From a524e3015ce20c33f1ef56f09b0a8e4e5abb517e Mon Sep 17 00:00:00 2001
From: StephenNneji <34302892+StephenNneji@users.noreply.github.com>
Date: Mon, 19 Jan 2026 13:43:38 +0000
Subject: [PATCH 1/9] Fixes matlab engine not shutting down (#196)

---
 ratapi/wrappers.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/ratapi/wrappers.py b/ratapi/wrappers.py
index 74eda41e..9475b93e 100644
--- a/ratapi/wrappers.py
+++ b/ratapi/wrappers.py
@@ -23,9 +23,12 @@ def start_matlab():
     future = None
     if os.environ.get("DELAY_MATLAB_START", "0") == "0":
         with suppress(ImportError):
+            import atexit
+
             import matlab.engine
 
             future = matlab.engine.start_matlab(background=True)
+            atexit.register(lambda: future.result())
 
     return future
 

From 048c4e23f6950b507f8b1dd6d67de97dcf6ed9f0 Mon Sep 17 00:00:00 2001
From: StephenNneji <34302892+StephenNneji@users.noreply.github.com>
Date: Mon, 9 Feb 2026 13:08:47 +0000
Subject: [PATCH 2/9] Unifies the contrast number used by python and cpp to be
 same with matlab and Data points now show when `show_error` is False (#197)

---
 cpp/RAT                                       |   2 +-
 .../absorption/volume_thiol_bilayer.py        |  23 +-
 .../bayes_benchmark/bayes_benchmark.ipynb     |  11 +-
 .../convert_rascal_project/Model_IIb.py       |   7 +-
 ratapi/examples/domains/alloy_domains.py      |   7 +-
 ratapi/examples/domains/domains_XY_model.py   |   9 +-
 ratapi/examples/languages/custom_bilayer.cpp  |  10 +-
 ratapi/examples/languages/custom_bilayer.py   |  11 +-
 .../DSPC_custom_layers.ipynb                  | 556 +-----------------
 .../normal_reflectivity/custom_XY_DSPC.py     |   5 +-
 .../custom_bilayer_DSPC.py                    |  10 +-
 ratapi/utils/plotting.py                      |  15 +-
 ratapi/wrappers.py                            |   4 +-
 tests/test_wrappers.py                        |   4 +-
 14 files changed, 88 insertions(+), 586 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..2b9f7fd4 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]
@@ -131,11 +134,11 @@ 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:
+    elif contrast == 1 or contrast == 3:
         output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]
 
     return output, subRough
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..90da84fd 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]
@@ -23,9 +26,9 @@ 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:
+    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 00567666..6a288ba2 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:
+    elif domain == 2:
         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..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": 1,
+   "execution_count": null,
    "id": "4b988c4a-3a09-4b75-8a87-8ba8402635ba",
    "metadata": {},
    "outputs": [],
@@ -29,12 +29,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "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()"
    ]
   },
   {
@@ -61,356 +62,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "9038b77f-e3fc-4946-87fe-af4addf8ee84",
    "metadata": {},
-   "outputs": [
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-       ".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",
-       "\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=\"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=\"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=\"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",
-       "\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\">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{k+kn}{import} \\PY{n+nn}{numpy} \\PY{k}{as} \\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",
-       "\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{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{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",
-       "\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}{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": [
-       "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",
-       "\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",
-       "    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 - oxide_hydration) * oxide_SLD)\n",
-       "    headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead)\n",
-       "    tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail)\n",
-       "\n",
-       "    # Make the layers\n",
-       "    oxide = [oxide_thick, oxSLD, sub_rough]\n",
-       "    water = [waterThick, bulk_out[contrast], 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": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Code(filename='custom_bilayer_DSPC.py', language='python')"
    ]
@@ -425,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "70494ef9-6cc5-47dc-9d02-6506645de46b",
    "metadata": {},
    "outputs": [],
@@ -454,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "453fe3d2-162a-42bb-91ee-b1d020ffd29e",
    "metadata": {},
    "outputs": [],
@@ -478,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "fa4c1b96-3a1b-4aa6-8d61-68f24b0cb482",
    "metadata": {},
    "outputs": [],
@@ -505,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "2e649c26-b32b-4c79-8ae7-fa701c87e6c2",
    "metadata": {},
    "outputs": [],
@@ -523,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "5d51954f-469a-4044-9a7d-1b6e30474a6b",
    "metadata": {},
    "outputs": [],
@@ -554,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "b1e4d313-8450-459b-b60e-868fe82f06b0",
    "metadata": {},
    "outputs": [],
@@ -572,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "efc7b351-2112-40c4-862b-a47e4570d173",
    "metadata": {},
    "outputs": [],
@@ -623,141 +278,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "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",
-      "non polarised\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 |  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",
-      "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",
-      "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",
-      "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",
-      "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",
-      "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",
-      "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",
-      "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",
-      "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",
-      "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 |   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",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(problem)"
    ]
@@ -772,27 +296,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "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",
-      "| calcSldDuringFit |   False   |\n",
-      "| resampleMinAngle |    0.9    |\n",
-      "| resampleNPoints  |     50    |\n",
-      "|     display      |    iter   |\n",
-      "+------------------+-----------+\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "controls = RAT.Controls()\n",
     "print(controls)"
@@ -808,33 +315,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "d5d9a782-0fb1-40b6-b1fa-86307abe32a6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "\n",
-      "Elapsed time is 0.020 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)"
@@ -857,7 +341,7 @@
    "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/utils/plotting.py b/ratapi/utils/plotting.py
index d4a81e1a..dc1ade53 100644
--- a/ratapi/utils/plotting.py
+++ b/ratapi/utils/plotting.py
@@ -53,7 +53,7 @@ def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool
         zip(event_data.reflectivity, event_data.shiftedData, event_data.sldProfiles, strict=False)
     ):
         # Calculate the divisor
-        div = 1 if i == 0 and not q4 else 10 ** ((i / 100) * shift_value)
+        div = 1 if i == 0 and not q4 else 10 ** ((i + 1) / 100 * shift_value)
         q4_data = 1 if not q4 or not event_data.dataPresent[i] else data[:, 0] ** 4
         mult = q4_data / div
 
@@ -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)):
@@ -145,7 +148,7 @@ def plot_ref_sld_helper(
         fig.clf()
         fig.subplots(1, 2)
 
-    fig.subplots_adjust(wspace=0.3)
+    fig.subplots_adjust(wspace=0.3, hspace=0)
 
     ref_plot: plt.Axes = fig.axes[0]
     sld_plot: plt.Axes = fig.axes[1]
@@ -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),
diff --git a/ratapi/wrappers.py b/ratapi/wrappers.py
index 9475b93e..38f276ac 100644
--- a/ratapi/wrappers.py
+++ b/ratapi/wrappers.py
@@ -78,10 +78,10 @@ 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 a599d448bfd469755eb67a84af0a874fd55e5fe1 Mon Sep 17 00:00:00 2001
From: StephenNneji <34302892+StephenNneji@users.noreply.github.com>
Date: Mon, 16 Feb 2026 11:49:16 +0000
Subject: [PATCH 3/9] Allow relative paths for custom file (#198)

---
 ratapi/models.py      |  8 +-------
 ratapi/project.py     | 10 ++++++++--
 tests/conftest.py     |  8 ++++++++
 tests/test_models.py  | 11 +++++------
 tests/test_project.py | 20 +++++++++++---------
 5 files changed, 33 insertions(+), 24 deletions(-)

diff --git a/ratapi/models.py b/ratapi/models.py
index 7d0d4f74..755ea974 100644
--- a/ratapi/models.py
+++ b/ratapi/models.py
@@ -313,13 +313,7 @@ class CustomFile(RATModel):
     filename: str = ""
     function_name: str = ""
     language: Languages = Languages.Python
-    path: pathlib.Path = pathlib.Path(".").resolve()
-
-    @field_validator("path")
-    @classmethod
-    def resolve_relative_paths(cls, path: pathlib.Path) -> pathlib.Path:
-        """Return the absolute path of the given path."""
-        return path.resolve()
+    path: pathlib.Path = pathlib.Path(".")
 
     def model_post_init(self, __context: Any) -> None:
         """Autogenerate the function name from the filename if not set.
diff --git a/ratapi/project.py b/ratapi/project.py
index fcbbaf83..b8ca49f2 100644
--- a/ratapi/project.py
+++ b/ratapi/project.py
@@ -956,12 +956,16 @@ def make_data_dict(item):
             elif field == "custom_files":
 
                 def make_custom_file_dict(item):
-                    return {
+                    file_dict = {
                         "name": item.name,
                         "filename": item.filename,
                         "language": item.language,
                         "path": try_relative_to(item.path, filepath.parent),
                     }
+                    if item.name != item.function_name:
+                        file_dict["function_name"] = item.function_name
+
+                    return file_dict
 
                 json_dict["custom_files"] = [make_custom_file_dict(file) for file in attr]
 
@@ -1062,7 +1066,9 @@ def try_relative_to(path: Path, relative_to: Path) -> str:
     """
     path = Path(path)
     relative_to = Path(relative_to)
-    if path.is_relative_to(relative_to):
+    if not path.is_absolute():
+        return str(path)
+    elif path.is_relative_to(relative_to):
         return str(path.relative_to(relative_to))
     else:
         warnings.warn(
diff --git a/tests/conftest.py b/tests/conftest.py
index b717ec4e..0f3b3569 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -8537,3 +8537,11 @@ def absorption():
     """The project from the absorption example."""
     project, _ = ratapi.examples.absorption()
     return project
+
+
+@pytest.fixture
+def absorption_different_function():
+    """The project from the absorption example with a function name different from filename."""
+    project, _ = ratapi.examples.absorption()
+    project.custom_files[0].function_name = "test_func"
+    return project
diff --git a/tests/test_models.py b/tests/test_models.py
index 906a5d67..949cf5aa 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -1,6 +1,5 @@
 """Test the pydantic models."""
 
-import pathlib
 import re
 from collections.abc import Callable
 
@@ -101,11 +100,11 @@ def test_initialise_with_extra_fields(self, model: Callable, model_params: dict)
             model(new_field=1, **model_params)
 
 
-def test_custom_file_path_is_absolute() -> None:
-    """If we use provide a relative path to the custom file model, it should be converted to an absolute path."""
-    relative_path = pathlib.Path("./relative_path")
-    custom_file = ratapi.models.CustomFile(path=relative_path)
-    assert custom_file.path.is_absolute()
+# def test_custom_file_path_is_absolute() -> None:
+#     """If we use provide a relative path to the custom file model, it should be converted to an absolute path."""
+#     relative_path = pathlib.Path("./relative_path")
+#     custom_file = ratapi.models.CustomFile(path=relative_path)
+#     assert custom_file.path.is_absolute()
 
 
 def test_data_eq() -> None:
diff --git a/tests/test_project.py b/tests/test_project.py
index 31e13760..35d5864e 100644
--- a/tests/test_project.py
+++ b/tests/test_project.py
@@ -1616,6 +1616,7 @@ def test_wrap_extend(test_project, class_list: str, model_type: str, field: str,
         "domains_custom_layers",
         "domains_custom_xy",
         "absorption",
+        "absorption_different_function",
     ],
 )
 def test_save_load(project, request):
@@ -1637,24 +1638,25 @@ def test_save_load(project, request):
 def test_relative_paths():
     """Test that ``try_relative_to`` correctly creates relative paths to subfolders."""
 
-    with tempfile.TemporaryDirectory() as tmp:
-        data_path = Path(tmp, "data/myfile.dat")
+    cur_path = Path(".").resolve()
+    data_path = cur_path / "data/myfile.dat"
+    assert Path(ratapi.project.try_relative_to(data_path, cur_path)) == Path("data/myfile.dat")
 
-        assert Path(ratapi.project.try_relative_to(data_path, tmp)) == Path("data/myfile.dat")
+    # relative path will be left relative.
+    data_path = "data/myfile.dat"
+    assert Path(ratapi.project.try_relative_to(data_path, cur_path)) == Path("data/myfile.dat")
 
 
 def test_relative_paths_warning():
     """Test that we get a warning for trying to walk up paths."""
 
-    data_path = "/tmp/project/data/mydata.dat"
-    relative_path = "/tmp/project/project_path/myproj.dat"
+    cur_path = Path(".").resolve()
+    data_path = cur_path / "tmp/project/data/mydata.dat"
+    relative_path = cur_path / "tmp/project/project_path/myproj.dat"
 
     with pytest.warns(
         match="Could not write custom file path as relative to the project directory, "
         "which means that it may not work on other devices. If you would like to share your project, "
         "make sure your custom files are in a subfolder of the project save location.",
     ):
-        assert (
-            Path(ratapi.project.try_relative_to(data_path, relative_path))
-            == Path("/tmp/project/data/mydata.dat").resolve()
-        )
+        assert Path(ratapi.project.try_relative_to(data_path, relative_path)) == data_path

From 3d12df45aa2cbab1af063831ecf5b587df608a98 Mon Sep 17 00:00:00 2001
From: StephenNneji <34302892+StephenNneji@users.noreply.github.com>
Date: Thu, 19 Feb 2026 13:36:07 +0000
Subject: [PATCH 4/9] Fixes auto name bug, adds clearer error for missing
 custom file or function name and update to dev12 (#199)

---
 cpp/RAT              |  2 +-
 cpp/rat.cpp          |  4 +--
 pyproject.toml       |  2 +-
 ratapi/inputs.py     |  7 ++++
 ratapi/models.py     | 84 ++++++++++++++++++++++++++++++++++----------
 tests/test_inputs.py | 26 ++++++++++++++
 tests/test_models.py | 22 ++++++------
 7 files changed, 114 insertions(+), 33 deletions(-)

diff --git a/cpp/RAT b/cpp/RAT
index 7da24c8d..a5d4d80b 160000
--- a/cpp/RAT
+++ b/cpp/RAT
@@ -1 +1 @@
-Subproject commit 7da24c8d9700bff1eb5c660f5389214c473a1c24
+Subproject commit a5d4d80ba63185846db678c3ebd9a85bdd82921d
diff --git a/cpp/rat.cpp b/cpp/rat.cpp
index 31bbe144..957ba4fa 100644
--- a/cpp/rat.cpp
+++ b/cpp/rat.cpp
@@ -525,8 +525,8 @@ OutputBayesResult OutputBayesResultsFromStruct(const RAT::BayesResults results)
     bayesResults.confidenceIntervals.percentile65 = pyArrayFromRatArray2d(results.confidenceIntervals.percentile65);
     bayesResults.confidenceIntervals.mean = pyArrayFromRatArray2d(results.confidenceIntervals.mean);
 
-    bayesResults.nestedSamplerOutput.logZ = results.nestedSamplerOutput.LogZ;
-    bayesResults.nestedSamplerOutput.logZErr = results.nestedSamplerOutput.LogZErr;
+    bayesResults.nestedSamplerOutput.logZ = results.nestedSamplerOutput.logZ;
+    bayesResults.nestedSamplerOutput.logZErr = results.nestedSamplerOutput.logZErr;
     bayesResults.nestedSamplerOutput.nestSamples = pyArrayFromRatArray2d(results.nestedSamplerOutput.nestSamples);
     bayesResults.nestedSamplerOutput.postSamples = pyArrayFromRatArray2d(results.nestedSamplerOutput.postSamples);
 
diff --git a/pyproject.toml b/pyproject.toml
index 85d6e161..c68585c3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ build-backend = 'setuptools.build_meta'
 
 [project]
 name = "ratapi"
-version = "0.0.0.dev11"
+version = "0.0.0.dev12"
 description = "Python extension for the Reflectivity Analysis Toolbox (RAT)"
 readme = "README.md"
 requires-python = ">=3.10"
diff --git a/ratapi/inputs.py b/ratapi/inputs.py
index 921890b5..bdfaddad 100644
--- a/ratapi/inputs.py
+++ b/ratapi/inputs.py
@@ -77,6 +77,13 @@ def get_handle(self, index: int):
         """
         custom_file = self.files[index]
         full_path = os.path.join(custom_file["path"], custom_file["filename"])
+
+        if not os.path.isfile(full_path):
+            raise FileNotFoundError(f"The custom file ({custom_file['name']}) does not have a valid path.")
+
+        if not custom_file["function_name"] and custom_file["language"] != Languages.Matlab:
+            raise ValueError(f"The custom file ({custom_file['name']}) does not have a valid function name.")
+
         if custom_file["language"] == Languages.Python:
             file_handle = get_python_handle(custom_file["filename"], custom_file["function_name"], custom_file["path"])
         elif custom_file["language"] == Languages.Matlab:
diff --git a/ratapi/models.py b/ratapi/models.py
index 755ea974..ba0a1e99 100644
--- a/ratapi/models.py
+++ b/ratapi/models.py
@@ -2,7 +2,7 @@
 
 import pathlib
 import warnings
-from itertools import count
+from contextlib import suppress
 from typing import Any
 
 import numpy as np
@@ -18,14 +18,41 @@
 
 
 # Create a counter for each model
-background_number = count(1)
-contrast_number = count(1)
-custom_file_number = count(1)
-data_number = count(1)
-domain_contrast_number = count(1)
-layer_number = count(1)
-parameter_number = count(1)
-resolution_number = count(1)
+background_number = ["Background", 0]
+contrast_number = ["Contrast", 0]
+custom_file_number = ["Custom File", 0]
+data_number = ["Data", 0]
+domain_contrast_number = ["Domain Contrast", 0]
+layer_number = ["Layer", 0]
+parameter_number = ["Parameter", 0]
+resolution_number = ["Resolution", 0]
+
+_model_counter = {
+    "Background": background_number,
+    "Contrast": contrast_number,
+    "ContrastWithRatio": contrast_number,
+    "CustomFile": custom_file_number,
+    "Data": data_number,
+    "DomainContrast": domain_contrast_number,
+    "Layer": layer_number,
+    "AbsorptionLayer": layer_number,
+    "Parameter": parameter_number,
+    "ProtectedParameter": parameter_number,
+    "Resolution": resolution_number,
+}
+
+
+def _model_name_factory(model_name: str) -> str:
+    """Generate a unique name for model using a global counter.
+
+    Parameters
+    ----------
+    model_name : str
+        The name of the model class.
+    """
+    title, number = _model_counter[model_name]
+    _model_counter[model_name][1] += 1
+    return f"New {title} {(number + 1)}"
 
 
 class RATModel(BaseModel, validate_assignment=True, extra="forbid"):
@@ -38,6 +65,25 @@ def __repr__(self):
         )
         return f"{self.__repr_name__()}({fields_repr})"
 
+    @field_validator("name", mode="after", check_fields=False)
+    @classmethod
+    def update_counter(cls, name: str) -> str:
+        """Update the auto name counter if a similar name is manually given.
+
+        Parameters
+        ----------
+        name : str
+            The name of the model.
+        """
+        title, number = _model_counter[cls.__name__]
+        prefix = f"New {title} "
+        if name.startswith(prefix):
+            with suppress(ValueError):
+                new_number = int(name[len(prefix) :])
+                if new_number > number:
+                    _model_counter[cls.__name__][1] = new_number
+        return name
+
     def __str__(self):
         table = prettytable.PrettyTable()
         table.field_names = [key.replace("_", " ") for key in self.display_fields]
@@ -116,7 +162,7 @@ class Background(Signal):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Background {next(background_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("Background"), min_length=1)
 
     @model_validator(mode="after")
     def check_unsupported_parameters(self):
@@ -173,7 +219,7 @@ class Contrast(RATModel):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Contrast {next(contrast_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("Contrast"), min_length=1)
     data: str = ""
     background: str = ""
     background_action: BackgroundActions = BackgroundActions.Add
@@ -255,7 +301,7 @@ class ContrastWithRatio(RATModel):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Contrast {next(contrast_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("ContrastWithRatio"), min_length=1)
     data: str = ""
     background: str = ""
     background_action: BackgroundActions = BackgroundActions.Add
@@ -309,7 +355,7 @@ class CustomFile(RATModel):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Custom File {next(custom_file_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("CustomFile"), min_length=1)
     filename: str = ""
     function_name: str = ""
     language: Languages = Languages.Python
@@ -348,7 +394,7 @@ class Data(RATModel, arbitrary_types_allowed=True):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Data {next(data_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("Data"), min_length=1)
     data: np.ndarray = np.empty([0, 3])
     data_range: list[float] = Field(default=[], min_length=2, max_length=2)
     simulation_range: list[float] = Field(default=[], min_length=2, max_length=2)
@@ -453,7 +499,7 @@ class DomainContrast(RATModel):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Domain Contrast {next(domain_contrast_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("DomainContrast"), min_length=1)
     model: list[str] = []
 
     def __str__(self):
@@ -483,7 +529,7 @@ class Layer(RATModel, populate_by_name=True):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Layer {next(layer_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("Layer"), min_length=1)
     thickness: str
     SLD: str = Field(validation_alias="SLD_real")
     roughness: str
@@ -522,7 +568,7 @@ class AbsorptionLayer(RATModel, populate_by_name=True):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Layer {next(layer_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("AbsorptionLayer"), min_length=1)
     thickness: str
     SLD_real: str = Field(validation_alias="SLD")
     SLD_imaginary: str
@@ -555,7 +601,7 @@ class Parameter(RATModel):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Parameter {next(parameter_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("Parameter"), min_length=1)
     min: float = 0.0
     value: float = 0.0
     max: float = 0.0
@@ -638,7 +684,7 @@ class Resolution(Signal):
 
     """
 
-    name: str = Field(default_factory=lambda: f"New Resolution {next(resolution_number)}", min_length=1)
+    name: str = Field(default_factory=lambda: _model_name_factory("Resolution"), min_length=1)
 
     @field_validator("type")
     @classmethod
diff --git a/tests/test_inputs.py b/tests/test_inputs.py
index 244fd4d2..242d7480 100644
--- a/tests/test_inputs.py
+++ b/tests/test_inputs.py
@@ -2,6 +2,8 @@
 
 import pathlib
 import pickle
+import tempfile
+from unittest.mock import patch
 
 import numpy as np
 import pytest
@@ -675,6 +677,30 @@ def test_make_controls(standard_layers_controls) -> None:
     check_controls_equal(controls, standard_layers_controls)
 
 
+@patch("ratapi.wrappers.MatlabWrapper")
+def test_file_handles(wrapper):
+    handle = FileHandles([ratapi.models.CustomFile(name="Test Custom File", filename="cpp_test.dll", language="cpp")])
+
+    with pytest.raises(FileNotFoundError, match="The custom file \\(Test Custom File\\) does not have a valid path."):
+        handle.get_handle(0)
+
+    with tempfile.NamedTemporaryFile("w", suffix=".dll") as f:
+        tmp_file = pathlib.Path(f.name)
+        handle.files[0]["path"] = tmp_file.parent
+        handle.files[0]["filename"] = tmp_file.name
+        handle.files[0]["function_name"] = ""
+        # No function name should throw exception
+        with pytest.raises(
+            ValueError, match="The custom file \\(Test Custom File\\) does not have a valid function name."
+        ):
+            handle.get_handle(0)
+
+        # Matlab does not need function name
+        handle.files[0]["language"] = "matlab"
+        handle.get_handle(0)
+        wrapper.assert_called()
+
+
 def check_problem_equal(actual_problem, expected_problem) -> None:
     """Compare two instances of the "problem" object for equality."""
     scalar_fields = [
diff --git a/tests/test_models.py b/tests/test_models.py
index 949cf5aa..4343b442 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -32,14 +32,23 @@ def test_default_names(model: Callable, model_name: str, model_params: dict) ->
     format: "New <model name> <integer>".
     """
     model_1 = model(**model_params)
+    prefix = f"New {model_name} "
+    assert model_1.name.startswith(prefix)
+    index = int(model_1.name[len(prefix) :])
+
     model_2 = model(**model_params)
     model_3 = model(name="Given Name", **model_params)
     model_4 = model(**model_params)
 
-    assert model_1.name == f"New {model_name} 1"
-    assert model_2.name == f"New {model_name} 2"
+    assert model_1.name == f"New {model_name} {index}"
+    assert model_2.name == f"New {model_name} {index + 1}"
     assert model_3.name == "Given Name"
-    assert model_4.name == f"New {model_name} 3"
+    assert model_4.name == f"New {model_name} {index + 2}"
+
+    # If user adds name in similar format. The next auto number will take it into account.
+    model(name=f"{prefix}{index + 20}", **model_params)
+    model_5 = model(**model_params)
+    assert model_5.name == f"New {model_name} {index + 21}"
 
 
 @pytest.mark.parametrize(
@@ -100,13 +109,6 @@ def test_initialise_with_extra_fields(self, model: Callable, model_params: dict)
             model(new_field=1, **model_params)
 
 
-# def test_custom_file_path_is_absolute() -> None:
-#     """If we use provide a relative path to the custom file model, it should be converted to an absolute path."""
-#     relative_path = pathlib.Path("./relative_path")
-#     custom_file = ratapi.models.CustomFile(path=relative_path)
-#     assert custom_file.path.is_absolute()
-
-
 def test_data_eq() -> None:
     """If we use the Data.__eq__ method with an object that is not a pydantic BaseModel, we should return
     "NotImplemented".

From 18862ca3332b550fae1fd2845c7b1b1b7b2ebb93 Mon Sep 17 00:00:00 2001
From: StephenNneji <34302892+StephenNneji@users.noreply.github.com>
Date: Mon, 9 Mar 2026 11:40:21 +0000
Subject: [PATCH 5/9] Change pybind build version, blit plot bug and update to
 dev13 (#200)

---
 pyproject.toml           | 4 ++--
 ratapi/utils/plotting.py | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index c68585c3..09796c44 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,13 +2,13 @@
 requires = [
     'setuptools>=61',
     'wheel',
-    'pybind11>=2.4',
+    'pybind11>=2.4, <=2.13.6',
 ]
 build-backend = 'setuptools.build_meta'
 
 [project]
 name = "ratapi"
-version = "0.0.0.dev12"
+version = "0.0.0.dev13"
 description = "Python extension for the Reflectivity Analysis Toolbox (RAT)"
 readme = "README.md"
 requires-python = ">=3.10"
diff --git a/ratapi/utils/plotting.py b/ratapi/utils/plotting.py
index dc1ade53..8225bd43 100644
--- a/ratapi/utils/plotting.py
+++ b/ratapi/utils/plotting.py
@@ -505,6 +505,7 @@ def update_plot(self, data):
             show_error_bar=self.show_error_bar,
             show_grid=self.show_grid,
             show_legend=self.show_legend,
+            shift_value=self.shift_value,
             animated=True,
         )
         self.figure.canvas.draw()

From 072c9325e1a63b3fca4625231abbda6749cba28d Mon Sep 17 00:00:00 2001
From: StephenNneji <34302892+StephenNneji@users.noreply.github.com>
Date: Wed, 11 Mar 2026 09:27:43 +0000
Subject: [PATCH 6/9] Use latest version of cibuildwheel (#201)

---
 .github/workflows/build_wheel.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml
index 03176b7b..688118ed 100644
--- a/.github/workflows/build_wheel.yml
+++ b/.github/workflows/build_wheel.yml
@@ -67,7 +67,7 @@ jobs:
           export PATH="$pythonLocation:$PATH"
           CIBW_TEST_COMMAND='cd ${pwd}/tmp && python -m pytest tests'
           echo "CIBW_TEST_COMMAND=${CIBW_TEST_COMMAND}" >> $GITHUB_ENV
-          python -m pip install cibuildwheel==2.23.3
+          python -m pip install cibuildwheel
           python -m cibuildwheel --output-dir ./wheelhouse 
       - uses: actions/upload-artifact@v4
         with:

From 06926937c54b6e8ecb5c0b44089ca5e45f045d2d Mon Sep 17 00:00:00 2001
From: StephenNneji <34302892+StephenNneji@users.noreply.github.com>
Date: Tue, 24 Mar 2026 09:40:56 +0000
Subject: [PATCH 7/9] Fix shift value for confidence interval (#202)

---
 ratapi/utils/plotting.py | 16 ++++++++--------
 tests/test_plotting.py   |  6 +++---
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/ratapi/utils/plotting.py b/ratapi/utils/plotting.py
index 8225bd43..6f9debb7 100644
--- a/ratapi/utils/plotting.py
+++ b/ratapi/utils/plotting.py
@@ -36,7 +36,7 @@ def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool
     show_error_bar : bool, default: True
         Controls whether the error bars are shown
     shift_value : float
-        A value between 1 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
+        A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
 
     Returns
     -------
@@ -46,14 +46,14 @@ def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool
     """
     results = {"ref": [], "error": [], "sld": [], "sld_resample": []}
 
-    if shift_value < 1 or shift_value > 100:
-        raise ValueError("Parameter `shift_value` must be between 1 and 100")
+    if shift_value < 0 or shift_value > 100:
+        raise ValueError("Parameter `shift_value` must be between 0 and 100")
 
     for i, (r, data, sld) in enumerate(
         zip(event_data.reflectivity, event_data.shiftedData, event_data.sldProfiles, strict=False)
     ):
         # Calculate the divisor
-        div = 1 if i == 0 and not q4 else 10 ** ((i + 1) / 100 * shift_value)
+        div = 10 ** (i / 100 * shift_value)
         q4_data = 1 if not q4 or not event_data.dataPresent[i] else data[:, 0] ** 4
         mult = q4_data / div
 
@@ -137,7 +137,7 @@ def plot_ref_sld_helper(
     show_legend : bool, default: True
         Controls whether the legend is shown
     shift_value : float, default: 100
-        A value between 1 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
+        A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
     animated : bool, default: False
         Controls whether the animated property of foreground plot elements should be set.
 
@@ -168,7 +168,7 @@ def plot_ref_sld_helper(
         # Plot confidence intervals if required
         if confidence_intervals is not None:
             # Calculate the divisor
-            div = 1 if i == 0 and not q4 else 10 ** ((i / 100) * shift_value)
+            div = 10 ** (i / 100 * shift_value)
             ref_min, ref_max = confidence_intervals["reflectivity"][i]
             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")
@@ -277,7 +277,7 @@ def plot_ref_sld(
     show_legend : bool, default: True
             Controls whether the legend is shown
     shift_value : float, default: 100
-        A value between 1 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
+        A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
 
     Returns
     -------
@@ -387,7 +387,7 @@ class BlittingSupport:
     show_legend : bool, default: True
         Controls whether the legend is shown
     shift_value : float, default: 100
-        A value between 1 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
+        A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts
     """
 
     def __init__(
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index 710729a5..31049a28 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -490,8 +490,8 @@ def test_extract_plot_data(data) -> None:
     assert len(plot_data["ref"]) == len(data.reflectivity)
     assert len(plot_data["sld"]) == len(data.shiftedData)
 
-    with pytest.raises(ValueError, match=r"Parameter `shift_value` must be between 1 and 100"):
-        RATplot._extract_plot_data(data, False, True, 0)
+    with pytest.raises(ValueError, match=r"Parameter `shift_value` must be between 0 and 100"):
+        RATplot._extract_plot_data(data, False, True, -0.1)
 
-    with pytest.raises(ValueError, match=r"Parameter `shift_value` must be between 1 and 100"):
+    with pytest.raises(ValueError, match=r"Parameter `shift_value` must be between 0 and 100"):
         RATplot._extract_plot_data(data, False, True, 100.5)

From f40513507428d3ff3bc0f1086531c472a52a044f Mon Sep 17 00:00:00 2001
From: StephenNneji <34302892+StephenNneji@users.noreply.github.com>
Date: Wed, 1 Apr 2026 12:41:47 +0100
Subject: [PATCH 8/9] Update to dev14 (#203)

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 09796c44..ab6724ae 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ build-backend = 'setuptools.build_meta'
 
 [project]
 name = "ratapi"
-version = "0.0.0.dev13"
+version = "0.0.0.dev14"
 description = "Python extension for the Reflectivity Analysis Toolbox (RAT)"
 readme = "README.md"
 requires-python = ">=3.10"

From b0f51a91ec3bc40f0e5ccd518f07b4ce946881fd Mon Sep 17 00:00:00 2001
From: StephenNneji <34302892+StephenNneji@users.noreply.github.com>
Date: Thu, 23 Apr 2026 10:13:01 +0100
Subject: [PATCH 9/9] Handle R1 contrast not being unique (#204)

---
 ratapi/utils/convert.py               |   8 +++++++-
 tests/test_convert.py                 |   7 +++++++
 tests/test_data/nonUniqueContrast.mat | Bin 0 -> 17500 bytes
 3 files changed, 14 insertions(+), 1 deletion(-)
 create mode 100644 tests/test_data/nonUniqueContrast.mat

diff --git a/ratapi/utils/convert.py b/ratapi/utils/convert.py
index b957789c..7b3a44f7 100644
--- a/ratapi/utils/convert.py
+++ b/ratapi/utils/convert.py
@@ -5,7 +5,7 @@
 from os import PathLike
 from pathlib import Path
 
-from numpy import array, empty
+from numpy import array, empty, ndarray
 from scipy.io.matlab import MatlabOpaque, loadmat
 
 from ratapi import Project, wrappers
@@ -227,6 +227,12 @@ def fix_invalid_constraints(name: str, constrs: tuple[float, float], value: floa
     if isinstance(mat_project["resolNames"], str):
         mat_project["resolNames"] = [mat_project["resolNames"]]
 
+    if isinstance(mat_project["contrastNames"], (ndarray, list)) and len(
+        dict.fromkeys(mat_project["contrastNames"])
+    ) != len(mat_project["contrastNames"]):
+        # contrast names are not unique so create unique ones
+        mat_project["contrastNames"] = [f"Contrast {i + 1}" for i in range(len(mat_project["contrastNames"]))]
+
     contrasts = ClassList(
         [
             Contrast(
diff --git a/tests/test_convert.py b/tests/test_convert.py
index fc6f4400..0cbd621b 100644
--- a/tests/test_convert.py
+++ b/tests/test_convert.py
@@ -69,6 +69,13 @@ def test_r1_to_project(file, project, path_type, request):
         assert getattr(output_project, class_list) == getattr(expected_project, class_list)
 
 
+def test_r1_with_non_unique_contrast_names():
+    """Test that R1 to Project class conversion returns the expected Project."""
+    output_project = r1_to_project(pathlib.Path(TEST_DIR_PATH, "nonUniqueContrast.mat"))
+    assert output_project.contrasts[0].name == "Contrast 1"
+    assert output_project.contrasts[1].name == "Contrast 2"
+
+
 @pytest.mark.parametrize(
     "project",
     [
diff --git a/tests/test_data/nonUniqueContrast.mat b/tests/test_data/nonUniqueContrast.mat
new file mode 100644
index 0000000000000000000000000000000000000000..4137513434671e945b287102fbdfa7df00cbd150
GIT binary patch
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