] = lam_vol3
         mdict['io_ptr3'] = io_ptr3
         mdict['emulate_ptr3'] = emulate_ptr3
-            
-
-        # Export P and compare to MATLAB solution visually
+        print "Exporting P"    
+        # Export P 
         sio.savemat(filename, mdict, do_compression=True)
 
 # Post-process and save P and emulated points
@@ -66,20 +65,21 @@ def postprocess(station_nums, ref_num):
 station_nums = [0, 4, 1] # 1, 5, 2
 postprocess(station_nums, ref_num)
 
+"""
 # q1, q5 ref 15
-#station_nums = [0, 4] # 1, 5
-#postprocess(station_nums, ref_num)
+station_nums = [0, 4] # 1, 5
+postprocess(station_nums, ref_num)
 
 # q1, q5, q12 ref 16
-#station_nums = [0, 4, 11] # 1, 5, 12
-#postprocess(station_nums, ref_num)
-
+station_nums = [0, 4, 11] # 1, 5, 12
+postprocess(station_nums, ref_num)
 
-#station_nums = [0, 8, 6] # 1, 5, 12
-#postprocess(station_nums, ref_num)
 
+station_nums = [0, 8, 6] # 1, 5, 12
+postprocess(station_nums, ref_num)
 
-#station_nums = [0, 8, 11] # 1, 5, 12
-#postprocess(station_nums, ref_num)
 
+station_nums = [0, 8, 11] # 1, 5, 12
+postprocess(station_nums, ref_num)
+"""
 

From 2495a389262c36b8d11ce96dbcf442fc8ca25826 Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Fri, 15 May 2015 12:04:13 -0500
Subject: [PATCH 81/85] reduced test runtime for plotDOmains to 51 s

---
 test/test_postProcess/test_plotDomains.py | 64 +++++++----------------
 1 file changed, 20 insertions(+), 44 deletions(-)

diff --git a/test/test_postProcess/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
index e8bdbfbd..841e79e6 100644
--- a/test/test_postProcess/test_plotDomains.py
+++ b/test/test_postProcess/test_plotDomains.py
@@ -106,11 +106,8 @@ def test_scatter_2D(self):
         """
         sample_nos = [None, 25]
         p_ref = [None, self.samples[4, [0, 1]]]
-        save = [True, False]
-        for sn in sample_nos:
-            for pr in p_ref:
-                for s in save:
-                    self.check_scatter_2D(sn, pr, s)
+        for sn, pr in zip(sample_nos, p_ref):
+            self.check_scatter_2D(sn, pr, True)
 
     def check_scatter_2D(self, sample_nos, p_ref, save):
         """
@@ -133,11 +130,8 @@ def test_scatter_3D(self):
         """
         sample_nos = [None, 25]
         p_ref = [None, self.samples[4, :]]
-        save = [True, False]
-        for sn in sample_nos:
-            for pr in p_ref:
-                for s in save:
-                    self.check_scatter_3D(sn, pr, s)
+        for sn, pr in zip(sample_nos, p_ref):
+                self.check_scatter_3D(sn, pr, True)
 
     def check_scatter_3D(self, sample_nos, p_ref, save):
         """
@@ -158,7 +152,6 @@ def test_show_param(self):
         Test :meth:`bet.postProcess.plotDomains.show_param`
         """
         sample_nos = [None, 25]
-        save = [True, False]
         samples = [self.samples, self.samples[:, [0, 1]],
                 self.samples[:, [0, 1, 2]]]
         lnums = [None, self.lnums]
@@ -170,13 +163,9 @@ def test_show_param(self):
             if sample.shape[0] > 3:
                 showdim.append(3)
             for sd in showdim:
-                for ln in lnums:
-                    for sn in sample_nos:
-                        p_ref = [None, sample[4, :]]
-                        for pr in p_ref:
-                            for s in save:
-                                self.check_show_param(sample, sn, pr, s, ln,
-                                        sd)
+                p_ref = [None, sample[4, :]]
+                for ln, sn, pr in zip(lnums, sample_nos, p_ref):
+                            self.check_show_param(sample, sn, pr, True, ln, sd)
 
     def check_show_param(self, samples, sample_nos, p_ref, save, lnums,
             showdim):
@@ -197,23 +186,18 @@ def test_show_data(self):
         Test :meth:`bet.postProcess.plotDomains.show_data`
         """
         sample_nos = [None, 25]
-        save = [True, False]
         data_sets = [self.data, self.data[:, [0, 1]]]
         qnums = [None, [0, 1, 2]]#self.lnums]
 
-        for data in data_sets:
+        for data, qn, sn in zip(data_sets, qnums, sample_nos):
             showdim = [None]
             if data.shape[0] > 2:
                 showdim.append(2)
             if data.shape[0] > 3:
                 showdim.append(3)
-            for sd in showdim:
-                for qn in qnums:
-                    for sn in sample_nos:
-                        Q_ref = [None, data[4, :]]
-                        for qr in Q_ref:
-                            for s in save:
-                                self.check_show_data(data, sn, qr, s, qn, sd)
+            Q_ref = [None, data[4, :]]
+            for sd, qr in zip(showdim, Q_ref):
+                self.check_show_data(data, sn, qr, True, qn, sd)
 
     def check_show_data(self, data, sample_nos, q_ref, save, qnums, showdim):
         """
@@ -245,17 +229,10 @@ def test_show_data_domain_2D(self):
         """
         ref_markers = [None, self.markers]
         ref_colors = [None, self.colors]
-        triangulation = tri.Triangulation(self.samples[:, 0], self.samples[:, 1])
-        triangles = [None, triangulation.triangles]
         filenames = [None, ['domain_q1_q1_cs.eps', 'q1_q2_domain_Q_cs.eps']]
-        save = [None, False]
 
-        for rm in ref_markers:
-            for rc in ref_colors:
-                for t in triangles:
-                    for s in save:
-                        for fn in filenames:
-                            self.check_show_data_domain_2D(rm, rc, t, s, fn)
+        for rm, rc, fn in zip(ref_markers, ref_colors, filenames):
+            self.check_show_data_domain_2D(rm, rc, None, True, fn)
 
     def check_show_data_domain_2D(self, ref_markers, ref_colors, triangles,
             save, filenames):
@@ -286,14 +263,13 @@ def test_show_data_domain_multi(self):
         Q_nums = [None, [1, 2], [1, 2, 3]]
         ref_markers = [None, self.markers]
         ref_colors = [None, self.colors]
-        for rm in ref_markers:
-            for rc in ref_colors:
-                for qn in Q_nums:
-                    showdim = [None, 1]
-                    if qn and len(qn) > 2:
-                        showdim.extend(['all', 'ALL'])
-                    for sd in showdim:
-                        self.check_show_data_domain_multi(rm, rc, qn, sd)
+        for rm, rc in zip(ref_markers, ref_colors):
+            for qn in Q_nums:
+                showdim = [None, 1]
+                if qn and len(qn) > 2:
+                    showdim.extend(['all', 'ALL'])
+                for sd in showdim:
+                    self.check_show_data_domain_multi(rm, rc, qn, sd)
 
     def check_show_data_domain_multi(self, ref_markers, ref_colors, Q_nums,
             showdim):

From 0502cb1f326db387a0d4e52a9c52666beec18f7a Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Fri, 15 May 2015 12:20:29 -0500
Subject: [PATCH 82/85] all examples now run

---
 examples/fromADCIRCMap/Q_1D_parallel.py | 103 ------------------------
 examples/fromADCIRCMap/Q_3D_parallel.py |  85 -------------------
 examples/fromADCIRCMap/Q_3D_serial.py   |   8 +-
 3 files changed, 4 insertions(+), 192 deletions(-)
 delete mode 100644 examples/fromADCIRCMap/Q_1D_parallel.py
 delete mode 100644 examples/fromADCIRCMap/Q_3D_parallel.py

diff --git a/examples/fromADCIRCMap/Q_1D_parallel.py b/examples/fromADCIRCMap/Q_1D_parallel.py
deleted file mode 100644
index 229c6bd0..00000000
--- a/examples/fromADCIRCMap/Q_1D_parallel.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import bet.calculateP.calculateP as calcP
-import bet.calculateP.simpleFunP as sfun
-import numpy as np
-import scipy.io as sio
-from bet.Comm import rank
-import bet.util as util
-
-# Import "Truth"
-mdat = sio.loadmat('Q_2D')
-Q = mdat['Q']
-Q_ref = mdat['Q_true']
-
-# Import Data
-samples = mdat['points'].transpose()
-lam_domain = np.array([[0.07, .15], [0.1, 0.2]])
-
-print "Finished loading data"
-
-def postprocess(station_nums, ref_num):
-    
-    filename = 'P_q'+str(station_nums[0]+1)+'_q'
-    if len(station_nums) == 3:
-        filename += '_q'+str(station_nums[2]+1)
-    filename += '_ref_'+str(ref_num+1)
-
-    data = Q[:, station_nums]
-    q_ref = Q_ref[ref_num, station_nums]
-
-    # Create Simple function approximation
-    # Save points used to parition D for simple function approximation and the
-    # approximation itself (this can be used to make close comparisions...)
-    (rho_D_M, d_distr_samples, d_Tree) = sfun.uniform_hyperrectangle(data,
-            q_ref, bin_ratio=0.15,
-            center_pts_per_edge=np.ones((data.shape[1],)))
-    
-    # Calclate P on the actual samples with assumption that voronoi cells have
-    # equal size
-    (P1, lam_vol1, io_ptr1) = calcP.prob(samples, data,
-            rho_D_M, d_distr_samples, d_Tree)
-    if rank == 0:
-        print "Consolidating prob"
-        mdict = dict()
-        mdict['P1'] = util.get_global_values(P1)
-        mdict['lam_vol1'] = lam_vol1
-        mdict['lem1'] = samples
-        mdict['io_ptr1'] = io_ptr1
-        # Export P
-        sio.savemat(filename, mdict, do_compression=True)
-
-    """    
-    num_l_emulate = 1e4
-    lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
-    print "Finished emulating lambda samples"
-
-    if rank == 0:
-        mdict['rho_D_M'] = rho_D_M
-        mdict['d_distr_samples'] = d_distr_samples 
-        mdict['num_l_emulate'] = num_l_emulate
-        # Export P
-        sio.savemat(filename, mdict, do_compression=True)
-    """
-    """
-    # Calculate P on lambda emulate
-    (P0, lem0, io_ptr0, emulate_ptr0) = calcP.prob_emulated(samples, data,
-            rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
-
-    if rank == 0:
-        print "Consolidating prob_emulated"
-        mdict['P0'] = util.get_global_values(P0)
-        mdict['lem0'] = lem0
-        mdict['io_ptr0'] = io_ptr0
-        mdict['emulate_ptr0'] = emulate_ptr0
-        # Export P
-        sio.savemat(filename, mdict, do_compression=True)
-    """
-    """
-    # Calculate P on the actual samples estimating voronoi cell volume with MC
-    # integration
-    (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
-            data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
-    if rank == 0:
-        print "Consolidating prob_mc"
-        mdict['P3'] = util.get_global_values(P3)
-        mdict['lam_vol3'] = lam_vol3
-        mdict['io_ptr3'] = io_ptr3
-        mdict['emulate_ptr3'] = emulate_ptr3
-        # Export P
-        sio.savemat(filename, mdict, do_compression=True)
-    """
-
-# Post-process and save P and emulated points
-ref_nums = [6, 11, 15] # 7, 12, 16
-stations = [1, 4, 5] # 2, 5, 6
-
-ref_nums, stations = np.meshgrid(ref_nums, stations)
-ref_nums = ref_nums.ravel()
-stations = stations.ravel()
-
-postprocess([0],11) 
-
-#for tnum, stat in zip(ref_nums, stations):
-#    postprocess([0], tnum)
-
diff --git a/examples/fromADCIRCMap/Q_3D_parallel.py b/examples/fromADCIRCMap/Q_3D_parallel.py
deleted file mode 100644
index cc3bd407..00000000
--- a/examples/fromADCIRCMap/Q_3D_parallel.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import bet.calculateP.calculateP as calcP
-import bet.calculateP.simpleFunP as sfun
-import numpy as np
-import scipy.io as sio
-from bet import util
-
-from bet.Comm import comm, rank
-
-# Import "Truth"
-mdat = sio.loadmat('Q_3D')
-Q = mdat['Q']
-Q_ref = mdat['Q_true']
-
-# Import Data
-samples = mdat['points'].transpose()
-lam_domain = np.array([[-900, 1200], [0.07, .15], [0.1, 0.2]])
-
-print "Finished loading data"
-
-def postprocess(station_nums, ref_num):
-    
-    filename = 'P_q'+str(station_nums[0]+1)+'_q'+str(station_nums[1]+1)
-    if len(station_nums) == 3:
-        filename += '_q'+str(station_nums[2]+1)
-    filename += '_ref_'+str(ref_num+1)
-
-    data = Q[:, station_nums]
-    q_ref = Q_ref[ref_num, station_nums]
-
-    # Create Simple function approximation
-    # Save points used to parition D for simple function approximation and the
-    # approximation itself (this can be used to make close comparisions...)
-    (rho_D_M, d_distr_samples, d_Tree) = sfun.uniform_hyperrectangle(data,
-            q_ref, bin_ratio=0.15,
-            center_pts_per_edge=np.ones((data.shape[1],)))
-
-    num_l_emulate = 1e6
-    lambda_emulate = calcP.emulate_iid_lebesgue(lam_domain, num_l_emulate)
-    print "Finished emulating lambda samples"
-
-    # Calculate P on the actual samples estimating voronoi cell volume with MC
-    # integration
-    print "Calculating prob_mc"
-    (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
-            data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
-
-    if rank == 0:
-        mdict = dict()
-        mdict['rho_D_M'] = rho_D_M
-        mdict['d_distr_samples'] = d_distr_samples 
-        mdict['lambda_emulate'] = util.get_global_values(lambda_emulate)   
-        mdict['num_l_emulate'] = mdict['lambda_emulate'].shape[1]
-        mdict['P3'] = util.get_global_values(P3)
-        mdict['lam_vol3'] = lam_vol3
-        mdict['io_ptr3'] = io_ptr3
-        mdict['emulate_ptr3'] = emulate_ptr3
-        print "Exporting P"    
-        # Export P 
-        sio.savemat(filename, mdict, do_compression=True)
-
-# Post-process and save P and emulated points
-ref_num = 14
-
-# q1, q5, q2 ref 15
-station_nums = [0, 4, 1] # 1, 5, 2
-postprocess(station_nums, ref_num)
-
-"""
-# q1, q5 ref 15
-station_nums = [0, 4] # 1, 5
-postprocess(station_nums, ref_num)
-
-# q1, q5, q12 ref 16
-station_nums = [0, 4, 11] # 1, 5, 12
-postprocess(station_nums, ref_num)
-
-
-station_nums = [0, 8, 6] # 1, 5, 12
-postprocess(station_nums, ref_num)
-
-
-station_nums = [0, 8, 11] # 1, 5, 12
-postprocess(station_nums, ref_num)
-"""
-
diff --git a/examples/fromADCIRCMap/Q_3D_serial.py b/examples/fromADCIRCMap/Q_3D_serial.py
index 4840c815..29f15cad 100644
--- a/examples/fromADCIRCMap/Q_3D_serial.py
+++ b/examples/fromADCIRCMap/Q_3D_serial.py
@@ -36,14 +36,13 @@ def postprocess(station_nums, ref_num):
 
     # Calclate P on the actual samples with assumption that voronoi cells have
     # equal size
-    (P1, lam_vol1, io_ptr1, emulate_ptr1) = calcP.prob(samples, data,
-            rho_D_M, d_distr_samples, d_Tree)
+    (P1, lam_vol1, io_ptr1) = calcP.prob(samples, data, rho_D_M,
+            d_distr_samples, d_Tree)
     print "Calculating prob"
     mdict['P1'] = P1
     mdict['lam_vol1'] = lam_vol1
     mdict['lem1'] = samples
     mdict['io_ptr1'] = io_ptr1
-    mdict['emulate_ptr1'] = emulate_ptr1
 
     # Export P and compare to MATLAB solution visually
     sio.savemat(filename, mdict, do_compression=True)
@@ -55,6 +54,7 @@ def postprocess(station_nums, ref_num):
 station_nums = [0, 4, 1] # 1, 5, 2
 postprocess(station_nums, ref_num)
 
+"""
 # q1, q5 ref 15
 station_nums = [0, 4] # 1, 5
 postprocess(station_nums, ref_num)
@@ -71,5 +71,5 @@ def postprocess(station_nums, ref_num):
 
 station_nums = [0, 8, 11] # 1, 5, 12
 postprocess(station_nums, ref_num)
-
+"""
 

From cd60ec82a3f7bb576a7574171bf153d9e11ab97a Mon Sep 17 00:00:00 2001
From: Steve Mattis <steve.a.mattis@gmail.com>
Date: Fri, 15 May 2015 12:43:45 -0500
Subject: [PATCH 83/85] Adds copyright to files

---
 bet/Comm.py                                    | 2 ++
 bet/__init__.py                                | 2 ++
 bet/calculateP/__init__.py                     | 2 ++
 bet/calculateP/calculateP.py                   | 2 ++
 bet/calculateP/simpleFunP.py                   | 2 ++
 bet/calculateP/voronoiHistogram.py             | 2 ++
 bet/postProcess/__init__.py                    | 2 ++
 bet/postProcess/plotDomains.py                 | 2 ++
 bet/postProcess/plotP.py                       | 2 ++
 bet/postProcess/postTools.py                   | 2 ++
 bet/sampling/__init__.py                       | 2 ++
 bet/sampling/adaptiveSampling.py               | 2 ++
 bet/sampling/basicSampling.py                  | 2 ++
 bet/util.py                                    | 2 ++
 doc/conf.py                                    | 2 ++
 examples/fromADCIRCMap/Q_1D_serial.py          | 2 ++
 examples/fromADCIRCMap/Q_2D_parallel.py        | 2 ++
 examples/fromADCIRCMap/Q_2D_serial.py          | 2 ++
 examples/fromADCIRCMap/Q_3D_serial.py          | 2 ++
 examples/fromADCIRCMap/adaptive_2D.py          | 3 +++
 examples/fromADCIRCMap/adaptive_3D.py          | 3 +++
 examples/fromFileMap/fromFile2D.py             | 3 +++
 examples/fromFileMap/sandbox_test_2D.py        | 3 +++
 examples/fromFileMap/sandbox_test_3D.py        | 3 +++
 examples/linearMap/linearMapUniformSampling.py | 3 +++
 setup.py                                       | 3 +++
 test/__init__.py                               | 2 ++
 test/test_Comm.py                              | 2 ++
 test/test_calculateP/__init__.py               | 2 ++
 test/test_calculateP/test_calculateP.py        | 2 ++
 test/test_calculateP/test_simpleFunP.py        | 2 ++
 test/test_calculateP/test_voronoiHistogram.py  | 2 ++
 test/test_postProcess/test_plotDomains.py      | 2 ++
 test/test_postProcess/test_plotP.py            | 2 ++
 test/test_postProcess/test_postTools.py        | 2 ++
 test/test_sampling/__init__.py                 | 2 ++
 test/test_sampling/test_adaptiveSampling.py    | 2 ++
 test/test_sampling/test_basicSampling.py       | 2 ++
 test/test_util.py                              | 2 ++
 39 files changed, 85 insertions(+)
 mode change 100755 => 100644 examples/fromADCIRCMap/adaptive_2D.py
 mode change 100755 => 100644 examples/fromADCIRCMap/adaptive_3D.py
 mode change 100755 => 100644 examples/fromFileMap/fromFile2D.py
 mode change 100755 => 100644 examples/fromFileMap/sandbox_test_2D.py
 mode change 100755 => 100644 examples/fromFileMap/sandbox_test_3D.py

diff --git a/bet/Comm.py b/bet/Comm.py
index e7c69c04..20684abf 100644
--- a/bet/Comm.py
+++ b/bet/Comm.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 This module provides a workaround for people without mpi4py installed
 to run BET.
diff --git a/bet/__init__.py b/bet/__init__.py
index 8f8838d3..9d87ac59 100644
--- a/bet/__init__.py
+++ b/bet/__init__.py
@@ -1 +1,3 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 __all__ = ['sampling', 'calculateP', 'postProcess', 'loadBalance', 'util','Comm']
diff --git a/bet/calculateP/__init__.py b/bet/calculateP/__init__.py
index 5bd16370..de72a3b3 100644
--- a/bet/calculateP/__init__.py
+++ b/bet/calculateP/__init__.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 r""" 
 This subpackage provides classes and methods for calulating the
 probability measure :math:`P_{\Lambda}`.
diff --git a/bet/calculateP/calculateP.py b/bet/calculateP/calculateP.py
index 232205a1..40dc6b53 100644
--- a/bet/calculateP/calculateP.py
+++ b/bet/calculateP/calculateP.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 r""" 
 This module provides methods for calulating the probability measure
 :math:`P_{\Lambda}`.
diff --git a/bet/calculateP/simpleFunP.py b/bet/calculateP/simpleFunP.py
index 9819ffde..e2340194 100644
--- a/bet/calculateP/simpleFunP.py
+++ b/bet/calculateP/simpleFunP.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 This module provides methods for creating simple funciton approximations to be
 used by :mod:`~bet.calculateP.calculateP`.
diff --git a/bet/calculateP/voronoiHistogram.py b/bet/calculateP/voronoiHistogram.py
index b7932168..28eac24f 100644
--- a/bet/calculateP/voronoiHistogram.py
+++ b/bet/calculateP/voronoiHistogram.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # -*- coding: utf-8 -*-
 """
 This module provides methods for creating the edges and generating point for
diff --git a/bet/postProcess/__init__.py b/bet/postProcess/__init__.py
index 3fc54256..e1ff3ea1 100644
--- a/bet/postProcess/__init__.py
+++ b/bet/postProcess/__init__.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 r"""
 This subpackage contains
 
diff --git a/bet/postProcess/plotDomains.py b/bet/postProcess/plotDomains.py
index 4fe1f84a..2ff1a5d3 100644
--- a/bet/postProcess/plotDomains.py
+++ b/bet/postProcess/plotDomains.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 This module provides methods used to plot two-dimensional domains and/or
 two-dimensional slices/projections of domains.
diff --git a/bet/postProcess/plotP.py b/bet/postProcess/plotP.py
index 30331548..c3fb9285 100644
--- a/bet/postProcess/plotP.py
+++ b/bet/postProcess/plotP.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 This module provides methods for plotting probabilities. 
 """
diff --git a/bet/postProcess/postTools.py b/bet/postProcess/postTools.py
index 8c648298..acb70e61 100644
--- a/bet/postProcess/postTools.py
+++ b/bet/postProcess/postTools.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 This module provides methods for postprocessing probabilities and data. 
 """
diff --git a/bet/sampling/__init__.py b/bet/sampling/__init__.py
index 6c878f87..4e5806f8 100644
--- a/bet/sampling/__init__.py
+++ b/bet/sampling/__init__.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 This subpackage contains
 
diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index 9557e1f3..fe2bfeb5 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # -*- coding: utf-8 -*-
 # Lindley Graham 3/10/2014
 """
diff --git a/bet/sampling/basicSampling.py b/bet/sampling/basicSampling.py
index b7c4c0e3..c7592f3f 100644
--- a/bet/sampling/basicSampling.py
+++ b/bet/sampling/basicSampling.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # Lindley Graham 4/15/2014
 """
 This module contains functions for sampling. We assume we are given access to a
diff --git a/bet/util.py b/bet/util.py
index 9bbe7f80..4f3a0258 100644
--- a/bet/util.py
+++ b/bet/util.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 The module contains general tools for BET.
 """
diff --git a/doc/conf.py b/doc/conf.py
index c7de5658..64b3bcd9 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # -*- coding: utf-8 -*-
 #
 # BET documentation build configuration file, created by
diff --git a/examples/fromADCIRCMap/Q_1D_serial.py b/examples/fromADCIRCMap/Q_1D_serial.py
index 1d16c16d..c0c14ec9 100644
--- a/examples/fromADCIRCMap/Q_1D_serial.py
+++ b/examples/fromADCIRCMap/Q_1D_serial.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 import bet.calculateP.calculateP as calcP
 import bet.calculateP.simpleFunP as sfun
 import numpy as np
diff --git a/examples/fromADCIRCMap/Q_2D_parallel.py b/examples/fromADCIRCMap/Q_2D_parallel.py
index 4ee4cec6..79a00ef2 100644
--- a/examples/fromADCIRCMap/Q_2D_parallel.py
+++ b/examples/fromADCIRCMap/Q_2D_parallel.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 import bet.calculateP.calculateP as calcP
 import bet.calculateP.simpleFunP as sfun
 import numpy as np
diff --git a/examples/fromADCIRCMap/Q_2D_serial.py b/examples/fromADCIRCMap/Q_2D_serial.py
index 8a18f3c3..56be87dc 100644
--- a/examples/fromADCIRCMap/Q_2D_serial.py
+++ b/examples/fromADCIRCMap/Q_2D_serial.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 import bet.calculateP.calculateP as calcP
 import bet.calculateP.simpleFunP as sfun
 import numpy as np
diff --git a/examples/fromADCIRCMap/Q_3D_serial.py b/examples/fromADCIRCMap/Q_3D_serial.py
index 29f15cad..f0a1f0e3 100644
--- a/examples/fromADCIRCMap/Q_3D_serial.py
+++ b/examples/fromADCIRCMap/Q_3D_serial.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 import bet.calculateP.calculateP as calcP
 import bet.calculateP.simpleFunP as sfun
 import numpy as np
diff --git a/examples/fromADCIRCMap/adaptive_2D.py b/examples/fromADCIRCMap/adaptive_2D.py
old mode 100755
new mode 100644
index 3a2fb5d4..1922df6d
--- a/examples/fromADCIRCMap/adaptive_2D.py
+++ b/examples/fromADCIRCMap/adaptive_2D.py
@@ -1,4 +1,7 @@
 #! /usr/bin/env python
+
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # import necessary modules
 import polyadcirc.run_framework.domain as dom
 import polyadcirc.run_framework.random_wall_Q as rmw
diff --git a/examples/fromADCIRCMap/adaptive_3D.py b/examples/fromADCIRCMap/adaptive_3D.py
old mode 100755
new mode 100644
index 8310ece7..e52e3fdd
--- a/examples/fromADCIRCMap/adaptive_3D.py
+++ b/examples/fromADCIRCMap/adaptive_3D.py
@@ -1,4 +1,7 @@
 #! /usr/bin/env python
+
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # import necessary modules
 import polyadcirc.run_framework.domain as dom
 import polyadcirc.run_framework.random_wall_Q as rmw
diff --git a/examples/fromFileMap/fromFile2D.py b/examples/fromFileMap/fromFile2D.py
old mode 100755
new mode 100644
index 4cb46e57..7c78bf88
--- a/examples/fromFileMap/fromFile2D.py
+++ b/examples/fromFileMap/fromFile2D.py
@@ -1,4 +1,7 @@
 #! /usr/bin/env python
+
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # import necessary modules
 import numpy as np
 import bet.sampling.adaptiveSampling as asam
diff --git a/examples/fromFileMap/sandbox_test_2D.py b/examples/fromFileMap/sandbox_test_2D.py
old mode 100755
new mode 100644
index e1fc663e..b9fcb0b3
--- a/examples/fromFileMap/sandbox_test_2D.py
+++ b/examples/fromFileMap/sandbox_test_2D.py
@@ -1,4 +1,7 @@
 #! /usr/bin/env python
+
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # import necessary modules
 import numpy as np
 import polyadcirc.pyADCIRC.basic as basic
diff --git a/examples/fromFileMap/sandbox_test_3D.py b/examples/fromFileMap/sandbox_test_3D.py
old mode 100755
new mode 100644
index 93f67e80..db176206
--- a/examples/fromFileMap/sandbox_test_3D.py
+++ b/examples/fromFileMap/sandbox_test_3D.py
@@ -1,4 +1,7 @@
 #! /usr/bin/env python
+
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # -*- coding: utf-8 -*-
 # import necessary modules
 import numpy as np
diff --git a/examples/linearMap/linearMapUniformSampling.py b/examples/linearMap/linearMapUniformSampling.py
index 4798a9a1..d8b28930 100644
--- a/examples/linearMap/linearMapUniformSampling.py
+++ b/examples/linearMap/linearMapUniformSampling.py
@@ -1,4 +1,7 @@
 #! /usr/bin/env python
+
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
     This example generates uniform samples on a 3D grid
     and evaluates a linear map to a 2d space. Probabilities
diff --git a/setup.py b/setup.py
index 222021e6..340beff2 100644
--- a/setup.py
+++ b/setup.py
@@ -1,4 +1,7 @@
 #!/usr/bin/env python
+
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 '''
 The python script for building the BET package and subpackages.
 '''
diff --git a/test/__init__.py b/test/__init__.py
index 15bdfe52..3c8b769e 100644
--- a/test/__init__.py
+++ b/test/__init__.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 This package contains all of the tests for :program:`BET`. The package
 structure mirrors the ``bet`` package structure.
diff --git a/test/test_Comm.py b/test/test_Comm.py
index 3cb98a7e..f2c9f629 100644
--- a/test/test_Comm.py
+++ b/test/test_Comm.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # Lindley Graham 04/07/2015
 """
 This module contains unittests for :mod:`~bet.Comm`
diff --git a/test/test_calculateP/__init__.py b/test/test_calculateP/__init__.py
index 076ed6ae..9b590ba6 100644
--- a/test/test_calculateP/__init__.py
+++ b/test/test_calculateP/__init__.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 This package contains all of the tests for :program:`BET`. The package
 structure mirrors the ``bet`` package structure.
diff --git a/test/test_calculateP/test_calculateP.py b/test/test_calculateP/test_calculateP.py
index 51d678b3..a70d04b0 100644
--- a/test/test_calculateP/test_calculateP.py
+++ b/test/test_calculateP/test_calculateP.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # Steven Mattis and Lindley Graham 04/06/2015
 """
 This module contains tests for :module:`bet.calculateP.calculateP`.
diff --git a/test/test_calculateP/test_simpleFunP.py b/test/test_calculateP/test_simpleFunP.py
index d50faded..d20ef4f1 100644
--- a/test/test_calculateP/test_simpleFunP.py
+++ b/test/test_calculateP/test_simpleFunP.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # Lindley Graham 04/09/2015
 
 """
diff --git a/test/test_calculateP/test_voronoiHistogram.py b/test/test_calculateP/test_voronoiHistogram.py
index e5a1b85e..20795ee3 100644
--- a/test/test_calculateP/test_voronoiHistogram.py
+++ b/test/test_calculateP/test_voronoiHistogram.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # -*- coding: utf-8 -*-
 # Lindley Graham 04/12/2015
 """
diff --git a/test/test_postProcess/test_plotDomains.py b/test/test_postProcess/test_plotDomains.py
index 841e79e6..68a00d77 100644
--- a/test/test_postProcess/test_plotDomains.py
+++ b/test/test_postProcess/test_plotDomains.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # Lindley Graham 04/07/2015
 """
 This module contains tests for :module:`bet.postProcess.plotDomains`.
diff --git a/test/test_postProcess/test_plotP.py b/test/test_postProcess/test_plotP.py
index 3ee69efe..3d244831 100644
--- a/test/test_postProcess/test_plotP.py
+++ b/test/test_postProcess/test_plotP.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # Steven Mattis 04/07/2015
 """
 This module contains tests for :module:`bet.postProcess.plotP`.
diff --git a/test/test_postProcess/test_postTools.py b/test/test_postProcess/test_postTools.py
index 59ba6bce..0adc9a30 100644
--- a/test/test_postProcess/test_postTools.py
+++ b/test/test_postProcess/test_postTools.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # Steven Mattis 04/07/2015
 """
 This module contains tests for :module:`bet.postProcess.postTools`.
diff --git a/test/test_sampling/__init__.py b/test/test_sampling/__init__.py
index a45e59be..44d50e56 100644
--- a/test/test_sampling/__init__.py
+++ b/test/test_sampling/__init__.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 """
 This subpackage contains the test modules for the sampling subpackage.
 """
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index 9bd72918..06ca8b14 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # -*- coding: utf-8 -*-
 # Lindley Graham 04/07/2015
 
diff --git a/test/test_sampling/test_basicSampling.py b/test/test_sampling/test_basicSampling.py
index 1b9a7d42..53fedf7b 100644
--- a/test/test_sampling/test_basicSampling.py
+++ b/test/test_sampling/test_basicSampling.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # Lindley Graham 04/07/2015
 """
 This module contains unittests for :mod:`~bet.sampling.basicSampling:`
diff --git a/test/test_util.py b/test/test_util.py
index c298314b..37f26010 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -1,3 +1,5 @@
+# Copyright (C) 2014-2015 Lindley Graham and Steven Mattis
+
 # Lindley Graham 04/07/2015
 """
 This module contains unittests for :mod:`~bet.util`

From 2042b5dbf619c662033f3061020a40f18a76ec1c Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Fri, 15 May 2015 13:13:32 -0500
Subject: [PATCH 84/85] updated documentation to match examples

---
 doc/bet.loadBalance.rst                    | 30 ----------------------
 doc/examples/fromADCIRCMap/Q_1D_serial.rst |  9 +++----
 doc/examples/fromADCIRCMap/Q_2D.rst        |  6 ++---
 doc/examples/fromADCIRCMap/Q_3D.rst        |  7 ++---
 doc/index.rst                              |  1 +
 doc/overview.rst                           |  6 +----
 6 files changed, 13 insertions(+), 46 deletions(-)
 delete mode 100644 doc/bet.loadBalance.rst

diff --git a/doc/bet.loadBalance.rst b/doc/bet.loadBalance.rst
deleted file mode 100644
index 9d0888c6..00000000
--- a/doc/bet.loadBalance.rst
+++ /dev/null
@@ -1,30 +0,0 @@
-bet.loadBalance package
-=======================
-
-Submodules
-----------
-
-bet.loadBalance.lb_PADCIRC module
----------------------------------
-
-.. automodule:: bet.loadBalance.lb_PADCIRC
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-bet.loadBalance.load_balance module
------------------------------------
-
-.. automodule:: bet.loadBalance.load_balance
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: bet.loadBalance
-    :members:
-    :undoc-members:
-    :show-inheritance:
diff --git a/doc/examples/fromADCIRCMap/Q_1D_serial.rst b/doc/examples/fromADCIRCMap/Q_1D_serial.rst
index 79a212f1..1a3c4c25 100644
--- a/doc/examples/fromADCIRCMap/Q_1D_serial.rst
+++ b/doc/examples/fromADCIRCMap/Q_1D_serial.rst
@@ -91,7 +91,7 @@ Calculate :math:`\hat{\rho}_{\Lambda, j}` where :math:`\mathcal{V}_j` are the
 voronoi cells defined by :math:`\lambda_{emulate}`::
 
         (P0, lem0, io_ptr0, emulate_ptr0) = calcP.prob_emulated(samples, data,
-                rho_D_M, d_distr_samples, lam_domain, lambda_emulate, d_Tree)
+                rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
         mdict['P0'] = P0
         mdict['lem0'] = lem0
         mdict['io_ptr0'] = io_ptr0
@@ -101,13 +101,12 @@ Calculate :math:`\hat{\rho}_{\Lambda, j}` where :math:`\mathcal{V}_j` are the
 voronoi cells defined by :math:`\lambda_{samples}` assume that :math:`\lambda_{samples}`
 are uniformly distributed and therefore have approximately the same volume::
 
-        (P1, lam_vol1, lem1, io_ptr1, emulate_ptr1) = calcP.prob(samples, data,
-                rho_D_M, d_distr_samples, lam_domain, d_Tree)
+        (P1, lam_vol1, lem1, io_ptr1) = calcP.prob(samples, data,
+                rho_D_M, d_distr_samples, d_Tree)
         mdict['P1'] = P1
         mdict['lam_vol1'] = lam_vol1
         mdict['lem1'] = lem1
         mdict['io_ptr1'] = io_ptr1
-        mdict['emulate_ptr1'] = emulate_ptr1
 
 Calculate :math:`\hat{\rho}_{\Lambda, j}` where :math:`\mathcal{V}_j` are the
 voronoi cells defined by :math:`\lambda_{samples}` and we approximate the volume of
@@ -115,7 +114,7 @@ voronoi cells defined by :math:`\lambda_{samples}` and we approximate the volume
 :math:`\lambda_{emulate}` to estimate the volume of :math:`\mathcal{V}_j` ::
 
         (P3, lam_vol3, lambda_emulate3, io_ptr3, emulate_ptr3) = calcP.prob_mc(samples,
-                data, rho_D_M, d_distr_samples, lam_domain, lambda_emulate, d_Tree)
+                data, rho_D_M, d_distr_samples, lambda_emulate, d_Tree)
         mdict['P3'] = P3
         mdict['lam_vol3'] = lam_vol3
         mdict['io_ptr3'] = io_ptr3
diff --git a/doc/examples/fromADCIRCMap/Q_2D.rst b/doc/examples/fromADCIRCMap/Q_2D.rst
index fc219853..5f4ae1f5 100644
--- a/doc/examples/fromADCIRCMap/Q_2D.rst
+++ b/doc/examples/fromADCIRCMap/Q_2D.rst
@@ -15,8 +15,8 @@ This example demostrates three different methods to estimate
 
 These methods are distinguished primarily by the way :math:`\mathcal{V}_j` are
 defined and the approximation of the volume of :math:`\mathcal{V}_j`. See
-:download:`Q_2D.py
-<../../../examples/fromADCIRCMap/Q_2D.py>` for the example source code. Since
+:download:`Q_2D_serial.py
+<../../../examples/fromADCIRCMap/Q_2D_serial.py>` for the example source code. Since
 this example is essentially the same as :ref:`q1D` we will only highlight the
 differences between the two.
 
@@ -44,7 +44,7 @@ Define the refernce solution. We define a region of interest, :math:`R_{ref} \su
                 center_pts_per_edge=np.ones((data.shape[1],)))
 
 
-Finally, we calculate :math:`\hat{\rho}_{\Lambda, j}` for three reference solutions and the QoI :math:( (q_1,q_2), (q_1, q_5), and (q_1, q_6)) ::
+Finally, we calculate :math:`\hat{\rho}_{\Lambda, j}` for three reference solutions and the QoI :math:`( (q_1,q_2), (q_1, q_5)`, and :math:`(q_1, q_6))` ::
 
     ref_nums = [6, 11, 15] # 7, 12, 16
     stations = [1, 4, 5] # 2, 5, 6
diff --git a/doc/examples/fromADCIRCMap/Q_3D.rst b/doc/examples/fromADCIRCMap/Q_3D.rst
index 6d5c19df..2d15f65d 100644
--- a/doc/examples/fromADCIRCMap/Q_3D.rst
+++ b/doc/examples/fromADCIRCMap/Q_3D.rst
@@ -35,6 +35,8 @@ Also the ``postprocess(station_nums, ref_num)`` function in this case only uses
 Parallel Example
 ~~~~~~~~~~~~~~~~
 
+.. note:: The parallel version of this example has been moved to the development branch. 
+
 This example demostrates how to estimate :math:`\hat{\rho}_{\Lambda, j}` using
 :meth:`~bet.calculateP.calculateP.prob_mc` where 
 
@@ -71,11 +73,10 @@ Within the ``postprocess(station_nums, ref_num)`` function in this case we only
 need to use :meth:`~bet.postProcess.plotP.get_global_values` to concatenate the arrays
 spread out across the processors into a single array::
 
-    mdict['lambda_emulate'] = util.get_global_values(lambda_emulate)   
     mdict['num_l_emulate'] = mdict['lambda_emulate'].shape[1]
     mdict['P3'] = util.get_global_values(P3)
-    mdict['lam_vol3'] = util.get_global_values(lam_vol3)
-    mdict['io_ptr3'] = util.get_global_values(io_ptr3)
+    mdict['lam_vol3'] = lam_vol3
+    mdict['io_ptr3'] = io_ptr3
     
 Furthermore, we only want to write out the solution using a single processor::
 
diff --git a/doc/index.rst b/doc/index.rst
index 3b852567..eb29ac0a 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -18,6 +18,7 @@ Contents:
    examples/linearMap/*
    examples/fromFileMap/*
    examples/fromADCIRCMap/*
+   modules
    todo_list
 
 .. todo:: Flesh out documentation.
diff --git a/doc/overview.rst b/doc/overview.rst
index 26ea4e36..68c37e9f 100644
--- a/doc/overview.rst
+++ b/doc/overview.rst
@@ -43,7 +43,7 @@ Code Overview
 --------------
 
 :mod:`calculateP` Package
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. automodule:: bet.calculateP
 
@@ -57,10 +57,6 @@ Code Overview
 
 .. automodule:: bet.postProcess
 
-:mod:`loadBalance` Package
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. automodule:: bet.loadBalance
 
 .. seealso:: :ref:`modindex` for detailed documentation of modules, classes, etc.
 

From 7246a38f495caaae33c4fcffc713c392ac3692ee Mon Sep 17 00:00:00 2001
From: Lindley Graham <lgraham@ices.utexas.edu>
Date: Fri, 15 May 2015 13:15:31 -0500
Subject: [PATCH 85/85] removed a todo

---
 doc/index.rst | 2 --
 1 file changed, 2 deletions(-)

diff --git a/doc/index.rst b/doc/index.rst
index eb29ac0a..60d29f84 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -21,8 +21,6 @@ Contents:
    modules
    todo_list
 
-.. todo:: Flesh out documentation.
-