From 2bbfb4107b9ef166026f9aaee40b71354397a8bd Mon Sep 17 00:00:00 2001
From: Stephen Nneji <steve.nneji@gmail.com>
Date: Wed, 4 Feb 2026 13:09:19 +0000
Subject: [PATCH 1/3] Unifies the contrast number used by python and cpp to be
 same with matlab

---
 cpp/RAT                                       |   2 +-
 .../absorption/volume_thiol_bilayer.py        |  21 +-
 .../bayes_benchmark/bayes_benchmark.ipynb     |  11 +-
 .../convert_rascal_project/Model_IIb.py       |   7 +-
 ratapi/examples/domains/alloy_domains.py      |   5 +-
 ratapi/examples/domains/domains_XY_model.py   |   7 +-
 ratapi/examples/languages/custom_bilayer.cpp  |  10 +-
 ratapi/examples/languages/custom_bilayer.py   |  11 +-
 .../DSPC_custom_layers.ipynb                  | 275 +++++++++---------
 .../normal_reflectivity/custom_XY_DSPC.py     |   5 +-
 .../custom_bilayer_DSPC.py                    |  10 +-
 ratapi/wrappers.py                            |   4 +-
 tests/test_wrappers.py                        |   4 +-
 13 files changed, 200 insertions(+), 172 deletions(-)

diff --git a/cpp/RAT b/cpp/RAT
index 7535f967..7da24c8d 160000
--- a/cpp/RAT
+++ b/cpp/RAT
@@ -1 +1 @@
-Subproject commit 7535f96759ee352919cae010f64af79652cbd094
+Subproject commit 7da24c8d9700bff1eb5c660f5389214c473a1c24
diff --git a/ratapi/examples/absorption/volume_thiol_bilayer.py b/ratapi/examples/absorption/volume_thiol_bilayer.py
index 444d5cac..cf00076b 100644
--- a/ratapi/examples/absorption/volume_thiol_bilayer.py
+++ b/ratapi/examples/absorption/volume_thiol_bilayer.py
@@ -23,6 +23,9 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):
 
     The second output parameter should be the substrate roughness.
     """
+    # Note - The first contrast number is 1 (not 0) so be careful if you use
+    # this variable for array indexing.
+
     subRough = params[0]
     alloyThick = params[1]
     alloySLDUp = params[2]
@@ -92,11 +95,11 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):
 
     # Correct head SLD based on hydration
     thiolHeadHydr = thiolHeadHydr / 100
-    sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast])
+    sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast - 1])
 
     # Now correct both the SLDs for the coverage parameter
-    sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast])
-    sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast])
+    sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast - 1])
+    sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast - 1])
 
     SAMTAILS = [thickTail, sldTail, 0, goldRough]
     SAMHEAD = [thickHead, sldHead, 0, goldRough]
@@ -113,7 +116,7 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):
     sldHead = sumbHead / vHead
     thickHead = vHead / bilayerAPM
     bilHeadHydr = bilHeadHydr / 100
-    sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast])
+    sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast - 1])
 
     sldTail = sumbTail / vTail
     thickTail = vTail / bilayerAPM
@@ -121,9 +124,9 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):
     sldMe = sumbMe / vMe
     thickMe = vMe / bilayerAPM
 
-    sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast])
-    sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast])
-    sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast])
+    sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast - 1])
+    sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast - 1])
+    sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast - 1])
 
     BILTAILS = [thickTail, sldTail, 0, bilayerRough]
     BILHEAD = [thickHead, sldHead, 0, bilayerRough]
@@ -131,9 +134,9 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):
 
     BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD]
 
-    CW = [cwThick, bulk_out[contrast], 0, bilayerRough]
+    CW = [cwThick, bulk_out[contrast - 1], 0, bilayerRough]
 
-    if contrast == 1 or contrast == 3:
+    if contrast == 2 or contrast == 4:
         output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]
     else:
         output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]
diff --git a/ratapi/examples/bayes_benchmark/bayes_benchmark.ipynb b/ratapi/examples/bayes_benchmark/bayes_benchmark.ipynb
index 0c083924..bb9bba9f 100644
--- a/ratapi/examples/bayes_benchmark/bayes_benchmark.ipynb
+++ b/ratapi/examples/bayes_benchmark/bayes_benchmark.ipynb
@@ -188,7 +188,7 @@
     "back_param = project.background_parameters[0]\n",
     "background = np.linspace(back_param.min, back_param.max, 30)\n",
     "\n",
-    "controls = RAT.Controls(procedure=\"calculate\", calcSldDuringFit=True, display=\"off\")\n",
+    "controls = RAT.Controls(procedure=\"calculate\", display=\"off\")\n",
     "\n",
     "# function to calculate exp(-chi_squared / 2) for a given pair of roughness/background values\n",
     "def calculate_posterior(roughness_index: int, background_index: int) -> float:\n",
@@ -339,13 +339,6 @@
     "fig.tight_layout()\n",
     "fig.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -364,7 +357,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/ratapi/examples/convert_rascal_project/Model_IIb.py b/ratapi/examples/convert_rascal_project/Model_IIb.py
index d7c61526..2e15d5d1 100644
--- a/ratapi/examples/convert_rascal_project/Model_IIb.py
+++ b/ratapi/examples/convert_rascal_project/Model_IIb.py
@@ -5,6 +5,9 @@
 
 def Model_IIb(params, bulk_in, bulk_out, contrast):
     """Calculate layer parameters for a monolayer volume model at two deuterations."""
+    # Note - The first contrast number is 1 (not 0) so be careful if you use
+    # this variable for array indexing. Same applies to the domain number.
+
     # converted from matlab file Model_IIb.m
 
     Roughness, APM, thickHead, theta = params
@@ -49,7 +52,7 @@ def Model_IIb(params, bulk_in, bulk_out, contrast):
     vTail = 2 * (16 * vCH2) + 2 * (vCH3)
 
     # make SLDs
-    thisMask = deut[contrast]
+    thisMask = deut[contrast - 1]
 
     if thisMask[0] == 0:
         thisWater = (H2O * 0.9249) + (D2O * 0.0871)
@@ -57,7 +60,7 @@ def Model_IIb(params, bulk_in, bulk_out, contrast):
         thisWater = D2O
 
     # Calculate mole fraction of D2O from the bulk SLD
-    d2o_molfr = (1 / D2O - H2O) * ((bulk_out[contrast] / 0.036182336306) - H2O)
+    d2o_molfr = (1 / D2O - H2O) * ((bulk_out[contrast - 1] / 0.036182336306) - H2O)
     thisWater = (d2o_molfr * D2O) + ((1 - d2o_molfr) * H2O)
 
     if thisMask[1] == 0:
diff --git a/ratapi/examples/domains/alloy_domains.py b/ratapi/examples/domains/alloy_domains.py
index 33454dcc..88a7fde5 100644
--- a/ratapi/examples/domains/alloy_domains.py
+++ b/ratapi/examples/domains/alloy_domains.py
@@ -7,6 +7,9 @@ def alloy_domains(params, bulkIn, bulkOut, contrast, domain):
     Simple custom model for testing incoherent summing.
     Simple two layer of permalloy / gold, with up/down domains.
     """
+    # Note - The first contrast number is 1 (not 0) so be careful if you use
+    # this variable for array indexing. Same applies to the domain number.
+
     # Split up the parameters
     subRough = params[0]
     alloyThick = params[1]
@@ -23,7 +26,7 @@ def alloy_domains(params, bulkIn, bulkOut, contrast, domain):
     gold = [goldThick, goldSLD, goldRough]
 
     # Make the model depending on which domain we are looking at
-    if domain == 0:
+    if domain == 1:
         output = [alloyUp, gold]
     else:
         output = [alloyDn, gold]
diff --git a/ratapi/examples/domains/domains_XY_model.py b/ratapi/examples/domains/domains_XY_model.py
index 00567666..a014e434 100644
--- a/ratapi/examples/domains/domains_XY_model.py
+++ b/ratapi/examples/domains/domains_XY_model.py
@@ -8,6 +8,9 @@
 
 def domains_XY_model(params, bulk_in, bulk_out, contrast, domain):
     """Calculate the SLD profile for a domains custom XY model."""
+    # Note - The first contrast number is 1 (not 0) so be careful if you use
+    # this variable for array indexing. Same applies to the domain number.
+
     # Split up the parameters for convenience
     subRough = params[0]
     oxideThick = params[1]
@@ -37,13 +40,13 @@ def domains_XY_model(params, bulk_in, bulk_out, contrast, domain):
     oxSLD = vfOxide * 3.41e-6
 
     # Layer SLD depends on whether we are calculating the domain or not
-    if domain == 0:
+    if domain == 1:
         laySLD = vfLayer * layerSLD
     else:
         laySLD = vfLayer * domainSLD
 
     # ... and finally the water SLD.
-    waterSLD = vfWater * bulk_out[contrast]
+    waterSLD = vfWater * bulk_out[contrast - 1]
 
     # Make the total SLD by just adding them all up
     totalSLD = siSLD + oxSLD + laySLD + waterSLD
diff --git a/ratapi/examples/languages/custom_bilayer.cpp b/ratapi/examples/languages/custom_bilayer.cpp
index ad25b18f..2cf0ca81 100644
--- a/ratapi/examples/languages/custom_bilayer.cpp
+++ b/ratapi/examples/languages/custom_bilayer.cpp
@@ -13,6 +13,8 @@ extern "C" {
 
     LIB_EXPORT void custom_bilayer(std::vector<double>& params, std::vector<double>& bulkIn, std::vector<double>& bulkOut, int contrast, std::vector<double>& output, double* outputSize, double* rough)
     {
+        // Note - The first contrast number is 1 (not 0) so be careful if you use 
+        // this variable for array indexing. 
         double subRough = params[0];
         double oxideThick = params[1];
         double oxideHydration = params[2];
@@ -65,9 +67,9 @@ extern "C" {
 
         // Manually deal with hydration for layers in
         // this example.
-        double oxSLD = (oxideHydration * bulkOut[contrast]) + ((1 - oxideHydration) * oxideSLD);
-        double headSLD = (headHydration * bulkOut[contrast]) + ((1 - headHydration) * SLDhead);
-        double tailSLD = (bilayerHydration * bulkOut[contrast]) + ((1 - bilayerHydration) * SLDtail);
+        double oxSLD = (oxideHydration * bulkOut[contrast-1]) + ((1 - oxideHydration) * oxideSLD);
+        double headSLD = (headHydration * bulkOut[contrast-1]) + ((1 - headHydration) * SLDhead);
+        double tailSLD = (bilayerHydration * bulkOut[contrast-1]) + ((1 - bilayerHydration) * SLDtail);
 
         // Make the layers
         // oxide...
@@ -77,7 +79,7 @@ extern "C" {
 
         // Water...
         output.push_back(waterThick);
-        output.push_back(bulkOut[contrast]);
+        output.push_back(bulkOut[contrast-1]);
         output.push_back(bilayerRough);
 
         // Heads...
diff --git a/ratapi/examples/languages/custom_bilayer.py b/ratapi/examples/languages/custom_bilayer.py
index 1777b358..bed82ca3 100644
--- a/ratapi/examples/languages/custom_bilayer.py
+++ b/ratapi/examples/languages/custom_bilayer.py
@@ -5,6 +5,9 @@
 
 def custom_bilayer(params, bulk_in, bulk_out, contrast):
     """Calculate the layer parameters for a custom bilayer model."""
+    # Note - The first contrast number is 1 (not 0) so be careful if you use
+    # this variable for array indexing.
+
     sub_rough = params[0]
     oxide_thick = params[1]
     oxide_hydration = params[2]
@@ -54,13 +57,13 @@ def custom_bilayer(params, bulk_in, bulk_out, contrast):
     tailThick = vTail / lipidAPM
 
     # Manually deal with hydration for layers in this example.
-    oxSLD = (oxide_hydration * bulk_out[contrast]) + ((1 - oxide_hydration) * oxide_SLD)
-    headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead)
-    tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail)
+    oxSLD = (oxide_hydration * bulk_out[contrast - 1]) + ((1 - oxide_hydration) * oxide_SLD)
+    headSLD = (headHydration * bulk_out[contrast - 1]) + ((1 - headHydration) * SLDhead)
+    tailSLD = (bilayerHydration * bulk_out[contrast - 1]) + ((1 - bilayerHydration) * SLDtail)
 
     # Make the layers
     oxide = [oxide_thick, oxSLD, sub_rough]
-    water = [waterThick, bulk_out[contrast], bilayerRough]
+    water = [waterThick, bulk_out[contrast - 1], bilayerRough]
     head = [headThick, headSLD, bilayerRough]
     tail = [tailThick, tailSLD, bilayerRough]
 
diff --git a/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb b/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb
index d2fd6ed3..3d807d1d 100644
--- a/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb
+++ b/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 25,
    "id": "4b988c4a-3a09-4b75-8a87-8ba8402635ba",
    "metadata": {},
    "outputs": [],
@@ -29,12 +29,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 26,
    "id": "9a60cd45-0e1d-448a-b4bd-4c02bd6a3475",
    "metadata": {},
    "outputs": [],
    "source": [
-    "problem = RAT.Project(name=\"Orso lipid example - custom layers\", model=\"custom layers\", geometry=\"substrate/liquid\")"
+    "problem = RAT.Project(name=\"Orso lipid example - custom layers\", model=\"custom layers\", geometry=\"substrate/liquid\")\n",
+    "problem.show_priors()"
    ]
   },
   {
@@ -61,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 27,
    "id": "9038b77f-e3fc-4946-87fe-af4addf8ee84",
    "metadata": {},
    "outputs": [
@@ -76,9 +77,9 @@
        ".output_html .hll { background-color: #ffffcc }\n",
        ".output_html { background: #f8f8f8; }\n",
        ".output_html .c { color: #3D7B7B; font-style: italic } /* Comment */\n",
-       ".output_html .err { border: 1px solid #FF0000 } /* Error */\n",
+       ".output_html .err { border: 1px solid #F00 } /* Error */\n",
        ".output_html .k { color: #008000; font-weight: bold } /* Keyword */\n",
-       ".output_html .o { color: #666666 } /* Operator */\n",
+       ".output_html .o { color: #666 } /* Operator */\n",
        ".output_html .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */\n",
        ".output_html .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */\n",
        ".output_html .cp { color: #9C6500 } /* Comment.Preproc */\n",
@@ -95,34 +96,34 @@
        ".output_html .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
        ".output_html .gs { font-weight: bold } /* Generic.Strong */\n",
        ".output_html .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
-       ".output_html .gt { color: #0044DD } /* Generic.Traceback */\n",
+       ".output_html .gt { color: #04D } /* Generic.Traceback */\n",
        ".output_html .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
        ".output_html .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
        ".output_html .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
        ".output_html .kp { color: #008000 } /* Keyword.Pseudo */\n",
        ".output_html .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
        ".output_html .kt { color: #B00040 } /* Keyword.Type */\n",
-       ".output_html .m { color: #666666 } /* Literal.Number */\n",
+       ".output_html .m { color: #666 } /* Literal.Number */\n",
        ".output_html .s { color: #BA2121 } /* Literal.String */\n",
        ".output_html .na { color: #687822 } /* Name.Attribute */\n",
        ".output_html .nb { color: #008000 } /* Name.Builtin */\n",
-       ".output_html .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
-       ".output_html .no { color: #880000 } /* Name.Constant */\n",
-       ".output_html .nd { color: #AA22FF } /* Name.Decorator */\n",
+       ".output_html .nc { color: #00F; font-weight: bold } /* Name.Class */\n",
+       ".output_html .no { color: #800 } /* Name.Constant */\n",
+       ".output_html .nd { color: #A2F } /* Name.Decorator */\n",
        ".output_html .ni { color: #717171; font-weight: bold } /* Name.Entity */\n",
        ".output_html .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */\n",
-       ".output_html .nf { color: #0000FF } /* Name.Function */\n",
+       ".output_html .nf { color: #00F } /* Name.Function */\n",
        ".output_html .nl { color: #767600 } /* Name.Label */\n",
-       ".output_html .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
+       ".output_html .nn { color: #00F; font-weight: bold } /* Name.Namespace */\n",
        ".output_html .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
        ".output_html .nv { color: #19177C } /* Name.Variable */\n",
-       ".output_html .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
-       ".output_html .w { color: #bbbbbb } /* Text.Whitespace */\n",
-       ".output_html .mb { color: #666666 } /* Literal.Number.Bin */\n",
-       ".output_html .mf { color: #666666 } /* Literal.Number.Float */\n",
-       ".output_html .mh { color: #666666 } /* Literal.Number.Hex */\n",
-       ".output_html .mi { color: #666666 } /* Literal.Number.Integer */\n",
-       ".output_html .mo { color: #666666 } /* Literal.Number.Oct */\n",
+       ".output_html .ow { color: #A2F; font-weight: bold } /* Operator.Word */\n",
+       ".output_html .w { color: #BBB } /* Text.Whitespace */\n",
+       ".output_html .mb { color: #666 } /* Literal.Number.Bin */\n",
+       ".output_html .mf { color: #666 } /* Literal.Number.Float */\n",
+       ".output_html .mh { color: #666 } /* Literal.Number.Hex */\n",
+       ".output_html .mi { color: #666 } /* Literal.Number.Integer */\n",
+       ".output_html .mo { color: #666 } /* Literal.Number.Oct */\n",
        ".output_html .sa { color: #BA2121 } /* Literal.String.Affix */\n",
        ".output_html .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
        ".output_html .sc { color: #BA2121 } /* Literal.String.Char */\n",
@@ -137,16 +138,18 @@
        ".output_html .s1 { color: #BA2121 } /* Literal.String.Single */\n",
        ".output_html .ss { color: #19177C } /* Literal.String.Symbol */\n",
        ".output_html .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
-       ".output_html .fm { color: #0000FF } /* Name.Function.Magic */\n",
+       ".output_html .fm { color: #00F } /* Name.Function.Magic */\n",
        ".output_html .vc { color: #19177C } /* Name.Variable.Class */\n",
        ".output_html .vg { color: #19177C } /* Name.Variable.Global */\n",
        ".output_html .vi { color: #19177C } /* Name.Variable.Instance */\n",
        ".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
-       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"kn\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span>\n",
+       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"sd\">&quot;&quot;&quot;A custom layer model for a DSPC supported bilayer.&quot;&quot;&quot;</span>\n",
        "\n",
+       "<span class=\"kn\">import</span><span class=\"w\"> </span><span class=\"nn\">numpy</span><span class=\"w\"> </span><span class=\"k\">as</span><span class=\"w\"> </span><span class=\"nn\">np</span>\n",
        "\n",
-       "<span class=\"k\">def</span> <span class=\"nf\">custom_bilayer_DSPC</span><span class=\"p\">(</span><span class=\"n\">params</span><span class=\"p\">,</span> <span class=\"n\">bulk_in</span><span class=\"p\">,</span> <span class=\"n\">bulk_out</span><span class=\"p\">,</span> <span class=\"n\">contrast</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;CUSTOMBILAYER RAT Custom Layer Model File.</span>\n",
+       "\n",
+       "<span class=\"k\">def</span><span class=\"w\"> </span><span class=\"nf\">custom_bilayer_DSPC</span><span class=\"p\">(</span><span class=\"n\">params</span><span class=\"p\">,</span> <span class=\"n\">bulk_in</span><span class=\"p\">,</span> <span class=\"n\">bulk_out</span><span class=\"p\">,</span> <span class=\"n\">contrast</span><span class=\"p\">):</span>\n",
+       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;Calculate layer parameters for a DSPC supported bilayer.</span>\n",
        "\n",
        "<span class=\"sd\">    This file accepts 3 vectors containing the values for params, bulk in and bulk out.</span>\n",
        "<span class=\"sd\">    The final parameter is an index of the contrast being calculated.</span>\n",
@@ -167,6 +170,8 @@
        "\n",
        "<span class=\"sd\">    The second output parameter should be the substrate roughness.</span>\n",
        "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
+       "    <span class=\"c1\"># Note - The first contrast number is 1 (not 0) so be careful if you use</span>\n",
+       "    <span class=\"c1\"># this variable for array indexing.</span>\n",
        "    <span class=\"n\">sub_rough</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n",
        "    <span class=\"n\">oxide_thick</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n",
        "    <span class=\"n\">oxide_hydration</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">]</span>\n",
@@ -214,13 +219,13 @@
        "    <span class=\"n\">tailThick</span> <span class=\"o\">=</span> <span class=\"n\">vTail</span> <span class=\"o\">/</span> <span class=\"n\">lipidAPM</span>\n",
        "\n",
        "    <span class=\"c1\"># Manually deal with hydration for layers in this example.</span>\n",
-       "    <span class=\"n\">oxSLD</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">oxide_hydration</span> <span class=\"o\">*</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">oxide_hydration</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">oxide_SLD</span><span class=\"p\">)</span>\n",
-       "    <span class=\"n\">headSLD</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">headHydration</span> <span class=\"o\">*</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">headHydration</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">SLDhead</span><span class=\"p\">)</span>\n",
-       "    <span class=\"n\">tailSLD</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">bilayerHydration</span> <span class=\"o\">*</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">bilayerHydration</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">SLDtail</span><span class=\"p\">)</span>\n",
+       "    <span class=\"n\">oxSLD</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">oxide_hydration</span> <span class=\"o\">*</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">oxide_hydration</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">oxide_SLD</span><span class=\"p\">)</span>\n",
+       "    <span class=\"n\">headSLD</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">headHydration</span> <span class=\"o\">*</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">headHydration</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">SLDhead</span><span class=\"p\">)</span>\n",
+       "    <span class=\"n\">tailSLD</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">bilayerHydration</span> <span class=\"o\">*</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">bilayerHydration</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">SLDtail</span><span class=\"p\">)</span>\n",
        "\n",
        "    <span class=\"c1\"># Make the layers</span>\n",
        "    <span class=\"n\">oxide</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">oxide_thick</span><span class=\"p\">,</span> <span class=\"n\">oxSLD</span><span class=\"p\">,</span> <span class=\"n\">sub_rough</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">water</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">waterThick</span><span class=\"p\">,</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span><span class=\"p\">],</span> <span class=\"n\">bilayerRough</span><span class=\"p\">]</span>\n",
+       "    <span class=\"n\">water</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">waterThick</span><span class=\"p\">,</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"n\">bilayerRough</span><span class=\"p\">]</span>\n",
        "    <span class=\"n\">head</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">headThick</span><span class=\"p\">,</span> <span class=\"n\">headSLD</span><span class=\"p\">,</span> <span class=\"n\">bilayerRough</span><span class=\"p\">]</span>\n",
        "    <span class=\"n\">tail</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">tailThick</span><span class=\"p\">,</span> <span class=\"n\">tailSLD</span><span class=\"p\">,</span> <span class=\"n\">bilayerRough</span><span class=\"p\">]</span>\n",
        "\n",
@@ -231,11 +236,13 @@
       ],
       "text/latex": [
        "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
-       "\\PY{k+kn}{import} \\PY{n+nn}{numpy} \\PY{k}{as} \\PY{n+nn}{np}\n",
+       "\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}A custom layer model for a DSPC supported bilayer.\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
+       "\n",
+       "\\PY{k+kn}{import}\\PY{+w}{ }\\PY{n+nn}{numpy}\\PY{+w}{ }\\PY{k}{as}\\PY{+w}{ }\\PY{n+nn}{np}\n",
        "\n",
        "\n",
-       "\\PY{k}{def} \\PY{n+nf}{custom\\PYZus{}bilayer\\PYZus{}DSPC}\\PY{p}{(}\\PY{n}{params}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}in}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{,} \\PY{n}{contrast}\\PY{p}{)}\\PY{p}{:}\n",
-       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}CUSTOMBILAYER RAT Custom Layer Model File.}\n",
+       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{custom\\PYZus{}bilayer\\PYZus{}DSPC}\\PY{p}{(}\\PY{n}{params}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}in}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{,} \\PY{n}{contrast}\\PY{p}{)}\\PY{p}{:}\n",
+       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Calculate layer parameters for a DSPC supported bilayer.}\n",
        "\n",
        "\\PY{l+s+sd}{    This file accepts 3 vectors containing the values for params, bulk in and bulk out.}\n",
        "\\PY{l+s+sd}{    The final parameter is an index of the contrast being calculated.}\n",
@@ -256,6 +263,8 @@
        "\n",
        "\\PY{l+s+sd}{    The second output parameter should be the substrate roughness.}\n",
        "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
+       "    \\PY{c+c1}{\\PYZsh{} Note \\PYZhy{} The first contrast number is 1 (not 0) so be careful if you use}\n",
+       "    \\PY{c+c1}{\\PYZsh{} this variable for array indexing.}\n",
        "    \\PY{n}{sub\\PYZus{}rough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\n",
        "    \\PY{n}{oxide\\PYZus{}thick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\n",
        "    \\PY{n}{oxide\\PYZus{}hydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\n",
@@ -303,13 +312,13 @@
        "    \\PY{n}{tailThick} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{lipidAPM}\n",
        "\n",
        "    \\PY{c+c1}{\\PYZsh{} Manually deal with hydration for layers in this example.}\n",
-       "    \\PY{n}{oxSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{oxide\\PYZus{}hydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{oxide\\PYZus{}hydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{oxide\\PYZus{}SLD}\\PY{p}{)}\n",
-       "    \\PY{n}{headSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{headHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{headHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDhead}\\PY{p}{)}\n",
-       "    \\PY{n}{tailSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDtail}\\PY{p}{)}\n",
+       "    \\PY{n}{oxSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{oxide\\PYZus{}hydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{oxide\\PYZus{}hydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{oxide\\PYZus{}SLD}\\PY{p}{)}\n",
+       "    \\PY{n}{headSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{headHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{headHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDhead}\\PY{p}{)}\n",
+       "    \\PY{n}{tailSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDtail}\\PY{p}{)}\n",
        "\n",
        "    \\PY{c+c1}{\\PYZsh{} Make the layers}\n",
        "    \\PY{n}{oxide} \\PY{o}{=} \\PY{p}{[}\\PY{n}{oxide\\PYZus{}thick}\\PY{p}{,} \\PY{n}{oxSLD}\\PY{p}{,} \\PY{n}{sub\\PYZus{}rough}\\PY{p}{]}\n",
-       "    \\PY{n}{water} \\PY{o}{=} \\PY{p}{[}\\PY{n}{waterThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n",
+       "    \\PY{n}{water} \\PY{o}{=} \\PY{p}{[}\\PY{n}{waterThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n",
        "    \\PY{n}{head} \\PY{o}{=} \\PY{p}{[}\\PY{n}{headThick}\\PY{p}{,} \\PY{n}{headSLD}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n",
        "    \\PY{n}{tail} \\PY{o}{=} \\PY{p}{[}\\PY{n}{tailThick}\\PY{p}{,} \\PY{n}{tailSLD}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n",
        "\n",
@@ -319,11 +328,13 @@
        "\\end{Verbatim}\n"
       ],
       "text/plain": [
+       "\"\"\"A custom layer model for a DSPC supported bilayer.\"\"\"\n",
+       "\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast):\n",
-       "    \"\"\"CUSTOMBILAYER RAT Custom Layer Model File.\n",
+       "    \"\"\"Calculate layer parameters for a DSPC supported bilayer.\n",
        "\n",
        "    This file accepts 3 vectors containing the values for params, bulk in and bulk out.\n",
        "    The final parameter is an index of the contrast being calculated.\n",
@@ -344,6 +355,8 @@
        "\n",
        "    The second output parameter should be the substrate roughness.\n",
        "    \"\"\"\n",
+       "    # Note - The first contrast number is 1 (not 0) so be careful if you use\n",
+       "    # this variable for array indexing.\n",
        "    sub_rough = params[0]\n",
        "    oxide_thick = params[1]\n",
        "    oxide_hydration = params[2]\n",
@@ -391,13 +404,13 @@
        "    tailThick = vTail / lipidAPM\n",
        "\n",
        "    # Manually deal with hydration for layers in this example.\n",
-       "    oxSLD = (oxide_hydration * bulk_out[contrast]) + ((1 - oxide_hydration) * oxide_SLD)\n",
-       "    headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead)\n",
-       "    tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail)\n",
+       "    oxSLD = (oxide_hydration * bulk_out[contrast - 1]) + ((1 - oxide_hydration) * oxide_SLD)\n",
+       "    headSLD = (headHydration * bulk_out[contrast - 1]) + ((1 - headHydration) * SLDhead)\n",
+       "    tailSLD = (bilayerHydration * bulk_out[contrast - 1]) + ((1 - bilayerHydration) * SLDtail)\n",
        "\n",
        "    # Make the layers\n",
        "    oxide = [oxide_thick, oxSLD, sub_rough]\n",
-       "    water = [waterThick, bulk_out[contrast], bilayerRough]\n",
+       "    water = [waterThick, bulk_out[contrast - 1], bilayerRough]\n",
        "    head = [headThick, headSLD, bilayerRough]\n",
        "    tail = [tailThick, tailSLD, bilayerRough]\n",
        "\n",
@@ -406,7 +419,7 @@
        "    return output, sub_rough"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -425,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 28,
    "id": "70494ef9-6cc5-47dc-9d02-6506645de46b",
    "metadata": {},
    "outputs": [],
@@ -454,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 29,
    "id": "453fe3d2-162a-42bb-91ee-b1d020ffd29e",
    "metadata": {},
    "outputs": [],
@@ -478,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 30,
    "id": "fa4c1b96-3a1b-4aa6-8d61-68f24b0cb482",
    "metadata": {},
    "outputs": [],
@@ -505,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 31,
    "id": "2e649c26-b32b-4c79-8ae7-fa701c87e6c2",
    "metadata": {},
    "outputs": [],
@@ -523,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 32,
    "id": "5d51954f-469a-4044-9a7d-1b6e30474a6b",
    "metadata": {},
    "outputs": [],
@@ -554,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 33,
    "id": "b1e4d313-8450-459b-b60e-868fe82f06b0",
    "metadata": {},
    "outputs": [],
@@ -572,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 34,
    "id": "efc7b351-2112-40c4-862b-a47e4570d173",
    "metadata": {},
    "outputs": [],
@@ -623,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 35,
    "id": "ee889e55-8357-4363-860d-fb1c13bb8e8b",
    "metadata": {},
    "outputs": [
@@ -637,7 +650,7 @@
       "\n",
       "Calculation: ---------------------------------------------------------------------------------------\n",
       "\n",
-      "non polarised\n",
+      "normal\n",
       "\n",
       "Model: ---------------------------------------------------------------------------------------------\n",
       "\n",
@@ -649,89 +662,89 @@
       "\n",
       "Parameters: ----------------------------------------------------------------------------------------\n",
       "\n",
-      "+-------+---------------------+------+-------+------+------+------------+-----+-------+\n",
-      "| index |         name        | min  | value | max  | fit  | prior type |  mu | sigma |\n",
-      "+-------+---------------------+------+-------+------+------+------------+-----+-------+\n",
-      "|   0   | Substrate Roughness | 1.0  |  3.0  | 10.0 | True |  uniform   | 0.0 |  inf  |\n",
-      "|   1   |   Oxide Thickness   | 5.0  |  20.0 | 60.0 | True |  uniform   | 0.0 |  inf  |\n",
-      "|   2   |   Oxide Hydration   | 0.0  |  0.2  | 0.5  | True |  uniform   | 0.0 |  inf  |\n",
-      "|   3   |      Lipid APM      | 45.0 |  55.0 | 65.0 | True |  uniform   | 0.0 |  inf  |\n",
-      "|   4   |    Head Hydration   | 0.0  |  0.2  | 0.5  | True |  gaussian  | 0.3 |  0.03 |\n",
-      "|   5   |  Bilayer Hydration  | 0.0  |  0.1  | 0.2  | True |  uniform   | 0.0 |  inf  |\n",
-      "|   6   |  Bilayer Roughness  | 2.0  |  4.0  | 8.0  | True |  uniform   | 0.0 |  inf  |\n",
-      "|   7   |   Water Thickness   | 0.0  |  2.0  | 10.0 | True |  uniform   | 0.0 |  inf  |\n",
-      "+-------+---------------------+------+-------+------+------+------------+-----+-------+\n",
+      "+-------+---------------------+------+-------+------+------+------------+\n",
+      "| index |         name        | min  | value | max  | fit  | prior type |\n",
+      "+-------+---------------------+------+-------+------+------+------------+\n",
+      "|   0   | Substrate Roughness | 1.0  |  3.0  | 10.0 | True |  uniform   |\n",
+      "|   1   |   Oxide Thickness   | 5.0  |  20.0 | 60.0 | True |  uniform   |\n",
+      "|   2   |   Oxide Hydration   | 0.0  |  0.2  | 0.5  | True |  uniform   |\n",
+      "|   3   |      Lipid APM      | 45.0 |  55.0 | 65.0 | True |  uniform   |\n",
+      "|   4   |    Head Hydration   | 0.0  |  0.2  | 0.5  | True |  gaussian  |\n",
+      "|   5   |  Bilayer Hydration  | 0.0  |  0.1  | 0.2  | True |  uniform   |\n",
+      "|   6   |  Bilayer Roughness  | 2.0  |  4.0  | 8.0  | True |  uniform   |\n",
+      "|   7   |   Water Thickness   | 0.0  |  2.0  | 10.0 | True |  uniform   |\n",
+      "+-------+---------------------+------+-------+------+------+------------+\n",
       "\n",
       "Bulk In: -------------------------------------------------------------------------------------------\n",
       "\n",
-      "+-------+---------+----------+-----------+----------+-------+------------+-----+-------+\n",
-      "| index |   name  |   min    |   value   |   max    |  fit  | prior type |  mu | sigma |\n",
-      "+-------+---------+----------+-----------+----------+-------+------------+-----+-------+\n",
-      "|   0   | Silicon | 2.07e-06 | 2.073e-06 | 2.08e-06 | False |  uniform   | 0.0 |  inf  |\n",
-      "+-------+---------+----------+-----------+----------+-------+------------+-----+-------+\n",
+      "+-------+---------+----------+-----------+----------+-------+------------+\n",
+      "| index |   name  |   min    |   value   |   max    |  fit  | prior type |\n",
+      "+-------+---------+----------+-----------+----------+-------+------------+\n",
+      "|   0   | Silicon | 2.07e-06 | 2.073e-06 | 2.08e-06 | False |  uniform   |\n",
+      "+-------+---------+----------+-----------+----------+-------+------------+\n",
       "\n",
       "Bulk Out: ------------------------------------------------------------------------------------------\n",
       "\n",
-      "+-------+---------+--------+-----------+----------+------+------------+-----+-------+\n",
-      "| index |   name  |  min   |   value   |   max    | fit  | prior type |  mu | sigma |\n",
-      "+-------+---------+--------+-----------+----------+------+------------+-----+-------+\n",
-      "|   0   | SLD D2O | 5e-06  |  6.35e-06 | 6.35e-06 | True |  uniform   | 0.0 |  inf  |\n",
-      "|   1   | SLD SMW | 1e-06  | 2.073e-06 |  3e-06   | True |  uniform   | 0.0 |  inf  |\n",
-      "|   2   | SLD H2O | -6e-07 |  -5.6e-07 |  -3e-07  | True |  uniform   | 0.0 |  inf  |\n",
-      "+-------+---------+--------+-----------+----------+------+------------+-----+-------+\n",
+      "+-------+---------+--------+-----------+----------+------+------------+\n",
+      "| index |   name  |  min   |   value   |   max    | fit  | prior type |\n",
+      "+-------+---------+--------+-----------+----------+------+------------+\n",
+      "|   0   | SLD D2O | 5e-06  |  6.35e-06 | 6.35e-06 | True |  uniform   |\n",
+      "|   1   | SLD SMW | 1e-06  | 2.073e-06 |  3e-06   | True |  uniform   |\n",
+      "|   2   | SLD H2O | -6e-07 |  -5.6e-07 |  -3e-07  | True |  uniform   |\n",
+      "+-------+---------+--------+-----------+----------+------+------------+\n",
       "\n",
       "Scalefactors: --------------------------------------------------------------------------------------\n",
       "\n",
-      "+-------+---------------+-----+-------+-----+------+------------+-----+-------+\n",
-      "| index |      name     | min | value | max | fit  | prior type |  mu | sigma |\n",
-      "+-------+---------------+-----+-------+-----+------+------------+-----+-------+\n",
-      "|   0   | Scalefactor 1 | 0.5 |  1.0  | 2.0 | True |  uniform   | 0.0 |  inf  |\n",
-      "+-------+---------------+-----+-------+-----+------+------------+-----+-------+\n",
+      "+-------+---------------+-----+-------+-----+------+------------+\n",
+      "| index |      name     | min | value | max | fit  | prior type |\n",
+      "+-------+---------------+-----+-------+-----+------+------------+\n",
+      "|   0   | Scalefactor 1 | 0.5 |  1.0  | 2.0 | True |  uniform   |\n",
+      "+-------+---------------+-----+-------+-----+------+------------+\n",
       "\n",
       "Background Parameters: -----------------------------------------------------------------------------\n",
       "\n",
-      "+-------+--------------------------+-------+-------+-------+------+------------+-----+-------+\n",
-      "| index |           name           |  min  | value |  max  | fit  | prior type |  mu | sigma |\n",
-      "+-------+--------------------------+-------+-------+-------+------+------------+-----+-------+\n",
-      "|   0   | Background parameter D2O | 1e-10 | 1e-07 | 1e-05 | True |  uniform   | 0.0 |  inf  |\n",
-      "|   1   | Background parameter SMW | 1e-10 | 1e-07 | 1e-05 | True |  uniform   | 0.0 |  inf  |\n",
-      "|   2   | Background parameter H2O | 1e-10 | 1e-07 | 1e-05 | True |  uniform   | 0.0 |  inf  |\n",
-      "+-------+--------------------------+-------+-------+-------+------+------------+-----+-------+\n",
+      "+-------+--------------------------+-------+-------+-------+------+------------+\n",
+      "| index |           name           |  min  | value |  max  | fit  | prior type |\n",
+      "+-------+--------------------------+-------+-------+-------+------+------------+\n",
+      "|   0   | Background parameter D2O | 1e-10 | 1e-07 | 1e-05 | True |  uniform   |\n",
+      "|   1   | Background parameter SMW | 1e-10 | 1e-07 | 1e-05 | True |  uniform   |\n",
+      "|   2   | Background parameter H2O | 1e-10 | 1e-07 | 1e-05 | True |  uniform   |\n",
+      "+-------+--------------------------+-------+-------+-------+------+------------+\n",
       "\n",
       "Backgrounds: ---------------------------------------------------------------------------------------\n",
       "\n",
-      "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n",
-      "| index |      name      |   type   |         value 1          | value 2 | value 3 | value 4 | value 5 |\n",
-      "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n",
-      "|   0   | Background D2O | constant | Background parameter D2O |         |         |         |         |\n",
-      "|   1   | Background SMW | constant | Background parameter SMW |         |         |         |         |\n",
-      "|   2   | Background H2O | constant | Background parameter H2O |         |         |         |         |\n",
-      "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n",
+      "+-------+----------------+----------+--------------------------+\n",
+      "| index |      name      |   type   |          source          |\n",
+      "+-------+----------------+----------+--------------------------+\n",
+      "|   0   | Background D2O | constant | Background parameter D2O |\n",
+      "|   1   | Background SMW | constant | Background parameter SMW |\n",
+      "|   2   | Background H2O | constant | Background parameter H2O |\n",
+      "+-------+----------------+----------+--------------------------+\n",
       "\n",
       "Resolution Parameters: -----------------------------------------------------------------------------\n",
       "\n",
-      "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n",
-      "| index |        name        | min  | value | max  |  fit  | prior type |  mu | sigma |\n",
-      "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n",
-      "|   0   | Resolution Param 1 | 0.01 |  0.03 | 0.05 | False |  uniform   | 0.0 |  inf  |\n",
-      "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n",
+      "+-------+--------------------+------+-------+------+-------+------------+\n",
+      "| index |        name        | min  | value | max  |  fit  | prior type |\n",
+      "+-------+--------------------+------+-------+------+-------+------------+\n",
+      "|   0   | Resolution Param 1 | 0.01 |  0.03 | 0.05 | False |  uniform   |\n",
+      "+-------+--------------------+------+-------+------+-------+------------+\n",
       "\n",
       "Resolutions: ---------------------------------------------------------------------------------------\n",
       "\n",
-      "+-------+-----------------+----------+--------------------+---------+---------+---------+---------+\n",
-      "| index |       name      |   type   |      value 1       | value 2 | value 3 | value 4 | value 5 |\n",
-      "+-------+-----------------+----------+--------------------+---------+---------+---------+---------+\n",
-      "|   0   |   Resolution 1  | constant | Resolution Param 1 |         |         |         |         |\n",
-      "|   1   | Data Resolution |   data   |                    |         |         |         |         |\n",
-      "+-------+-----------------+----------+--------------------+---------+---------+---------+---------+\n",
+      "+-------+-----------------+----------+--------------------+---------+\n",
+      "| index |       name      |   type   |       source       | value 1 |\n",
+      "+-------+-----------------+----------+--------------------+---------+\n",
+      "|   0   |   Resolution 1  | constant | Resolution Param 1 |         |\n",
+      "|   1   | Data Resolution |   data   |                    |         |\n",
+      "+-------+-----------------+----------+--------------------+---------+\n",
       "\n",
       "Custom Files: --------------------------------------------------------------------------------------\n",
       "\n",
-      "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------+\n",
-      "| index |    name    |        filename        |    function name    | language |                                   path                                  |\n",
-      "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------+\n",
-      "|   0   | DSPC Model | custom_bilayer_DSPC.py | custom_bilayer_DSPC |  python  | /mnt/c/Users/gnn85523/projects/python-RAT/ratapi/examples/non_polarised |\n",
-      "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------+\n",
+      "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------------------+\n",
+      "| index |    name    |        filename        |    function name    | language |                                         path                                        |\n",
+      "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------------------+\n",
+      "|   0   | DSPC Model | custom_bilayer_DSPC.py | custom_bilayer_DSPC |  python  | C:\\Users\\steve\\Documents\\Development\\python-RAT\\ratapi\\examples\\normal_reflectivity |\n",
+      "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------------------+\n",
       "\n",
       "Data: ----------------------------------------------------------------------------------------------\n",
       "\n",
@@ -746,13 +759,13 @@
       "\n",
       "Contrasts: -----------------------------------------------------------------------------------------\n",
       "\n",
-      "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+------------+\n",
-      "| index |      name     |      data     |   background   | background action | bulk in | bulk out |  scalefactor  |    resolution   | resample |   model    |\n",
-      "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+------------+\n",
-      "|   0   | Bilayer / D2O | Bilayer / D2O | Background D2O |        add        | Silicon | SLD D2O  | Scalefactor 1 | Data Resolution |  False   | DSPC Model |\n",
-      "|   1   | Bilayer / SMW | Bilayer / SMW | Background SMW |        add        | Silicon | SLD SMW  | Scalefactor 1 | Data Resolution |  False   | DSPC Model |\n",
-      "|   2   | Bilayer / H2O | Bilayer / H2O | Background H2O |        add        | Silicon | SLD H2O  | Scalefactor 1 | Data Resolution |  False   | DSPC Model |\n",
-      "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+------------+\n",
+      "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+---------------+------------+\n",
+      "| index |      name     |      data     |   background   | background action | bulk in | bulk out |  scalefactor  |    resolution   | resample | repeat layers |   model    |\n",
+      "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+---------------+------------+\n",
+      "|   0   | Bilayer / D2O | Bilayer / D2O | Background D2O |        add        | Silicon | SLD D2O  | Scalefactor 1 | Data Resolution |  False   |       1       | DSPC Model |\n",
+      "|   1   | Bilayer / SMW | Bilayer / SMW | Background SMW |        add        | Silicon | SLD SMW  | Scalefactor 1 | Data Resolution |  False   |       1       | DSPC Model |\n",
+      "|   2   | Bilayer / H2O | Bilayer / H2O | Background H2O |        add        | Silicon | SLD H2O  | Scalefactor 1 | Data Resolution |  False   |       1       | DSPC Model |\n",
+      "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+---------------+------------+\n",
       "\n",
       "\n"
      ]
@@ -772,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 36,
    "id": "154a33df-06b9-4035-aa4c-a0e095c1bb06",
    "metadata": {},
    "outputs": [
@@ -780,16 +793,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "+------------------+-----------+\n",
-      "|     Property     |   Value   |\n",
-      "+------------------+-----------+\n",
-      "|    procedure     | calculate |\n",
-      "|     parallel     |   single  |\n",
-      "| calcSldDuringFit |   False   |\n",
-      "| resampleMinAngle |    0.9    |\n",
-      "| resampleNPoints  |     50    |\n",
-      "|     display      |    iter   |\n",
-      "+------------------+-----------+\n"
+      "+---------------------+-----------+\n",
+      "|       Property      |   Value   |\n",
+      "+---------------------+-----------+\n",
+      "|      procedure      | calculate |\n",
+      "|       parallel      |   single  |\n",
+      "| numSimulationPoints |    500    |\n",
+      "|   resampleMinAngle  |    0.9    |\n",
+      "|   resampleNPoints   |     50    |\n",
+      "|       display       |    iter   |\n",
+      "+---------------------+-----------+\n"
      ]
     }
    ],
@@ -808,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 37,
    "id": "d5d9a782-0fb1-40b6-b1fa-86307abe32a6",
    "metadata": {},
    "outputs": [
@@ -818,7 +831,7 @@
      "text": [
       "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
       "\n",
-      "Elapsed time is 0.020 seconds\n",
+      "Elapsed time is 0.001 seconds\n",
       "\n",
       "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
       "\n"
@@ -826,7 +839,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -857,7 +870,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/ratapi/examples/normal_reflectivity/custom_XY_DSPC.py b/ratapi/examples/normal_reflectivity/custom_XY_DSPC.py
index 93e25b08..c57adecf 100644
--- a/ratapi/examples/normal_reflectivity/custom_XY_DSPC.py
+++ b/ratapi/examples/normal_reflectivity/custom_XY_DSPC.py
@@ -8,6 +8,9 @@
 
 def custom_XY_DSPC(params, bulk_in, bulk_out, contrast):
     """Calculate the continuous SLD of a supported DSPC bilayer using volume restricted distribution functions."""
+    # Note - The first contrast number is 1 (not 0) so be careful if you use
+    # this variable for array indexing.
+
     # Split up the parameters
     subRough = params[0]
     oxideThick = params[1]
@@ -109,7 +112,7 @@ def custom_XY_DSPC(params, bulk_in, bulk_out, contrast):
     sldHeadL = vfHeadL * sld_Value_Head
     sldHeadR = vfHeadR * sld_Value_Head
     sldTails = vfTails * sld_Value_Tails
-    sldWat = vfWat * bulk_out[contrast]
+    sldWat = vfWat * bulk_out[contrast - 1]
 
     # Put this all together
     totSLD = sldSilicon + sldOxide + sldHeadL + sldTails + sldHeadR + sldWat
diff --git a/ratapi/examples/normal_reflectivity/custom_bilayer_DSPC.py b/ratapi/examples/normal_reflectivity/custom_bilayer_DSPC.py
index 005147ff..8646de71 100644
--- a/ratapi/examples/normal_reflectivity/custom_bilayer_DSPC.py
+++ b/ratapi/examples/normal_reflectivity/custom_bilayer_DSPC.py
@@ -25,6 +25,8 @@ def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast):
 
     The second output parameter should be the substrate roughness.
     """
+    # Note - The first contrast number is 1 (not 0) so be careful if you use
+    # this variable for array indexing.
     sub_rough = params[0]
     oxide_thick = params[1]
     oxide_hydration = params[2]
@@ -72,13 +74,13 @@ def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast):
     tailThick = vTail / lipidAPM
 
     # Manually deal with hydration for layers in this example.
-    oxSLD = (oxide_hydration * bulk_out[contrast]) + ((1 - oxide_hydration) * oxide_SLD)
-    headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead)
-    tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail)
+    oxSLD = (oxide_hydration * bulk_out[contrast - 1]) + ((1 - oxide_hydration) * oxide_SLD)
+    headSLD = (headHydration * bulk_out[contrast - 1]) + ((1 - headHydration) * SLDhead)
+    tailSLD = (bilayerHydration * bulk_out[contrast - 1]) + ((1 - bilayerHydration) * SLDtail)
 
     # Make the layers
     oxide = [oxide_thick, oxSLD, sub_rough]
-    water = [waterThick, bulk_out[contrast], bilayerRough]
+    water = [waterThick, bulk_out[contrast - 1], bilayerRough]
     head = [headThick, headSLD, bilayerRough]
     tail = [tailThick, tailSLD, bilayerRough]
 
diff --git a/ratapi/wrappers.py b/ratapi/wrappers.py
index 74eda41e..4bb3f097 100644
--- a/ratapi/wrappers.py
+++ b/ratapi/wrappers.py
@@ -75,10 +75,10 @@ def handle(*args):
                     np.array(args[0], "float"),  # params
                     np.array(args[1], "float"),  # bulk in
                     np.array(args[2], "float"),  # bulk out
-                    float(args[3] + 1),  # contrast
+                    float(args[3]),  # contrast
                 ]
                 if len(args) > 4:
-                    matlab_args.append(float(args[4] + 1))  # domain number
+                    matlab_args.append(float(args[4]))  # domain number
 
                 output, sub_rough = getattr(self.engine, self.function_name)(
                     *matlab_args,
diff --git a/tests/test_wrappers.py b/tests/test_wrappers.py
index 680b198b..782e833c 100644
--- a/tests/test_wrappers.py
+++ b/tests/test_wrappers.py
@@ -25,13 +25,13 @@ def test_matlab_wrapper() -> None:
         handle = wrapper.getHandle()
 
         mocked_engine.demo.return_value = ([2], 5)
-        result = handle([1], [2], [3], 0)
+        result = handle([1], [2], [3], 1)
         assert result == ([2], 5)
         assert wrapper.engine.demo.call_args[0] == ([1], [2], [3], 1)
         mocked_engine.demo.assert_called_once()
 
         mocked_engine.demo.return_value = ([3, 1], 7)
-        result = handle([4], [5], [6], 1, 1)
+        result = handle([4], [5], [6], 2, 2)
         assert result == ([3, 1], 7)
         assert wrapper.engine.demo.call_args[0] == ([4], [5], [6], 2, 2)
         assert mocked_engine.demo.call_count == 2

From 7c15526ca9b43334f984eef6a2c3ebb94095643c Mon Sep 17 00:00:00 2001
From: Stephen Nneji <steve.nneji@gmail.com>
Date: Wed, 4 Feb 2026 14:28:07 +0000
Subject: [PATCH 2/3] Data point are shown when show error is False

---
 ratapi/utils/plotting.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/ratapi/utils/plotting.py b/ratapi/utils/plotting.py
index d4a81e1a..7409bb05 100644
--- a/ratapi/utils/plotting.py
+++ b/ratapi/utils/plotting.py
@@ -63,14 +63,17 @@ def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool
         if event_data.dataPresent[i]:
             sd_x = data[:, 0]
             sd_y, sd_e = map(lambda x: x * mult, (data[:, 1], data[:, 2]))
+            errors = np.zeros(len(sd_e))
 
             if show_error_bar:
-                errors = np.zeros(len(sd_e))
                 valid = sd_y - sd_e >= 0
                 errors[valid] = sd_e[valid]
                 valid |= sd_y < 0
+            else:
+                valid = np.ones(len(sd_e)).astype(bool)
+                sd_e = errors
 
-                results["error"].append([sd_x[valid], sd_y[valid], sd_e[valid]])
+            results["error"].append([sd_x[valid], sd_y[valid], sd_e[valid]])
 
         results["sld"].append([])
         for j in range(len(sld)):
@@ -170,7 +173,7 @@ def plot_ref_sld_helper(
             mult = (1 if not q4 else plot_data["ref"][i][0] ** 4) / div
             ref_plot.fill_between(plot_data["ref"][i][0], ref_min * mult, ref_max * mult, alpha=0.6, color="grey")
 
-        if data.dataPresent[i] and show_error_bar:
+        if data.dataPresent[i]:
             # Plot the errorbars
             ref_plot.errorbar(
                 x=plot_data["error"][i][0],
@@ -529,7 +532,7 @@ def update_foreground(self, data):
         self.figure.canvas.restore_region(self.bg)
         plot_data = _extract_plot_data(data, self.q4, self.show_error_bar, self.shift_value)
 
-        offset = 2 if self.show_error_bar else 1
+        offset = 2
         for i in range(
             0,
             len(self.figure.axes[0].lines),

From c20a6497a3ae2195fef9afa7e69dd3d14007019b Mon Sep 17 00:00:00 2001
From: Stephen Nneji <steve.nneji@gmail.com>
Date: Thu, 5 Feb 2026 10:06:12 +0000
Subject: [PATCH 3/3] Addresses review comments

---
 .../absorption/volume_thiol_bilayer.py        |   2 +-
 ratapi/examples/domains/alloy_domains.py      |   2 +-
 ratapi/examples/domains/domains_XY_model.py   |   2 +-
 .../DSPC_custom_layers.ipynb                  | 563 +-----------------
 4 files changed, 20 insertions(+), 549 deletions(-)

diff --git a/ratapi/examples/absorption/volume_thiol_bilayer.py b/ratapi/examples/absorption/volume_thiol_bilayer.py
index cf00076b..2b9f7fd4 100644
--- a/ratapi/examples/absorption/volume_thiol_bilayer.py
+++ b/ratapi/examples/absorption/volume_thiol_bilayer.py
@@ -138,7 +138,7 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):
 
     if contrast == 2 or contrast == 4:
         output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]
-    else:
+    elif contrast == 1 or contrast == 3:
         output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]
 
     return output, subRough
diff --git a/ratapi/examples/domains/alloy_domains.py b/ratapi/examples/domains/alloy_domains.py
index 88a7fde5..90da84fd 100644
--- a/ratapi/examples/domains/alloy_domains.py
+++ b/ratapi/examples/domains/alloy_domains.py
@@ -28,7 +28,7 @@ def alloy_domains(params, bulkIn, bulkOut, contrast, domain):
     # Make the model depending on which domain we are looking at
     if domain == 1:
         output = [alloyUp, gold]
-    else:
+    elif domain == 2:
         output = [alloyDn, gold]
 
     return output, subRough
diff --git a/ratapi/examples/domains/domains_XY_model.py b/ratapi/examples/domains/domains_XY_model.py
index a014e434..6a288ba2 100644
--- a/ratapi/examples/domains/domains_XY_model.py
+++ b/ratapi/examples/domains/domains_XY_model.py
@@ -42,7 +42,7 @@ def domains_XY_model(params, bulk_in, bulk_out, contrast, domain):
     # Layer SLD depends on whether we are calculating the domain or not
     if domain == 1:
         laySLD = vfLayer * layerSLD
-    else:
+    elif domain == 2:
         laySLD = vfLayer * domainSLD
 
     # ... and finally the water SLD.
diff --git a/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb b/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb
index 3d807d1d..b90c4aa3 100644
--- a/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb
+++ b/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "4b988c4a-3a09-4b75-8a87-8ba8402635ba",
    "metadata": {},
    "outputs": [],
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "9a60cd45-0e1d-448a-b4bd-4c02bd6a3475",
    "metadata": {},
    "outputs": [],
@@ -62,368 +62,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "9038b77f-e3fc-4946-87fe-af4addf8ee84",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"sd\">&quot;&quot;&quot;A custom layer model for a DSPC supported bilayer.&quot;&quot;&quot;</span>\n",
-       "\n",
-       "<span class=\"kn\">import</span><span class=\"w\"> </span><span class=\"nn\">numpy</span><span class=\"w\"> </span><span class=\"k\">as</span><span class=\"w\"> </span><span class=\"nn\">np</span>\n",
-       "\n",
-       "\n",
-       "<span class=\"k\">def</span><span class=\"w\"> </span><span class=\"nf\">custom_bilayer_DSPC</span><span class=\"p\">(</span><span class=\"n\">params</span><span class=\"p\">,</span> <span class=\"n\">bulk_in</span><span class=\"p\">,</span> <span class=\"n\">bulk_out</span><span class=\"p\">,</span> <span class=\"n\">contrast</span><span class=\"p\">):</span>\n",
-       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;Calculate layer parameters for a DSPC supported bilayer.</span>\n",
-       "\n",
-       "<span class=\"sd\">    This file accepts 3 vectors containing the values for params, bulk in and bulk out.</span>\n",
-       "<span class=\"sd\">    The final parameter is an index of the contrast being calculated.</span>\n",
-       "\n",
-       "<span class=\"sd\">    The function should output a matrix of layer values, in the form...</span>\n",
-       "\n",
-       "<span class=\"sd\">    Output = [thick 1, SLD 1, Rough 1, Percent Hydration 1, Hydrate how 1</span>\n",
-       "<span class=\"sd\">              ....</span>\n",
-       "<span class=\"sd\">              thick n, SLD n, Rough n, Percent Hydration n, Hydration how n]</span>\n",
-       "\n",
-       "<span class=\"sd\">    The &quot;hydrate how&quot; parameter decides if the layer is hydrated with Bulk out or Bulk in phases.</span>\n",
-       "<span class=\"sd\">    Set to 1 for Bulk out, zero for Bulk in.</span>\n",
-       "<span class=\"sd\">    Alternatively, leave out hydration and just return...</span>\n",
-       "\n",
-       "<span class=\"sd\">    Output = [thick 1, SLD 1, Rough 1,</span>\n",
-       "<span class=\"sd\">              ....</span>\n",
-       "<span class=\"sd\">              thick n, SLD n, Rough n]</span>\n",
-       "\n",
-       "<span class=\"sd\">    The second output parameter should be the substrate roughness.</span>\n",
-       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
-       "    <span class=\"c1\"># Note - The first contrast number is 1 (not 0) so be careful if you use</span>\n",
-       "    <span class=\"c1\"># this variable for array indexing.</span>\n",
-       "    <span class=\"n\">sub_rough</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">oxide_thick</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">oxide_hydration</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">lipidAPM</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">3</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">headHydration</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">bilayerHydration</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">5</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">bilayerRough</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">6</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">waterThick</span> <span class=\"o\">=</span> <span class=\"n\">params</span><span class=\"p\">[</span><span class=\"mi\">7</span><span class=\"p\">]</span>\n",
-       "\n",
-       "    <span class=\"c1\"># We have a constant SLD for the bilayer</span>\n",
-       "    <span class=\"n\">oxide_SLD</span> <span class=\"o\">=</span> <span class=\"mf\">3.41e-6</span>\n",
-       "\n",
-       "    <span class=\"c1\"># Now make the lipid layers</span>\n",
-       "    <span class=\"c1\"># Use known lipid volume and compositions to make the layers</span>\n",
-       "\n",
-       "    <span class=\"c1\"># define all the neutron b&#39;s.</span>\n",
-       "    <span class=\"n\">bc</span> <span class=\"o\">=</span> <span class=\"mf\">0.6646e-4</span>  <span class=\"c1\"># Carbon</span>\n",
-       "    <span class=\"n\">bo</span> <span class=\"o\">=</span> <span class=\"mf\">0.5843e-4</span>  <span class=\"c1\"># Oxygen</span>\n",
-       "    <span class=\"n\">bh</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"mf\">0.3739e-4</span>  <span class=\"c1\"># Hydrogen</span>\n",
-       "    <span class=\"n\">bp</span> <span class=\"o\">=</span> <span class=\"mf\">0.513e-4</span>  <span class=\"c1\"># Phosphorus</span>\n",
-       "    <span class=\"n\">bn</span> <span class=\"o\">=</span> <span class=\"mf\">0.936e-4</span>  <span class=\"c1\"># Nitrogen</span>\n",
-       "\n",
-       "    <span class=\"c1\"># Now make the lipid groups</span>\n",
-       "    <span class=\"n\">COO</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">4</span> <span class=\"o\">*</span> <span class=\"n\">bo</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">bc</span><span class=\"p\">)</span>\n",
-       "    <span class=\"n\">GLYC</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">3</span> <span class=\"o\">*</span> <span class=\"n\">bc</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">5</span> <span class=\"o\">*</span> <span class=\"n\">bh</span><span class=\"p\">)</span>\n",
-       "    <span class=\"n\">CH3</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">bc</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">6</span> <span class=\"o\">*</span> <span class=\"n\">bh</span><span class=\"p\">)</span>\n",
-       "    <span class=\"n\">PO4</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">*</span> <span class=\"n\">bp</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">4</span> <span class=\"o\">*</span> <span class=\"n\">bo</span><span class=\"p\">)</span>\n",
-       "    <span class=\"n\">CH2</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">*</span> <span class=\"n\">bc</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">bh</span><span class=\"p\">)</span>\n",
-       "    <span class=\"n\">CHOL</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">5</span> <span class=\"o\">*</span> <span class=\"n\">bc</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">12</span> <span class=\"o\">*</span> <span class=\"n\">bh</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">*</span> <span class=\"n\">bn</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"c1\"># Group these into heads and tails:</span>\n",
-       "    <span class=\"n\">Head</span> <span class=\"o\">=</span> <span class=\"n\">CHOL</span> <span class=\"o\">+</span> <span class=\"n\">PO4</span> <span class=\"o\">+</span> <span class=\"n\">GLYC</span> <span class=\"o\">+</span> <span class=\"n\">COO</span>\n",
-       "    <span class=\"n\">Tails</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mi\">34</span> <span class=\"o\">*</span> <span class=\"n\">CH2</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">CH3</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"c1\"># We need volumes for each. Use literature values:</span>\n",
-       "    <span class=\"n\">vHead</span> <span class=\"o\">=</span> <span class=\"mi\">319</span>\n",
-       "    <span class=\"n\">vTail</span> <span class=\"o\">=</span> <span class=\"mi\">782</span>\n",
-       "\n",
-       "    <span class=\"c1\"># We use the volumes to calculate the SLDs</span>\n",
-       "    <span class=\"n\">SLDhead</span> <span class=\"o\">=</span> <span class=\"n\">Head</span> <span class=\"o\">/</span> <span class=\"n\">vHead</span>\n",
-       "    <span class=\"n\">SLDtail</span> <span class=\"o\">=</span> <span class=\"n\">Tails</span> <span class=\"o\">/</span> <span class=\"n\">vTail</span>\n",
-       "\n",
-       "    <span class=\"c1\"># We calculate the layer thickness&#39; from the volumes and the APM</span>\n",
-       "    <span class=\"n\">headThick</span> <span class=\"o\">=</span> <span class=\"n\">vHead</span> <span class=\"o\">/</span> <span class=\"n\">lipidAPM</span>\n",
-       "    <span class=\"n\">tailThick</span> <span class=\"o\">=</span> <span class=\"n\">vTail</span> <span class=\"o\">/</span> <span class=\"n\">lipidAPM</span>\n",
-       "\n",
-       "    <span class=\"c1\"># Manually deal with hydration for layers in this example.</span>\n",
-       "    <span class=\"n\">oxSLD</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">oxide_hydration</span> <span class=\"o\">*</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">oxide_hydration</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">oxide_SLD</span><span class=\"p\">)</span>\n",
-       "    <span class=\"n\">headSLD</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">headHydration</span> <span class=\"o\">*</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">headHydration</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">SLDhead</span><span class=\"p\">)</span>\n",
-       "    <span class=\"n\">tailSLD</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">bilayerHydration</span> <span class=\"o\">*</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">])</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">bilayerHydration</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">SLDtail</span><span class=\"p\">)</span>\n",
-       "\n",
-       "    <span class=\"c1\"># Make the layers</span>\n",
-       "    <span class=\"n\">oxide</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">oxide_thick</span><span class=\"p\">,</span> <span class=\"n\">oxSLD</span><span class=\"p\">,</span> <span class=\"n\">sub_rough</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">water</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">waterThick</span><span class=\"p\">,</span> <span class=\"n\">bulk_out</span><span class=\"p\">[</span><span class=\"n\">contrast</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"n\">bilayerRough</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">head</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">headThick</span><span class=\"p\">,</span> <span class=\"n\">headSLD</span><span class=\"p\">,</span> <span class=\"n\">bilayerRough</span><span class=\"p\">]</span>\n",
-       "    <span class=\"n\">tail</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">tailThick</span><span class=\"p\">,</span> <span class=\"n\">tailSLD</span><span class=\"p\">,</span> <span class=\"n\">bilayerRough</span><span class=\"p\">]</span>\n",
-       "\n",
-       "    <span class=\"n\">output</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([</span><span class=\"n\">oxide</span><span class=\"p\">,</span> <span class=\"n\">water</span><span class=\"p\">,</span> <span class=\"n\">head</span><span class=\"p\">,</span> <span class=\"n\">tail</span><span class=\"p\">,</span> <span class=\"n\">tail</span><span class=\"p\">,</span> <span class=\"n\">head</span><span class=\"p\">])</span>\n",
-       "\n",
-       "    <span class=\"k\">return</span> <span class=\"n\">output</span><span class=\"p\">,</span> <span class=\"n\">sub_rough</span>\n",
-       "</pre></div>\n"
-      ],
-      "text/latex": [
-       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
-       "\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}A custom layer model for a DSPC supported bilayer.\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
-       "\n",
-       "\\PY{k+kn}{import}\\PY{+w}{ }\\PY{n+nn}{numpy}\\PY{+w}{ }\\PY{k}{as}\\PY{+w}{ }\\PY{n+nn}{np}\n",
-       "\n",
-       "\n",
-       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{custom\\PYZus{}bilayer\\PYZus{}DSPC}\\PY{p}{(}\\PY{n}{params}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}in}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{,} \\PY{n}{contrast}\\PY{p}{)}\\PY{p}{:}\n",
-       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Calculate layer parameters for a DSPC supported bilayer.}\n",
-       "\n",
-       "\\PY{l+s+sd}{    This file accepts 3 vectors containing the values for params, bulk in and bulk out.}\n",
-       "\\PY{l+s+sd}{    The final parameter is an index of the contrast being calculated.}\n",
-       "\n",
-       "\\PY{l+s+sd}{    The function should output a matrix of layer values, in the form...}\n",
-       "\n",
-       "\\PY{l+s+sd}{    Output = [thick 1, SLD 1, Rough 1, Percent Hydration 1, Hydrate how 1}\n",
-       "\\PY{l+s+sd}{              ....}\n",
-       "\\PY{l+s+sd}{              thick n, SLD n, Rough n, Percent Hydration n, Hydration how n]}\n",
-       "\n",
-       "\\PY{l+s+sd}{    The \\PYZdq{}hydrate how\\PYZdq{} parameter decides if the layer is hydrated with Bulk out or Bulk in phases.}\n",
-       "\\PY{l+s+sd}{    Set to 1 for Bulk out, zero for Bulk in.}\n",
-       "\\PY{l+s+sd}{    Alternatively, leave out hydration and just return...}\n",
-       "\n",
-       "\\PY{l+s+sd}{    Output = [thick 1, SLD 1, Rough 1,}\n",
-       "\\PY{l+s+sd}{              ....}\n",
-       "\\PY{l+s+sd}{              thick n, SLD n, Rough n]}\n",
-       "\n",
-       "\\PY{l+s+sd}{    The second output parameter should be the substrate roughness.}\n",
-       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
-       "    \\PY{c+c1}{\\PYZsh{} Note \\PYZhy{} The first contrast number is 1 (not 0) so be careful if you use}\n",
-       "    \\PY{c+c1}{\\PYZsh{} this variable for array indexing.}\n",
-       "    \\PY{n}{sub\\PYZus{}rough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\n",
-       "    \\PY{n}{oxide\\PYZus{}thick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\n",
-       "    \\PY{n}{oxide\\PYZus{}hydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\n",
-       "    \\PY{n}{lipidAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n",
-       "    \\PY{n}{headHydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n",
-       "    \\PY{n}{bilayerHydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{5}\\PY{p}{]}\n",
-       "    \\PY{n}{bilayerRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{6}\\PY{p}{]}\n",
-       "    \\PY{n}{waterThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{7}\\PY{p}{]}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} We have a constant SLD for the bilayer}\n",
-       "    \\PY{n}{oxide\\PYZus{}SLD} \\PY{o}{=} \\PY{l+m+mf}{3.41e\\PYZhy{}6}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} Now make the lipid layers}\n",
-       "    \\PY{c+c1}{\\PYZsh{} Use known lipid volume and compositions to make the layers}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} define all the neutron b\\PYZsq{}s.}\n",
-       "    \\PY{n}{bc} \\PY{o}{=} \\PY{l+m+mf}{0.6646e\\PYZhy{}4}  \\PY{c+c1}{\\PYZsh{} Carbon}\n",
-       "    \\PY{n}{bo} \\PY{o}{=} \\PY{l+m+mf}{0.5843e\\PYZhy{}4}  \\PY{c+c1}{\\PYZsh{} Oxygen}\n",
-       "    \\PY{n}{bh} \\PY{o}{=} \\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.3739e\\PYZhy{}4}  \\PY{c+c1}{\\PYZsh{} Hydrogen}\n",
-       "    \\PY{n}{bp} \\PY{o}{=} \\PY{l+m+mf}{0.513e\\PYZhy{}4}  \\PY{c+c1}{\\PYZsh{} Phosphorus}\n",
-       "    \\PY{n}{bn} \\PY{o}{=} \\PY{l+m+mf}{0.936e\\PYZhy{}4}  \\PY{c+c1}{\\PYZsh{} Nitrogen}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} Now make the lipid groups}\n",
-       "    \\PY{n}{COO} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{4} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)}\n",
-       "    \\PY{n}{GLYC} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{3} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{5} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)}\n",
-       "    \\PY{n}{CH3} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{6} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)}\n",
-       "    \\PY{n}{PO4} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bp}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{4} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)}\n",
-       "    \\PY{n}{CH2} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)}\n",
-       "    \\PY{n}{CHOL} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{5} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{12} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bn}\\PY{p}{)}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} Group these into heads and tails:}\n",
-       "    \\PY{n}{Head} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{n}{COO}\n",
-       "    \\PY{n}{Tails} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{34} \\PY{o}{*} \\PY{n}{CH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\\PY{p}{)}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} We need volumes for each. Use literature values:}\n",
-       "    \\PY{n}{vHead} \\PY{o}{=} \\PY{l+m+mi}{319}\n",
-       "    \\PY{n}{vTail} \\PY{o}{=} \\PY{l+m+mi}{782}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} We use the volumes to calculate the SLDs}\n",
-       "    \\PY{n}{SLDhead} \\PY{o}{=} \\PY{n}{Head} \\PY{o}{/} \\PY{n}{vHead}\n",
-       "    \\PY{n}{SLDtail} \\PY{o}{=} \\PY{n}{Tails} \\PY{o}{/} \\PY{n}{vTail}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} We calculate the layer thickness\\PYZsq{} from the volumes and the APM}\n",
-       "    \\PY{n}{headThick} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{lipidAPM}\n",
-       "    \\PY{n}{tailThick} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{lipidAPM}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} Manually deal with hydration for layers in this example.}\n",
-       "    \\PY{n}{oxSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{oxide\\PYZus{}hydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{oxide\\PYZus{}hydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{oxide\\PYZus{}SLD}\\PY{p}{)}\n",
-       "    \\PY{n}{headSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{headHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{headHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDhead}\\PY{p}{)}\n",
-       "    \\PY{n}{tailSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDtail}\\PY{p}{)}\n",
-       "\n",
-       "    \\PY{c+c1}{\\PYZsh{} Make the layers}\n",
-       "    \\PY{n}{oxide} \\PY{o}{=} \\PY{p}{[}\\PY{n}{oxide\\PYZus{}thick}\\PY{p}{,} \\PY{n}{oxSLD}\\PY{p}{,} \\PY{n}{sub\\PYZus{}rough}\\PY{p}{]}\n",
-       "    \\PY{n}{water} \\PY{o}{=} \\PY{p}{[}\\PY{n}{waterThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n",
-       "    \\PY{n}{head} \\PY{o}{=} \\PY{p}{[}\\PY{n}{headThick}\\PY{p}{,} \\PY{n}{headSLD}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n",
-       "    \\PY{n}{tail} \\PY{o}{=} \\PY{p}{[}\\PY{n}{tailThick}\\PY{p}{,} \\PY{n}{tailSLD}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n",
-       "\n",
-       "    \\PY{n}{output} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{p}{[}\\PY{n}{oxide}\\PY{p}{,} \\PY{n}{water}\\PY{p}{,} \\PY{n}{head}\\PY{p}{,} \\PY{n}{tail}\\PY{p}{,} \\PY{n}{tail}\\PY{p}{,} \\PY{n}{head}\\PY{p}{]}\\PY{p}{)}\n",
-       "\n",
-       "    \\PY{k}{return} \\PY{n}{output}\\PY{p}{,} \\PY{n}{sub\\PYZus{}rough}\n",
-       "\\end{Verbatim}\n"
-      ],
-      "text/plain": [
-       "\"\"\"A custom layer model for a DSPC supported bilayer.\"\"\"\n",
-       "\n",
-       "import numpy as np\n",
-       "\n",
-       "\n",
-       "def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast):\n",
-       "    \"\"\"Calculate layer parameters for a DSPC supported bilayer.\n",
-       "\n",
-       "    This file accepts 3 vectors containing the values for params, bulk in and bulk out.\n",
-       "    The final parameter is an index of the contrast being calculated.\n",
-       "\n",
-       "    The function should output a matrix of layer values, in the form...\n",
-       "\n",
-       "    Output = [thick 1, SLD 1, Rough 1, Percent Hydration 1, Hydrate how 1\n",
-       "              ....\n",
-       "              thick n, SLD n, Rough n, Percent Hydration n, Hydration how n]\n",
-       "\n",
-       "    The \"hydrate how\" parameter decides if the layer is hydrated with Bulk out or Bulk in phases.\n",
-       "    Set to 1 for Bulk out, zero for Bulk in.\n",
-       "    Alternatively, leave out hydration and just return...\n",
-       "\n",
-       "    Output = [thick 1, SLD 1, Rough 1,\n",
-       "              ....\n",
-       "              thick n, SLD n, Rough n]\n",
-       "\n",
-       "    The second output parameter should be the substrate roughness.\n",
-       "    \"\"\"\n",
-       "    # Note - The first contrast number is 1 (not 0) so be careful if you use\n",
-       "    # this variable for array indexing.\n",
-       "    sub_rough = params[0]\n",
-       "    oxide_thick = params[1]\n",
-       "    oxide_hydration = params[2]\n",
-       "    lipidAPM = params[3]\n",
-       "    headHydration = params[4]\n",
-       "    bilayerHydration = params[5]\n",
-       "    bilayerRough = params[6]\n",
-       "    waterThick = params[7]\n",
-       "\n",
-       "    # We have a constant SLD for the bilayer\n",
-       "    oxide_SLD = 3.41e-6\n",
-       "\n",
-       "    # Now make the lipid layers\n",
-       "    # Use known lipid volume and compositions to make the layers\n",
-       "\n",
-       "    # define all the neutron b's.\n",
-       "    bc = 0.6646e-4  # Carbon\n",
-       "    bo = 0.5843e-4  # Oxygen\n",
-       "    bh = -0.3739e-4  # Hydrogen\n",
-       "    bp = 0.513e-4  # Phosphorus\n",
-       "    bn = 0.936e-4  # Nitrogen\n",
-       "\n",
-       "    # Now make the lipid groups\n",
-       "    COO = (4 * bo) + (2 * bc)\n",
-       "    GLYC = (3 * bc) + (5 * bh)\n",
-       "    CH3 = (2 * bc) + (6 * bh)\n",
-       "    PO4 = (1 * bp) + (4 * bo)\n",
-       "    CH2 = (1 * bc) + (2 * bh)\n",
-       "    CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n",
-       "\n",
-       "    # Group these into heads and tails:\n",
-       "    Head = CHOL + PO4 + GLYC + COO\n",
-       "    Tails = (34 * CH2) + (2 * CH3)\n",
-       "\n",
-       "    # We need volumes for each. Use literature values:\n",
-       "    vHead = 319\n",
-       "    vTail = 782\n",
-       "\n",
-       "    # We use the volumes to calculate the SLDs\n",
-       "    SLDhead = Head / vHead\n",
-       "    SLDtail = Tails / vTail\n",
-       "\n",
-       "    # We calculate the layer thickness' from the volumes and the APM\n",
-       "    headThick = vHead / lipidAPM\n",
-       "    tailThick = vTail / lipidAPM\n",
-       "\n",
-       "    # Manually deal with hydration for layers in this example.\n",
-       "    oxSLD = (oxide_hydration * bulk_out[contrast - 1]) + ((1 - oxide_hydration) * oxide_SLD)\n",
-       "    headSLD = (headHydration * bulk_out[contrast - 1]) + ((1 - headHydration) * SLDhead)\n",
-       "    tailSLD = (bilayerHydration * bulk_out[contrast - 1]) + ((1 - bilayerHydration) * SLDtail)\n",
-       "\n",
-       "    # Make the layers\n",
-       "    oxide = [oxide_thick, oxSLD, sub_rough]\n",
-       "    water = [waterThick, bulk_out[contrast - 1], bilayerRough]\n",
-       "    head = [headThick, headSLD, bilayerRough]\n",
-       "    tail = [tailThick, tailSLD, bilayerRough]\n",
-       "\n",
-       "    output = np.array([oxide, water, head, tail, tail, head])\n",
-       "\n",
-       "    return output, sub_rough"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Code(filename='custom_bilayer_DSPC.py', language='python')"
    ]
@@ -438,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "70494ef9-6cc5-47dc-9d02-6506645de46b",
    "metadata": {},
    "outputs": [],
@@ -467,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "453fe3d2-162a-42bb-91ee-b1d020ffd29e",
    "metadata": {},
    "outputs": [],
@@ -491,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "fa4c1b96-3a1b-4aa6-8d61-68f24b0cb482",
    "metadata": {},
    "outputs": [],
@@ -518,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "2e649c26-b32b-4c79-8ae7-fa701c87e6c2",
    "metadata": {},
    "outputs": [],
@@ -536,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "5d51954f-469a-4044-9a7d-1b6e30474a6b",
    "metadata": {},
    "outputs": [],
@@ -567,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "b1e4d313-8450-459b-b60e-868fe82f06b0",
    "metadata": {},
    "outputs": [],
@@ -585,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "efc7b351-2112-40c4-862b-a47e4570d173",
    "metadata": {},
    "outputs": [],
@@ -636,141 +278,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "ee889e55-8357-4363-860d-fb1c13bb8e8b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: ----------------------------------------------------------------------------------------------\n",
-      "\n",
-      "Orso lipid example - custom layers\n",
-      "\n",
-      "Calculation: ---------------------------------------------------------------------------------------\n",
-      "\n",
-      "normal\n",
-      "\n",
-      "Model: ---------------------------------------------------------------------------------------------\n",
-      "\n",
-      "custom layers\n",
-      "\n",
-      "Geometry: ------------------------------------------------------------------------------------------\n",
-      "\n",
-      "substrate/liquid\n",
-      "\n",
-      "Parameters: ----------------------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+---------------------+------+-------+------+------+------------+\n",
-      "| index |         name        | min  | value | max  | fit  | prior type |\n",
-      "+-------+---------------------+------+-------+------+------+------------+\n",
-      "|   0   | Substrate Roughness | 1.0  |  3.0  | 10.0 | True |  uniform   |\n",
-      "|   1   |   Oxide Thickness   | 5.0  |  20.0 | 60.0 | True |  uniform   |\n",
-      "|   2   |   Oxide Hydration   | 0.0  |  0.2  | 0.5  | True |  uniform   |\n",
-      "|   3   |      Lipid APM      | 45.0 |  55.0 | 65.0 | True |  uniform   |\n",
-      "|   4   |    Head Hydration   | 0.0  |  0.2  | 0.5  | True |  gaussian  |\n",
-      "|   5   |  Bilayer Hydration  | 0.0  |  0.1  | 0.2  | True |  uniform   |\n",
-      "|   6   |  Bilayer Roughness  | 2.0  |  4.0  | 8.0  | True |  uniform   |\n",
-      "|   7   |   Water Thickness   | 0.0  |  2.0  | 10.0 | True |  uniform   |\n",
-      "+-------+---------------------+------+-------+------+------+------------+\n",
-      "\n",
-      "Bulk In: -------------------------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+---------+----------+-----------+----------+-------+------------+\n",
-      "| index |   name  |   min    |   value   |   max    |  fit  | prior type |\n",
-      "+-------+---------+----------+-----------+----------+-------+------------+\n",
-      "|   0   | Silicon | 2.07e-06 | 2.073e-06 | 2.08e-06 | False |  uniform   |\n",
-      "+-------+---------+----------+-----------+----------+-------+------------+\n",
-      "\n",
-      "Bulk Out: ------------------------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+---------+--------+-----------+----------+------+------------+\n",
-      "| index |   name  |  min   |   value   |   max    | fit  | prior type |\n",
-      "+-------+---------+--------+-----------+----------+------+------------+\n",
-      "|   0   | SLD D2O | 5e-06  |  6.35e-06 | 6.35e-06 | True |  uniform   |\n",
-      "|   1   | SLD SMW | 1e-06  | 2.073e-06 |  3e-06   | True |  uniform   |\n",
-      "|   2   | SLD H2O | -6e-07 |  -5.6e-07 |  -3e-07  | True |  uniform   |\n",
-      "+-------+---------+--------+-----------+----------+------+------------+\n",
-      "\n",
-      "Scalefactors: --------------------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+---------------+-----+-------+-----+------+------------+\n",
-      "| index |      name     | min | value | max | fit  | prior type |\n",
-      "+-------+---------------+-----+-------+-----+------+------------+\n",
-      "|   0   | Scalefactor 1 | 0.5 |  1.0  | 2.0 | True |  uniform   |\n",
-      "+-------+---------------+-----+-------+-----+------+------------+\n",
-      "\n",
-      "Background Parameters: -----------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+--------------------------+-------+-------+-------+------+------------+\n",
-      "| index |           name           |  min  | value |  max  | fit  | prior type |\n",
-      "+-------+--------------------------+-------+-------+-------+------+------------+\n",
-      "|   0   | Background parameter D2O | 1e-10 | 1e-07 | 1e-05 | True |  uniform   |\n",
-      "|   1   | Background parameter SMW | 1e-10 | 1e-07 | 1e-05 | True |  uniform   |\n",
-      "|   2   | Background parameter H2O | 1e-10 | 1e-07 | 1e-05 | True |  uniform   |\n",
-      "+-------+--------------------------+-------+-------+-------+------+------------+\n",
-      "\n",
-      "Backgrounds: ---------------------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+----------------+----------+--------------------------+\n",
-      "| index |      name      |   type   |          source          |\n",
-      "+-------+----------------+----------+--------------------------+\n",
-      "|   0   | Background D2O | constant | Background parameter D2O |\n",
-      "|   1   | Background SMW | constant | Background parameter SMW |\n",
-      "|   2   | Background H2O | constant | Background parameter H2O |\n",
-      "+-------+----------------+----------+--------------------------+\n",
-      "\n",
-      "Resolution Parameters: -----------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+--------------------+------+-------+------+-------+------------+\n",
-      "| index |        name        | min  | value | max  |  fit  | prior type |\n",
-      "+-------+--------------------+------+-------+------+-------+------------+\n",
-      "|   0   | Resolution Param 1 | 0.01 |  0.03 | 0.05 | False |  uniform   |\n",
-      "+-------+--------------------+------+-------+------+-------+------------+\n",
-      "\n",
-      "Resolutions: ---------------------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+-----------------+----------+--------------------+---------+\n",
-      "| index |       name      |   type   |       source       | value 1 |\n",
-      "+-------+-----------------+----------+--------------------+---------+\n",
-      "|   0   |   Resolution 1  | constant | Resolution Param 1 |         |\n",
-      "|   1   | Data Resolution |   data   |                    |         |\n",
-      "+-------+-----------------+----------+--------------------+---------+\n",
-      "\n",
-      "Custom Files: --------------------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------------------+\n",
-      "| index |    name    |        filename        |    function name    | language |                                         path                                        |\n",
-      "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------------------+\n",
-      "|   0   | DSPC Model | custom_bilayer_DSPC.py | custom_bilayer_DSPC |  python  | C:\\Users\\steve\\Documents\\Development\\python-RAT\\ratapi\\examples\\normal_reflectivity |\n",
-      "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------------------+\n",
-      "\n",
-      "Data: ----------------------------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+---------------+-----------------------+------------------+----------------------+\n",
-      "| index |      name     |          data         |    data range    |   simulation range   |\n",
-      "+-------+---------------+-----------------------+------------------+----------------------+\n",
-      "|   0   |   Simulation  |           []          |        []        |     [0.005, 0.7]     |\n",
-      "|   1   | Bilayer / D2O | Data array: [146 x 4] |  [0.013, 0.37]   | [0.0057118, 0.39606] |\n",
-      "|   2   | Bilayer / SMW |  Data array: [97 x 4] | [0.013, 0.32996] | [0.0076029, 0.32996] |\n",
-      "|   3   | Bilayer / H2O | Data array: [104 x 4] | [0.013, 0.33048] | [0.0063374, 0.33048] |\n",
-      "+-------+---------------+-----------------------+------------------+----------------------+\n",
-      "\n",
-      "Contrasts: -----------------------------------------------------------------------------------------\n",
-      "\n",
-      "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+---------------+------------+\n",
-      "| index |      name     |      data     |   background   | background action | bulk in | bulk out |  scalefactor  |    resolution   | resample | repeat layers |   model    |\n",
-      "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+---------------+------------+\n",
-      "|   0   | Bilayer / D2O | Bilayer / D2O | Background D2O |        add        | Silicon | SLD D2O  | Scalefactor 1 | Data Resolution |  False   |       1       | DSPC Model |\n",
-      "|   1   | Bilayer / SMW | Bilayer / SMW | Background SMW |        add        | Silicon | SLD SMW  | Scalefactor 1 | Data Resolution |  False   |       1       | DSPC Model |\n",
-      "|   2   | Bilayer / H2O | Bilayer / H2O | Background H2O |        add        | Silicon | SLD H2O  | Scalefactor 1 | Data Resolution |  False   |       1       | DSPC Model |\n",
-      "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+---------------+------------+\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(problem)"
    ]
@@ -785,27 +296,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "154a33df-06b9-4035-aa4c-a0e095c1bb06",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+---------------------+-----------+\n",
-      "|       Property      |   Value   |\n",
-      "+---------------------+-----------+\n",
-      "|      procedure      | calculate |\n",
-      "|       parallel      |   single  |\n",
-      "| numSimulationPoints |    500    |\n",
-      "|   resampleMinAngle  |    0.9    |\n",
-      "|   resampleNPoints   |     50    |\n",
-      "|       display       |    iter   |\n",
-      "+---------------------+-----------+\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "controls = RAT.Controls()\n",
     "print(controls)"
@@ -821,33 +315,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "d5d9a782-0fb1-40b6-b1fa-86307abe32a6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "\n",
-      "Elapsed time is 0.001 seconds\n",
-      "\n",
-      "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "problem, results = RAT.run(problem, controls)\n",
     "RAT.plotting.plot_ref_sld(problem, results)"