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UT-CHG · Jul 4, 2015 · c2d9ffc · c2d9ffc
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diff --git a/doc/examples/example_rst_files/Q_3D.rst b/doc/examples/example_rst_files/Q_3D.rst
@@ -44,7 +44,7 @@ This example demostrates how to estimate :math:`\hat{\rho}_{\Lambda, j}` using
 
     P_\Lambda \approx \sum_{\mathcal{V}_j \subset A} \hat{\rho}_{\Lambda, j}.
 
-See :download:`Q_3D_parallel.py <../../../examples/fromFile_ADCIRCMap/Q_3D_serial.py>`
+See `Q_3D_parallel.py <../../../examples/fromFile_ADCIRCMap/Q_3D_serial.py>`_
 for the example source code. Since example is essentially the same as
 :ref:`q2D` we will only highlight the differences between the two.
 

diff --git a/doc/examples/example_rst_files/fromfile2D.rst b/doc/examples/example_rst_files/fromfile2D.rst
@@ -116,18 +116,17 @@ In some instances the user may want to generate and compare several sets of
 adaptive samples using a surrogate model to determine what the best kernel,
 transition set, number of generalized chains, and chain length are before
 adaptively sampling a more computationally expensive model. See
-:download:`sandbox_test_2D.py <../../../examples/fromFile_ADCIRCMap/sandbox_test_2D.py>`. The set up in
-:download:`sandbox_test_2D.py <../../../examples/fromFile_ADCIRCMap/sandbox_test_2D.py>` is very similar to the
-set up in :download:`fromFile2D <../../../examples/fromFile_ADCIRCMap/fromFile2D.py>` and is
+`sandbox_test_2D.py <https://github.com/UT-CHG/BET/tree/master/examples/fromFile_ADCIRCMap/sandbox_test_2D.py>`_. The set up in
+sandbox_test_2D.py <https://github.com/UT-CHG/BET/tree/master/examples/fromFile_ADCIRCMap/sandbox_test_2D.py>`_ is very similar to the
+set up in `fromFile2D <https://github.com/UT-CHG/BET/tree/master/examples/fromFile_ADCIRCMap/fromFile2D.py>`_ and is
 omitted for brevity.
 
 We can explore several types of kernels::
 
     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]
+    kernel_rD = asam.rhoD_kernel(maximum, rho_D)
+    kern_list = [kernel_mm, kernel_rD, kernel_m]
     # Get samples
     # Run with varying kernels
     gen_results = sampler.run_gen(kern_list, rho_D, maximum, param_min,

diff --git a/doc/examples/example_rst_files/fromfile3D.rst b/doc/examples/example_rst_files/fromfile3D.rst
@@ -117,18 +117,17 @@ In some instances the user may want to generate and compare several sets of
 adaptive samples using a surrogate model to determine what the best kernel,
 transition set, number of generalized chains, and chain length are before
 adaptively sampling a more computationally expensive model. See
-:download:`sandbox_test_2D.py <../../../examples/fromFile_ADCIRCMap/sandbox_test_2D.py>`. The set up in
-:download:`sandbox_test_2D.py <../../../examples/fromFile_ADCIRCMap/sandbox_test_2D.py>` is very similar to the
-set up in :download:`fromFile2D <../../../examples/fromFile_ADCIRCMap/fromFile2D.py>` and is
+`sandbox_test_2D.py <https://github.com/UT-CHG/BET/tree/master/examples/fromFile_ADCIRCMap/sandbox_test_2D.py>`_. The set up in
+`sandbox_test_2D.py <https://github.com/UT-CHG/BET/tree/master/examples/fromFile_ADCIRCMap/sandbox_test_2D.py>`_ is very similar to the
+set up in `fromFile2D <https://github.com/UT-CHG/BET/tree/master/examples/fromFile_ADCIRCMap/fromFile2D.py>`_ and is
 omitted for brevity.
 
 We can explore several types of kernels::
 
     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]
     # Get samples
     # Run with varying kernels
     gen_results = sampler.run_gen(kern_list, rho_D, maximum, param_min,