diff --git a/.ci/BHoMBot/Nuget/BHoM.Interop.LadybugTools.nuspec b/.ci/BHoMBot/Nuget/BHoM.Interop.LadybugTools.nuspec
new file mode 100644
index 00000000..a3f178da
--- /dev/null
+++ b/.ci/BHoMBot/Nuget/BHoM.Interop.LadybugTools.nuspec
@@ -0,0 +1,28 @@
+<?xml version="1.0" encoding="utf-8"?>
+<package>
+  <metadata>
+    <id>BHoM.Interop.LadybugTools</id>
+    <version></version>
+    <authors>BHoM</authors>
+    <projectUrl>https://github.com/BHoM/LadybugTools_Toolkit</projectUrl>
+    <license type="file">licence.txt</license>
+    <icon>icon.png</icon>
+    <readme>readme.md</readme>
+    <requireLicenseAcceptance>false</requireLicenseAcceptance>
+    <description>Interact with Ladybug Tools via BHoM</description>
+    <releaseNotes></releaseNotes>
+    <copyright></copyright>
+    <tags>BHoM engine aec LadyBug LadyBugTools</tags>
+    <title></title>
+    <dependencies>
+      <group targetFramework="netstandard2.0">
+        <dependency id="NETStandard.Library" version="2.0.3" />
+      </group>
+    </dependencies>
+  </metadata>
+  <files>
+    <file src="licence/licence.txt" target="" />
+    <file src="images/icon.png" target="" />
+    <file src="docs/readme.md" target="" />
+  </files>
+</package>
\ No newline at end of file
diff --git a/LadybugTools_Adapter/AdapterActions/Execute.cs b/LadybugTools_Adapter/AdapterActions/Execute.cs
new file mode 100644
index 00000000..f3299eba
--- /dev/null
+++ b/LadybugTools_Adapter/AdapterActions/Execute.cs
@@ -0,0 +1,263 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.Engine.Adapter;
+using BH.Engine.LadybugTools;
+using BH.oM.Adapter;
+using BH.oM.Adapter.Commands;
+using BH.oM.Base;
+using BH.oM.Data.Requests;
+using BH.oM.LadybugTools;
+using BH.oM.Python;
+using BH.Engine.Python;
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public partial class LadybugToolsAdapter : BHoMAdapter
+    {
+        bool m_executeSuccess = false;
+        public override Output<List<object>, bool> Execute(IExecuteCommand command, ActionConfig actionConfig = null)
+        {
+            m_executeSuccess = false;
+            Output<List<object>, bool> output = new Output<List<object>, bool>() { Item1 = new List<object>(), Item2 = false };
+
+            List<object> temp = IRunCommand(command);
+
+            output.Item1 = temp;
+            output.Item2 = m_executeSuccess;
+
+            return output;
+        }
+
+        /**************************************************/
+        /* Public methods - Interface                     */
+        /**************************************************/
+
+        public List<object> IRunCommand(IExecuteCommand command)
+        {
+            if (command == null)
+            {
+                BH.Engine.Base.Compute.RecordError("Please input a valid Ladybug Command to execute.");
+                return new List<object>();
+            }
+
+            return RunCommand(command as dynamic);
+        }
+
+        /**************************************************/
+        /* Private methods - Run Ladybug Command          */
+        /**************************************************/
+
+        private List<object> RunCommand(GetMaterialCommand command)
+        {
+            PythonEnvironment env = Engine.LadybugTools.Compute.InstallPythonEnv_LBT(true);
+            LadybugConfig config = new LadybugConfig()
+            {
+                JsonFile = new FileSettings()
+                {
+                    FileName = $"LBTBHoM_Materials_{DateTime.Now:yyyyMMdd}.json",
+                    Directory = Path.GetTempPath()
+                }
+            };
+
+            if (!File.Exists(config.JsonFile.GetFullFileName()))
+            {
+                string script = Path.Combine(Engine.Python.Query.DirectoryCode(), "LadybugTools_Toolkit\\src\\ladybugtools_toolkit\\bhom\\wrapped", "get_material.py");
+
+                string cmdCommand = $"{env.Executable} {script} -j \"{config.JsonFile.GetFullFileName()}\"";
+
+                Engine.Python.Compute.RunCommandStdout(command: cmdCommand, hideWindows: true);
+            }
+
+            List<object> materialObjects = Pull(new FilterRequest(), actionConfig: config).ToList();
+
+            m_executeSuccess = true;
+            return materialObjects.Where(m => (m as IEnergyMaterialOpaque).Name.Contains(command.Filter)).ToList();
+        }
+
+        /**************************************************/
+
+        private List<object> RunCommand(GetTypologyCommand command)
+        {
+            PythonEnvironment env = Engine.LadybugTools.Compute.InstallPythonEnv_LBT(true);
+            LadybugConfig config = new LadybugConfig()
+            {
+                JsonFile = new FileSettings()
+                {
+                    FileName = $"LBTBHoM_Typologies_{DateTime.Now:yyyyMMdd}.json",
+                    Directory = Path.GetTempPath()
+                }
+            };
+
+            if (!File.Exists(config.JsonFile.GetFullFileName()))
+            {
+                string script = Path.Combine(Engine.Python.Query.DirectoryCode(), "LadybugTools_Toolkit\\src\\ladybugtools_toolkit\\bhom\\wrapped", "get_typology.py");
+
+                string cmdCommand = $"{env.Executable} {script} -j \"{config.JsonFile.GetFullFileName()}\"";
+
+                Engine.Python.Compute.RunCommandStdout(command: cmdCommand, hideWindows: true);
+            }
+
+            List<object> typologyObjects = Pull(new FilterRequest(), actionConfig: config).ToList();
+
+            m_executeSuccess = true;
+            return typologyObjects.Where(m => (m as Typology).Name.Contains(command.Filter)).ToList();
+        }
+
+        /**************************************************/
+
+        private List<object> RunCommand(RunSimulationCommand command)
+        {
+            // validation prior to passing to Python
+            if (command.EpwFile == null)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(command.EpwFile)} input cannot be null.");
+                return null;
+            }
+
+            if (!File.Exists(command.EpwFile.GetFullFileName()))
+            {
+                BH.Engine.Base.Compute.RecordError($"{command.EpwFile.GetFullFileName()} does not exist.");
+                return null;
+            }
+
+            if (command.GroundMaterial == null)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(command.GroundMaterial)} input cannot be null.");
+                return null;
+            }
+
+            if (command.ShadeMaterial == null)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(command.ShadeMaterial)} input cannot be null.");
+                return null;
+            }
+
+            // construct adapter and config
+            LadybugConfig config = new LadybugConfig()
+            {
+                JsonFile = new FileSettings()
+                {
+                    FileName = $"LBTBHoM_{Guid.NewGuid()}.json",
+                    Directory = Path.GetTempPath()
+                }
+            };
+
+            // construct the base object and file to be passed to Python for simulation
+            SimulationResult simulationResult = new SimulationResult()
+            {
+                EpwFile = Path.GetFullPath(command.EpwFile.GetFullFileName()).Replace(@"\", "/"),
+                GroundMaterial = command.GroundMaterial,
+                ShadeMaterial = command.ShadeMaterial,
+                Name = Engine.LadybugTools.Compute.SimulationID(command.EpwFile.GetFullFileName(), command.GroundMaterial, command.ShadeMaterial)
+            };
+
+            // push object to json file
+            Push(new List<SimulationResult>() { simulationResult }, actionConfig: config);
+
+            // locate the Python executable and file containing the simulation code
+            PythonEnvironment env = Engine.LadybugTools.Compute.InstallPythonEnv_LBT(true);
+            string script = Path.Combine(Engine.Python.Query.DirectoryCode(), "LadybugTools_Toolkit\\src\\ladybugtools_toolkit\\bhom\\wrapped", "simulation_result.py");
+
+            // run the simulation
+            string cmdCommand = $"{env.Executable} {script} -j \"{config.JsonFile.GetFullFileName()}\"";
+            Engine.Python.Compute.RunCommandStdout(command: cmdCommand, hideWindows: true);
+
+            // reload from Python results
+            List<object> simulationResultPopulated = Pull(new FilterRequest(), actionConfig: config).ToList();
+
+            // remove temporary file
+            File.Delete(config.JsonFile.GetFullFileName());
+
+            m_executeSuccess = true;
+            return simulationResultPopulated;
+        }
+
+        /**************************************************/
+
+        private List<object> RunCommand(RunExternalComfortCommand command)
+        {
+            if (command.SimulationResult == null)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(command.SimulationResult)} input cannot be null.");
+                return null;
+            }
+
+            if (command.Typology == null)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(command.Typology)} input cannot be null.");
+                return null;
+            }
+
+            LadybugConfig config = new LadybugConfig()
+            {
+                JsonFile = new FileSettings()
+                {
+                    FileName = $"LBTBHoM_{Guid.NewGuid()}.json",
+                    Directory = Path.GetTempPath()
+                }
+            };
+
+            // construct the base object
+            ExternalComfort externalComfort = new ExternalComfort()
+            {
+                SimulationResult = command.SimulationResult,
+                Typology = command.Typology,
+            };
+
+            // push objects to json file
+            Push(new List<ExternalComfort>() { externalComfort }, actionConfig: config);
+
+            // locate the Python executable and file containing the simulation code
+            PythonEnvironment env = Engine.LadybugTools.Compute.InstallPythonEnv_LBT(true);
+            string script = Path.Combine(Engine.Python.Query.DirectoryCode(), "LadybugTools_Toolkit\\src\\ladybugtools_toolkit\\bhom\\wrapped", "external_comfort.py");
+
+            // run the calculation
+            string cmdCommand = $"{env.Executable} {script} -j \"{config.JsonFile.GetFullFileName()}\"";
+            Engine.Python.Compute.RunCommandStdout(command: cmdCommand, hideWindows: true);
+
+            // reload from Python results
+            List<object> externalComfortPopulated = Pull(new FilterRequest(), actionConfig: config).ToList();
+
+            // remove temporary file
+            File.Delete(config.JsonFile.GetFullFileName());
+
+            m_executeSuccess = true;
+            return externalComfortPopulated;
+        }
+
+        /**************************************************/
+        /* Private methods - Fallback                     */
+        /**************************************************/
+
+        private List<object> RunCommand(IExecuteCommand command)
+        {
+            BH.Engine.Base.Compute.RecordError($"The command {command.GetType().FullName} is not valid for the LadybugTools Adapter. Please use a LadybugCommand, or use the correct adapter for the input command.");
+            return new List<object>();
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/AdapterActions/Pull.cs b/LadybugTools_Adapter/AdapterActions/Pull.cs
new file mode 100644
index 00000000..12ed9b8f
--- /dev/null
+++ b/LadybugTools_Adapter/AdapterActions/Pull.cs
@@ -0,0 +1,72 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.Engine.Adapter;
+using BH.oM.Adapter;
+using BH.oM.Base;
+using BH.oM.Data.Requests;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public partial class LadybugToolsAdapter : BHoMAdapter
+    {
+        public override IEnumerable<object> Pull(IRequest request, PullType pullType = PullType.AdapterDefault, ActionConfig actionConfig = null) 
+        {
+            if (actionConfig == null)
+            {
+                BH.Engine.Base.Compute.RecordError("Please provide a valid LadybugConfig ActionConfig.");
+                return new List<IBHoMObject>();
+            }
+
+            LadybugConfig config = actionConfig as LadybugConfig;
+            if (config == null)
+            {
+                BH.Engine.Base.Compute.RecordError($"The type of actionConfig provided: {actionConfig.GetType().FullName} is not valid for this adapter. Please provide a valid LadybugConfig actionConfig.");
+                return new List<IBHoMObject>();
+            }
+
+            if (config.JsonFile == null)
+            {
+                BH.Engine.Base.Compute.RecordError("Please provide a valid JsonFile FileSettings object.");
+                return new List<IBHoMObject>();
+            }
+
+            if (!System.IO.File.Exists(config.JsonFile.GetFullFileName()))
+            {
+                BH.Engine.Base.Compute.RecordError($"The file at {config.JsonFile.GetFullFileName()} does not exist to pull from.");
+                return new List<IBHoMObject>();
+            }
+
+            if (request != null)
+            {
+                FilterRequest filterRequest = request as FilterRequest;
+                return Read(filterRequest.Type, actionConfig: config);
+            }
+            else
+                return Read(null, config);
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/AdapterActions/Push.cs b/LadybugTools_Adapter/AdapterActions/Push.cs
new file mode 100644
index 00000000..f1cb6d15
--- /dev/null
+++ b/LadybugTools_Adapter/AdapterActions/Push.cs
@@ -0,0 +1,70 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.Engine.Adapter;
+using BH.Engine.LadybugTools;
+using BH.oM.Adapter;
+using BH.oM.Base;
+using BH.oM.Data.Requests;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public partial class LadybugToolsAdapter : BHoMAdapter
+    {
+        public override List<object> Push(IEnumerable<object> objects, string tag = "", PushType pushType = PushType.AdapterDefault, ActionConfig actionConfig = null)
+        {
+            if (actionConfig == null)
+            {
+                BH.Engine.Base.Compute.RecordError("Please provide a valid LadybugConfig ActionConfig.");
+                return new List<object>();
+            }
+
+            LadybugConfig config = actionConfig as LadybugConfig;
+            if (config == null)
+            {
+                BH.Engine.Base.Compute.RecordError("Please input a valid LadybugConfig.");
+                return new List<object>();
+            }
+
+            if (objects.Count() == 0)
+            {
+                BH.Engine.Base.Compute.RecordError("Please input a valid LadybugTools object.");
+                return new List<object>();
+            }
+
+            List<ILadybugTools> lbtObjects = objects.Where(x => typeof(ILadybugTools).IsAssignableFrom(x.GetType())).Cast<ILadybugTools>().ToList();
+
+            if (lbtObjects.Count() < objects.Count())
+            {
+                BH.Engine.Base.Compute.RecordWarning("The LadybugTools Toolkit adapter does not support converting non-ILadybugTools objects to json, skipping all objects that are not an ILadybugTools");
+            }
+
+            CreateLadybug(lbtObjects, config);
+            return objects.Where(x => typeof(ILadybugTools).IsAssignableFrom(x.GetType())).ToList();
+        }
+    }
+}
diff --git a/LadybugTools_Engine/Convert/PascalCaseToSnakeCase.cs b/LadybugTools_Adapter/CRUD/CreateLadybug.cs
similarity index 61%
rename from LadybugTools_Engine/Convert/PascalCaseToSnakeCase.cs
rename to LadybugTools_Adapter/CRUD/CreateLadybug.cs
index 01e67595..431f153d 100644
--- a/LadybugTools_Engine/Convert/PascalCaseToSnakeCase.cs
+++ b/LadybugTools_Adapter/CRUD/CreateLadybug.cs
@@ -20,41 +20,34 @@
  * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
  */
 
-using BH.oM.Base.Attributes;
-using System.ComponentModel;
+using BH.Engine.Adapter;
+using BH.oM.Adapter;
+using BH.oM.Base.Debugging;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
 using System.Text;
 
-namespace BH.Engine.LadybugTools
+namespace BH.Adapter.LadybugTools
 {
-    public static partial class Convert
+    public partial class LadybugToolsAdapter : BHoMAdapter
     {
-        [Description("Convert the case of the given text.")]
-        [Input("str", "Text in \"PascalCase\".")]
-        [Output("str", "Text in \"snake_case\".")]
-        public static string PascalCaseToSnakeCase(this string str)
+        public static void CreateLadybug(List<ILadybugTools> objects, LadybugConfig config = null)
         {
-            if (str.Length < 2)
+            List<string> jsonObjects = new List<string>();
+            
+            foreach (ILadybugTools lbtObject in objects)
             {
-                return str;
+                jsonObjects.Add(lbtObject.FromBHoM());
             }
-
-            StringBuilder sb = new StringBuilder();
-            sb.Append(char.ToLowerInvariant(str[0]));
-            for (int i = 1; i < str.Length; ++i)
-            {
-                char c = str[i];
-                if (char.IsUpper(c))
-                {
-                    sb.Append('_');
-                    sb.Append(char.ToLowerInvariant(c));
-                }
-                else
-                {
-                    sb.Append(c);
-                }
-            }
-            return sb.ToString();
+            string json = "{}";
+            if (jsonObjects.Count > 1)
+                json = $"[{string.Join(", ", jsonObjects)}]";
+            else if (jsonObjects.Count == 1)
+                json = jsonObjects[0];
+            File.WriteAllText(config.JsonFile.GetFullFileName(), json);
         }
     }
 }
-
diff --git a/LadybugTools_Adapter/CRUD/Read.cs b/LadybugTools_Adapter/CRUD/Read.cs
new file mode 100644
index 00000000..78d71607
--- /dev/null
+++ b/LadybugTools_Adapter/CRUD/Read.cs
@@ -0,0 +1,43 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.Engine.Adapter;
+using BH.Engine.Serialiser;
+using BH.oM.Adapter;
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public partial class LadybugToolsAdapter : BHoMAdapter
+    {
+        protected override IEnumerable<IBHoMObject> IRead(Type type, IList indices = null, ActionConfig actionConfig = null)
+        {
+            LadybugConfig config = actionConfig as LadybugConfig;
+            return config.JsonFile.ToBHoM();
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/Collections/EPW.cs b/LadybugTools_Adapter/Convert/Collections/EPW.cs
new file mode 100644
index 00000000..4d3721cb
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/Collections/EPW.cs
@@ -0,0 +1,104 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.Engine.Serialiser;
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Collections.ObjectModel;
+using System.Linq;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static BH.oM.LadybugTools.EPW ToEPW(Dictionary<string, object> oldObject)
+        {
+            Location location = new Location();
+            Dictionary<string, object> metaData = new Dictionary<string, object>();
+
+            try
+            {
+                if (oldObject["location"].GetType() == typeof(CustomObject))
+                    oldObject["location"] = (oldObject["location"] as CustomObject).CustomData;
+                location = ToLocation(oldObject["location"] as Dictionary<string, object>);
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the Location of the EPW. returning a default Location.\n The error: {ex}");
+            }
+
+            try
+            {
+                if (oldObject["metadata"].GetType() == typeof(CustomObject))
+                    oldObject["metadata"] = (oldObject["metadata"] as CustomObject).CustomData;
+                metaData = (Dictionary<string, object>)oldObject["metadata"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the meta data of the EPW. returning an empty metadata object.\n The error: {ex}");
+            }
+
+            List<BH.oM.LadybugTools.HourlyContinuousCollection> collections = new List<BH.oM.LadybugTools.HourlyContinuousCollection>();
+
+            if (oldObject.ContainsKey("data_collections"))
+            {
+                foreach (var collection in oldObject["data_collections"] as List<object>)
+                {
+                    if (collection.GetType() == typeof(CustomObject))
+                        collections.Add(ToHourlyContinuousCollection((collection as CustomObject).CustomData));
+                    else
+                        collections.Add(ToHourlyContinuousCollection(collection as Dictionary<string, object>));
+                }
+            }
+            else
+            {
+                BH.Engine.Base.Compute.RecordError($"Could not find any data collections for this EPW object. Returning an empty list.");
+            }
+            
+            return new EPW()
+            {
+                Location = location,
+                DataCollections = collections,
+                Metadata = metaData
+            };
+
+        }
+        
+        public static string FromEPW(BH.oM.LadybugTools.EPW epw)
+        {
+            string type = "EPW";
+            string location = FromLocation(epw.Location).ToJson();
+            string dataCollections = string.Join(", ", epw.DataCollections.Select(x => FromHourlyContinuousCollection(x)));
+            string metadata = epw.Metadata.ToJson();
+
+            if (metadata.Length == 0)
+                metadata = "{}";
+
+            string json = @"{ ""type"": """ + type + @""", ""location"": " + location + @", ""data_collections"": [ " + dataCollections + @"], ""metadata"": " + metadata + "}";
+
+            return json;
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/Collections/HourlyContinuousCollection.cs b/LadybugTools_Adapter/Convert/Collections/HourlyContinuousCollection.cs
new file mode 100644
index 00000000..c0d5de87
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/Collections/HourlyContinuousCollection.cs
@@ -0,0 +1,82 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.Engine.Base;
+using BH.Engine.Serialiser;
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static BH.oM.LadybugTools.HourlyContinuousCollection ToHourlyContinuousCollection(Dictionary<string, object> oldObject)
+        {
+            Header header = new Header();
+            List<string> hourlyValues = new List<string>();
+            try
+            {
+                if (oldObject["header"].GetType() == typeof(CustomObject))
+                    oldObject["header"] = (oldObject["header"] as CustomObject).CustomData;
+                header = ToHeader(oldObject["header"] as Dictionary<string, object>);
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the Header of the HourlyContinuousCollection. returning a default Header.\n The error: {ex}");
+            }
+
+            try
+            {
+               hourlyValues = (oldObject["values"] as List<object>).Select(x => x.ToString()).ToList();
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when converting the values in a collection. Returning an empty collection: \n The error: {ex}");
+            }
+
+            return new oM.LadybugTools.HourlyContinuousCollection()
+            {
+                Values = hourlyValues,
+                Header = header
+            };
+        }
+
+        public static string FromHourlyContinuousCollection(BH.oM.LadybugTools.HourlyContinuousCollection collection)
+        {
+            string valuesAsString = null;
+            if (double.TryParse(collection.Values[0], out double result))
+                valuesAsString = string.Join(", ", collection.Values);
+            else
+                valuesAsString = "\"" + string.Join("\", \"", collection.Values) + "\"";
+
+            string type = @"""type"" : ""HourlyContinuous""";
+            string values = $@"""values"" : [{valuesAsString}]";
+            string header = $@"""header"" : {FromHeader(collection.Header).ToJson()}";
+
+            return "{ " + type + ", " + values + ", " + header + " }";
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/Constructions/EnergyMaterial.cs b/LadybugTools_Adapter/Convert/Constructions/EnergyMaterial.cs
new file mode 100644
index 00000000..9e852c54
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/Constructions/EnergyMaterial.cs
@@ -0,0 +1,152 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static BH.oM.LadybugTools.EnergyMaterial ToEnergyMaterial(Dictionary<string, object> oldObject)
+        {
+            string name = "";
+            double thickness = 0.1;
+            double conductivity = 1.0;
+            double density = 1000.0;
+            double specificHeat = 900.0;
+            Roughness roughness = Roughness.MediumRough;
+            double thermalAbsorptance = 0.9;
+            double solarAbsorptance = 0.7;
+            double visibleAbsorptance = 0.7;
+
+            try
+            {
+                name = (string)oldObject["identifier"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the identifier of the EnergyMaterial. returning name as default (\"\").\n The error: {ex}");
+            }
+
+            try
+            {
+                thickness = (double)oldObject["thickness"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the thickness of the EnergyMaterial. returning thickness as default ({thickness}).\n The error: {ex}");
+            }
+
+            try
+            {
+                conductivity = (double)oldObject["conductivity"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the conductivity of the EnergyMaterial. returning conductivity as default ({conductivity}).\n The error: {ex}");
+            }
+
+            try
+            {
+                density = (double)oldObject["density"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the density of the EnergyMaterial. returning density as default ({density}).\n The error: {ex}");
+            }
+
+            try
+            {
+                specificHeat = (double)oldObject["specific_heat"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the specific heat of the EnergyMaterial. returning specific heat as default ({specificHeat}).\n The error: {ex}");
+            }
+
+            if (Enum.TryParse((string)oldObject["roughness"], out Roughness result))
+                roughness = result;
+            else
+                BH.Engine.Base.Compute.RecordError($"An error occurred when trying to parse the roughness of the EnergyMaterial. returning roughness as default ({roughness})");
+
+            try
+            {
+                thermalAbsorptance = (double)oldObject["thermal_absorptance"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the thermal absorptance of the EnergyMaterial. returning thermal absorptance as default ({thermalAbsorptance}).\n The error: {ex}");
+            }
+
+            try
+            {
+                solarAbsorptance = (double)oldObject["solar_absorptance"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the solar absorptance of the EnergyMaterial. returning solar absorptance as default ({solarAbsorptance}).\n The error: {ex}");
+            }
+
+            try
+            {
+                visibleAbsorptance = (double)oldObject["visible_absorptance"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the visible absorptance of the EnergyMaterial. returning visible absorptance as default ({visibleAbsorptance}).\n The error: {ex}");
+            }
+
+            return new oM.LadybugTools.EnergyMaterial()
+            {
+                Name = name,
+                Thickness = thickness,
+                Conductivity = conductivity,
+                Density = density,
+                SpecificHeat = specificHeat,
+                Roughness = roughness,
+                ThermalAbsorptance = thermalAbsorptance,
+                SolarAbsorptance = solarAbsorptance,
+                VisibleAbsorptance = visibleAbsorptance
+            };
+        }
+
+        public static Dictionary<string, object> FromEnergyMaterial(BH.oM.LadybugTools.EnergyMaterial energyMaterial)
+        {
+            return new Dictionary<string, object>()
+            {
+                { "type", "EnergyMaterial" },
+                { "identifier", energyMaterial.Name },
+                { "thickness", energyMaterial.Thickness },
+                { "conductivity", energyMaterial.Conductivity },
+                { "density", energyMaterial.Density },
+                { "specific_heat", energyMaterial.SpecificHeat },
+                { "roughness", energyMaterial.Roughness.ToString() },
+                { "thermal_absorptance", energyMaterial.ThermalAbsorptance },
+                { "solar_absorptance", energyMaterial.SolarAbsorptance },
+                { "visible_absorptance", energyMaterial.VisibleAbsorptance }
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/Constructions/EnergyMaterialVegetation.cs b/LadybugTools_Adapter/Convert/Constructions/EnergyMaterialVegetation.cs
new file mode 100644
index 00000000..6965655a
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/Constructions/EnergyMaterialVegetation.cs
@@ -0,0 +1,221 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static BH.oM.LadybugTools.EnergyMaterialVegetation ToEnergyMaterialVegetation(Dictionary<string, object> oldObject)
+        {
+            string name = "";
+            double thickness = 0.1;
+            double conductivity = 0.35;
+            double density = 1100.0;
+            double specificHeat = 1200.0;
+            Roughness roughness = Roughness.MediumRough;
+            double soilThermalAbsorptance = 0.9;
+            double soilSolarAbsorptance = 0.7;
+            double soilVisibleAbsorptance = 0.7;
+            double plantHeight = 0.2;
+            double leafAreaIndex = 1.0;
+            double leafReflectivity = 0.22;
+            double leafEmissivity = 0.95;
+            double minimumStomatalResistance = 180.0;
+
+            try
+            {
+                name = (string)oldObject["identifier"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the identifier of the EnergyMaterial. returning name as default (\"\").\n The error: {ex}");
+            }
+
+            try
+            {
+                thickness = (double)oldObject["thickness"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the thickness of the EnergyMaterial. returning thickness as default ({thickness}).\n The error: {ex}");
+            }
+
+            try
+            {
+                conductivity = (double)oldObject["conductivity"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the conductivity of the EnergyMaterial. returning conductivity as default ({conductivity}).\n The error: {ex}");
+            }
+
+            try
+            {
+                density = (double)oldObject["density"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the density of the EnergyMaterial. returning density as default ({density}).\n The error: {ex}");
+            }
+
+            try
+            {
+                specificHeat = (double)oldObject["specific_heat"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the specific heat of the EnergyMaterial. returning specific heat as default ({specificHeat}).\n The error: {ex}");
+            }
+
+            if (Enum.TryParse((string)oldObject["roughness"], out Roughness result))
+                roughness = result;
+            else
+                BH.Engine.Base.Compute.RecordError($"An error occurred when trying to parse the roughness of the EnergyMaterial. returning roughness as default ({roughness})");
+
+            try
+            {
+                soilThermalAbsorptance = (double)oldObject["soil_thermal_absorptance"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the soil thermal absorptance of the EnergyMaterial. returning soil thermal absorptance as default ({soilThermalAbsorptance}).\n The error: {ex}");
+            }
+
+            try
+            {
+                soilSolarAbsorptance = (double)oldObject["soil_solar_absorptance"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the soil solar absorptance of the EnergyMaterial. returning soil solar absorptance as default ({soilSolarAbsorptance}).\n The error: {ex}");
+            }
+
+            try
+            {
+                soilThermalAbsorptance = (double)oldObject["soil_thermal_absorptance"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the soil thermal absorptance of the EnergyMaterial. returning soil thermal absorptance as default ({soilThermalAbsorptance}).\n The error: {ex}");
+            }
+
+            try
+            {
+                soilVisibleAbsorptance = (double)oldObject["soil_visible_absorptance"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the soil visible absorptance of the EnergyMaterial. returning soil visible absorptance as default ({soilVisibleAbsorptance}).\n The error: {ex}");
+            }
+
+            try
+            {
+                plantHeight = (double)oldObject["plant_height"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the plant height of the EnergyMaterial. returning plant height as default ({plantHeight}).\n The error: {ex}");
+            }
+
+            try
+            {
+                leafAreaIndex = (double)oldObject["leaf_area_index"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the leaf area index of the EnergyMaterial. returning leaf area index as default ({leafAreaIndex}).\n The error: {ex}");
+            }
+
+            try
+            {
+                leafReflectivity = (double)oldObject["leaf_reflectivity"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the leaf reflectivity of the EnergyMaterial. returning leaf reflectivity as default ({leafReflectivity}).\n The error: {ex}");
+            }
+
+            try
+            {
+                leafEmissivity = (double)oldObject["leaf_emissivity"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the leaf emissivity of the EnergyMaterial. returning leaf emissivity as default ({leafEmissivity}).\n The error: {ex}");
+            }
+
+            try
+            {
+                minimumStomatalResistance = (double)oldObject["min_stomatal_resist"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the minimum stomatal resistance of the EnergyMaterial. returning minimum stomatal resistance as default ({minimumStomatalResistance}).\n The error: {ex}");
+            }
+
+            return new oM.LadybugTools.EnergyMaterialVegetation()
+            {
+                Name = name,
+                Thickness = thickness,
+                Conductivity = conductivity,
+                Density = density,
+                SpecificHeat = specificHeat,
+                Roughness = roughness,
+                SoilThermalAbsorptance = soilThermalAbsorptance,
+                SoilSolarAbsorptance = soilSolarAbsorptance,
+                SoilVisibleAbsorptance = soilVisibleAbsorptance,
+                PlantHeight = plantHeight,
+                LeafAreaIndex = leafAreaIndex,
+                LeafReflectivity = leafReflectivity,
+                LeafEmissivity = leafEmissivity,
+                MinimumStomatalResistance = minimumStomatalResistance,
+            };
+        }
+
+        public static Dictionary<string, object> FromEnergyMaterialVegetation(BH.oM.LadybugTools.EnergyMaterialVegetation energyMaterial)
+        {
+            return new Dictionary<string, object>
+            {
+                { "type", "EnergyMaterialVegetation" },
+                { "identifier", energyMaterial.Name },
+                { "thickness", energyMaterial.Thickness },
+                { "conductivity", energyMaterial.Conductivity },
+                { "density", energyMaterial.Density },
+                { "specific_heat", energyMaterial.SpecificHeat },
+                { "roughness", energyMaterial.Roughness.ToString() },
+                { "soil_thermal_absorptance", energyMaterial.SoilThermalAbsorptance },
+                { "soil_solar_absorptance", energyMaterial.SoilSolarAbsorptance },
+                { "soil_visible_absorptance", energyMaterial.SoilVisibleAbsorptance },
+                { "plant_height", energyMaterial.PlantHeight },
+                { "leaf_area_index", energyMaterial.LeafAreaIndex },
+                { "leaf_reflectivity", energyMaterial.LeafReflectivity },
+                { "leaf_emissivity",  energyMaterial.LeafEmissivity },
+                { "min_stomatal_resist", energyMaterial.MinimumStomatalResistance }
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/FromBHoM.cs b/LadybugTools_Adapter/Convert/FromBHoM.cs
new file mode 100644
index 00000000..bd808759
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/FromBHoM.cs
@@ -0,0 +1,111 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.LadybugTools;
+using BH.Engine.Serialiser;
+using System.Collections.Generic;
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static string FromBHoM(this ILadybugTools input)
+        {
+            return ICustomify(input);
+        }
+
+        public static string ICustomify(this ILadybugTools lbtObject)
+        {
+            if (lbtObject == null)
+            {
+                BH.Engine.Base.Compute.RecordError("Input object is null.");
+                return null;
+            }
+            return Jsonify(lbtObject as dynamic);
+        }
+
+        private static string Jsonify(this AnalysisPeriod analysisPeriod)
+        {
+            return FromAnalysisPeriod(analysisPeriod).ToJson();
+        }
+
+        private static string Jsonify(this DataType dataType)
+        {
+            return FromDataType(dataType).ToJson();
+        }
+
+        private static string Jsonify(this EnergyMaterial energyMaterial)
+        {
+            return FromEnergyMaterial(energyMaterial).ToJson();
+        }
+
+        private static string Jsonify(this EnergyMaterialVegetation energyMaterial)
+        {
+            return FromEnergyMaterialVegetation(energyMaterial).ToJson();
+        }
+
+        private static string Jsonify(this EPW epw)
+        {
+            return FromEPW(epw);
+        }
+
+        private static string Jsonify(this ExternalComfort externalComfort)
+        {
+            return FromExternalComfort(externalComfort);
+        }
+
+        private static string Jsonify(this Header header)
+        {
+            return FromHeader(header).ToJson();
+        }
+
+        private static string Jsonify(this HourlyContinuousCollection collection)
+        {
+            return FromHourlyContinuousCollection(collection);
+        }
+
+        private static string Jsonify(this Location location)
+        {
+            return FromLocation(location).ToJson();
+        }
+
+        private static string Jsonify(this Shelter shelter)
+        {
+            return FromShelter(shelter);
+        }
+
+        private static string Jsonify(this SimulationResult simulationResult)
+        {
+            return FromSimulationResult(simulationResult);
+        }
+
+        private static string Jsonify(this Typology typology)
+        {
+            return FromTypology(typology);
+        }
+
+        private static Dictionary<string, object> Jsonify(this ILadybugTools obj)
+        {
+            BH.Engine.Base.Compute.RecordError($"The type: {obj.GetType()} is not convertible to ladybug serialisable json yet.");
+            return null;
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/Geometry/Point.cs b/LadybugTools_Adapter/Convert/Geometry/Point.cs
new file mode 100644
index 00000000..fdaab1ff
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/Geometry/Point.cs
@@ -0,0 +1,81 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using System;
+using System.Collections.Generic;
+using System.Text;
+using BH.oM.LadybugTools;
+using BH.oM.Geometry;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static Point ToPoint(Dictionary<string, object> oldObject)
+        {
+            double x = 0.0;
+            double y = 0.0;
+            double z = 0.0;
+
+            try
+            {
+                x = (double)oldObject["x"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading x of the Point. returning x as default ({x}).\n The error: {ex}");
+            }
+
+            try
+            {
+                y = (double)oldObject["y"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading y of the Point. returning y as default ({y}).\n The error: {ex}");
+            }
+
+            try
+            {
+                z = (double)oldObject["z"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading z of the Point. returning z as default ({z}).\n The error: {ex}");
+            }
+
+            return new Point()
+            {
+                X = x,
+                Y = y,
+                Z = z
+            };
+        }
+
+        public static string FromPoint(Point point)
+        {
+            string type = @"""Point3D""";
+            string xyz = $@"""x"" : {point.X}, ""y"" : {point.Y}, ""z"" : {point.Z}";
+            return @"{""type"" : " + type + ", " + xyz + "}";
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/Geometry/Shelter.cs b/LadybugTools_Adapter/Convert/Geometry/Shelter.cs
new file mode 100644
index 00000000..24628c2d
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/Geometry/Shelter.cs
@@ -0,0 +1,112 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Net;
+using System.Text;
+using BH.Engine.Serialiser;
+using BH.oM.Base;
+using BH.oM.Geometry;
+using BH.oM.LadybugTools;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static oM.LadybugTools.Shelter ToShelter(Dictionary<string, object> oldObject)
+        {
+            List<Point> points = new List<Point>();
+            List<double> radiationPorosity = Enumerable.Repeat(0.0, 8760).ToList();
+            List<double> windPorosity = Enumerable.Repeat(0.0, 8760).ToList();
+            if (oldObject.ContainsKey("vertices"))
+            {
+                foreach (var vertex in oldObject["vertices"] as List<object>)
+                {
+                    Dictionary<string, object> point;
+                    if (vertex.GetType() == typeof(CustomObject))
+                    {
+                        point = (vertex as CustomObject).CustomData;
+                    }
+                    else
+                    {
+                        point = vertex as Dictionary<string, object>;
+                    }
+
+                    points.Add(ToPoint(point));
+                }
+            }
+
+            try
+            {
+                List<double> values = new List<double>();
+                foreach (object value in oldObject["radiation_porosity"] as List<object>)
+                {
+                    values.Add(double.Parse(value.ToString()));
+                }
+                radiationPorosity = values;
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the radiation porosity of the Shelter. returning as default (List of 0s of length 8760).\n The error: {ex}");
+            }
+
+            try
+            {
+                List<double> values = new List<double>();
+                foreach (object value in oldObject["wind_porosity"] as List<object>)
+                {
+                    values.Add(double.Parse(value.ToString()));
+                }
+                windPorosity = values;
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the wind porosity of the Shelter. returning as default (List of 0s of length 8760).\n The error: {ex}");
+            }
+
+            return new oM.LadybugTools.Shelter()
+            {
+                Vertices = points,
+                RadiationPorosity = radiationPorosity,
+                WindPorosity = windPorosity
+                
+            };
+        }
+
+        public static string FromShelter(oM.LadybugTools.Shelter shelter)
+        {
+            string radiationPorosity = $@"""radiation_porosity"": [{string.Join(", ", shelter.RadiationPorosity)}]";
+            string windPorosity = $@"""wind_porosity"": [{string.Join(", ", shelter.WindPorosity)}]";
+
+            List<string> points = new List<string>();
+            foreach (Point point in shelter.Vertices)
+            {
+                points.Add(FromPoint(point));
+            }
+            string vertices = $@"""vertices"": [{string.Join(", ", points)}]";
+
+            return @"{ ""type"": ""Shelter""," + vertices + ", " + radiationPorosity + ", " + windPorosity + "}";
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/MetaData/AnalysisPeriod.cs b/LadybugTools_Adapter/Convert/MetaData/AnalysisPeriod.cs
new file mode 100644
index 00000000..b240ed32
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/MetaData/AnalysisPeriod.cs
@@ -0,0 +1,145 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static BH.oM.LadybugTools.AnalysisPeriod ToAnalysisPeriod(Dictionary<string, object> oldObject)
+        {
+            int startMonth = 1;
+            int startDay = 1;
+            int startHour = 0;
+            int endMonth = 12;
+            int endDay = 31;
+            int endHour = 23;
+            int timeStep = 1;
+            bool isLeapYear = false;
+
+            try
+            {
+                startMonth = (int)oldObject["st_month"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the start month of the AnalysisPeriod. returning start month as default ({startMonth}).\n The error: {ex}");
+            }
+
+            try
+            {
+                startDay = (int)oldObject["st_day"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the start day of the AnalysisPeriod. returning start day as default ({startDay}).\n The error: {ex}");
+            }
+
+            try
+            {
+                startHour = (int)oldObject["st_hour"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the start hour of the AnalysisPeriod. returning start hour as default ({startHour}).\n The error: {ex}");
+            }
+
+            try
+            {
+                endMonth = (int)oldObject["end_month"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the end month of the AnalysisPeriod. returning end month as default ({endMonth}).\n The error: {ex}");
+            }
+
+            try
+            {
+                endDay = (int)oldObject["end_day"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the end day of the AnalysisPeriod. returning end day as default ({endDay}).\n The error: {ex}");
+            }
+
+            try
+            {
+                endHour = (int)oldObject["end_hour"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the end hour of the AnalysisPeriod. returning end hour as default ({endHour}).\n The error: {ex}");
+            }
+
+            try
+            {
+                isLeapYear = (bool)oldObject["is_leap_year"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when trying to determine whether the AnalysisPeriod is a leap year. returning IsLeapYear as default (false).\n The error: {ex}");
+            }
+
+            try
+            {
+                timeStep = (int)oldObject["timestep"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the time step of the AnalysisPeriod. returning time step as default ({timeStep}).\n The error: {ex}");
+            }
+
+            return new oM.LadybugTools.AnalysisPeriod()
+            {
+                StartMonth = startMonth,
+                StartDay = startDay,
+                StartHour = startHour,
+                EndMonth = endMonth,
+                EndDay = endDay,
+                EndHour = endHour,
+                IsLeapYear = isLeapYear,
+                TimeStep = timeStep
+            };
+        }
+
+        public static Dictionary<string, object> FromAnalysisPeriod(BH.oM.LadybugTools.AnalysisPeriod analysisPeriod)
+        {
+            return new Dictionary<string, object>
+            {
+                { "type", "AnalysisPeriod" },
+                { "st_month", analysisPeriod.StartMonth },
+                { "st_day", analysisPeriod.StartDay },
+                { "st_hour", analysisPeriod.StartHour },
+                { "end_month", analysisPeriod.EndMonth },
+                { "end_day", analysisPeriod.EndDay },
+                { "end_hour", analysisPeriod.EndHour },
+                { "is_leap_year", analysisPeriod.IsLeapYear },
+                { "timestep", analysisPeriod.TimeStep }
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/MetaData/DataType.cs b/LadybugTools_Adapter/Convert/MetaData/DataType.cs
new file mode 100644
index 00000000..ca7b4071
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/MetaData/DataType.cs
@@ -0,0 +1,92 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static BH.oM.LadybugTools.DataType ToDataType(Dictionary<string, object> oldObject)
+        {
+            string baseUnit;
+            string dataType = "GenericType";
+            string name = "";
+
+            //base_unit only occurs when Data_Type is "GenericType".
+            if (oldObject.ContainsKey("base_unit"))
+                baseUnit = (string)oldObject["base_unit"];
+            else
+                baseUnit = "";
+
+            try
+            {
+                dataType = (string)oldObject["data_type"];
+            }
+            catch (Exception ex)
+            {
+                //This DataType object won't work properly in ladybug python when serialised again as it required a base_unit.
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the data type of the DataType. returning data type as default ({dataType}).\n The error: {ex}");
+            }
+
+            try
+            {
+                name = (string)oldObject["name"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the name of the DataType. returning name as default (\"\").\n The error: {ex}");
+            }
+
+            return new oM.LadybugTools.DataType()
+            {
+                Data_Type = dataType,
+                Name = name,
+                BaseUnit = baseUnit
+            };
+        }
+
+        public static Dictionary<string, object> FromDataType(BH.oM.LadybugTools.DataType dataType)
+        {
+            Dictionary<string, object> returnDict = new Dictionary<string, object>
+            {
+                { "name", dataType.Name },
+                { "data_type", dataType.Data_Type }
+            };
+            
+            string type = "DataType";
+
+            if (dataType.Data_Type == "GenericType")
+            {
+                type = "GenericDataType";
+                returnDict.Add("base_unit", dataType.BaseUnit);
+            }
+            returnDict.Add("type", type);
+
+            return returnDict;
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/MetaData/Header.cs b/LadybugTools_Adapter/Convert/MetaData/Header.cs
new file mode 100644
index 00000000..dc2c99ab
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/MetaData/Header.cs
@@ -0,0 +1,104 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        //TODO - find out if Header is needed or should just return an AnalysisPeriod
+        public static BH.oM.LadybugTools.Header ToHeader(Dictionary<string, object> oldObject)
+        {
+            string unit = "";
+            DataType dataType = new DataType();
+            AnalysisPeriod analysisPeriod = new AnalysisPeriod();
+            Dictionary<string, object> metaData = new Dictionary<string, object>();
+
+            try
+            {
+                if (oldObject["data_type"].GetType() == typeof(CustomObject))
+                    oldObject["data_type"] = (oldObject["data_type"] as CustomObject).CustomData;
+                dataType = ToDataType(oldObject["data_type"] as Dictionary<string, object>);
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the DataType of the Header. returning a default DataType object instead.\n The error: {ex}");
+            }
+
+            try
+            {
+                if (oldObject["analysis_period"].GetType() == typeof(CustomObject))
+                    oldObject["analysis_period"] = (oldObject["analysis_period"] as CustomObject).CustomData;
+                analysisPeriod = ToAnalysisPeriod(oldObject["analysis_period"] as Dictionary<string, object>);
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the AnalysisPeriod of the Header. returning a default AnalysisPeriod.\n The error: {ex}");
+            }
+
+            try
+            {
+                if (oldObject["metadata"].GetType() == typeof(CustomObject))
+                    oldObject["metadata"] = (oldObject["metadata"] as CustomObject).CustomData;
+                metaData = (Dictionary<string, object>)oldObject["metadata"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the meta data of the Header. returning an empty metadata object.\n The error: {ex}");
+            }
+
+            try
+            {
+                unit = (string)oldObject["unit"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the unit of the Header. returning unit as default (\"\").\n The error: {ex}");
+            }
+
+            return new Header()
+            {
+                Unit = unit,
+                DataType = dataType,
+                AnalysisPeriod = analysisPeriod,
+                Metadata = metaData
+            };
+        }
+
+        public static Dictionary<string, object> FromHeader(BH.oM.LadybugTools.Header header)
+        {
+            return new Dictionary<string, object>()
+            {
+                { "type", "Header" },
+                { "data_type", FromDataType(header.DataType) },
+                { "unit", header.Unit },
+                { "analysis_period", FromAnalysisPeriod(header.AnalysisPeriod) },
+                { "metadata", header.Metadata }
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/MetaData/Location.cs b/LadybugTools_Adapter/Convert/MetaData/Location.cs
new file mode 100644
index 00000000..c4b664c6
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/MetaData/Location.cs
@@ -0,0 +1,156 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static BH.oM.LadybugTools.Location ToLocation(Dictionary<string, object> oldObject)
+        {
+            string city = "";
+            string state = "";
+            string country = "";
+            double latitude = 0.0;
+            double longitude = 0.0;
+            double timeZone = 0.0;
+            double elevation = 0.0;
+            string stationID = "";
+            string source = "BHoM LadybugTools_Toolkit default";
+
+            try
+            {
+                city = (string)oldObject["city"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the city name of the Location. returning city as default (\"\").\n The error: {ex}");
+            }
+
+            try
+            {
+                state = (string)oldObject["state"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the state name of the Location. returning state as default (\"\").\n The error: {ex}");
+            }
+
+            try
+            {
+                country = (string)oldObject["country"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the country name of the Location. returning country as default (\"\").\n The error: {ex}");
+            }
+
+            try
+            {
+                latitude = (double)oldObject["latitude"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the latitude of the Location. returning latitude as default ({latitude}).\n The error: {ex}");
+            }
+
+            try
+            {
+                longitude = (double)oldObject["longitude"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the longitude of the Location. returning longitude as default ({longitude}).\n The error: {ex}");
+            }
+
+            try
+            {
+                timeZone = (double)oldObject["time_zone"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the time zone of the Location. returning time zone as default ({timeZone}).\n The error: {ex}");
+            }
+
+            try
+            {
+                elevation = (double)oldObject["elevation"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the elevation of the Location. returning elevation as default ({elevation}).\n The error: {ex}");
+            }
+
+            try
+            {
+                stationID = (string)oldObject["station_id"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the station ID of the Location. returning station ID as default (\"\").\n The error: {ex}");
+            }
+
+            try
+            {
+                source = (string)oldObject["source"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the source of the Location. returning source as default ({source}).\n The error: {ex}");
+            }
+
+            return new oM.LadybugTools.Location()
+            {
+                City = city,
+                State = state,
+                Country = country,
+                Latitude = latitude,
+                Longitude = longitude,
+                TimeZone = timeZone,
+                Elevation = elevation,
+                StationId = stationID,
+                Source = source
+            };
+        }
+
+        public static Dictionary<string, object> FromLocation(BH.oM.LadybugTools.Location location)
+        {
+            return new Dictionary<string, object>()
+            {
+                { "type", "Location" },
+                { "city", location.City },
+                { "state", location.State },
+                { "country", location.Country },
+                { "latitude", location.Latitude },
+                { "longitude", location.Longitude },
+                { "time_zone", location.TimeZone },
+                { "elevation", location.Elevation },
+                { "station_id", location.StationId },
+                { "source", location.Source }
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/Simulation/ExternalComfort.cs b/LadybugTools_Adapter/Convert/Simulation/ExternalComfort.cs
new file mode 100644
index 00000000..838423ee
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/Simulation/ExternalComfort.cs
@@ -0,0 +1,134 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static ExternalComfort ToExternalComfort(Dictionary<string, object> oldObject)
+        {
+            SimulationResult simulationResult = new SimulationResult();
+            Typology typology = new Typology();
+            List<HourlyContinuousCollection> simulatedProperties = new List<HourlyContinuousCollection>();
+
+            List<string> properties = new List<string>()
+            {
+                "dry_bulb_temperature",
+                "relative_humidity",
+                "wind_speed",
+                "mean_radiant_temperature",
+                "universal_thermal_climate_index"
+            };
+
+            try
+            {
+                if (oldObject["simulation_result"].GetType() == typeof(CustomObject))
+                    oldObject["simulation_result"] = ((CustomObject)oldObject["simulation_result"]).CustomData;
+                simulationResult = ToSimulationResult(oldObject["simulation_result"] as Dictionary<string, object>);
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the SimulationResult of the ExternalComfort. Returning a default SimulationResult. \n The error: {ex}");
+            }
+
+            try
+            {
+                if (oldObject["typology"].GetType() == typeof(CustomObject))
+                    oldObject["typology"] = ((CustomObject)oldObject["typology"]).CustomData;
+                typology = ToTypology(oldObject["typology"] as Dictionary<string, object>);
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when reading the Typology of the ExternalComfort. Returning a default Typology. \n The error: {ex}");
+            }
+
+            foreach (string property in properties)
+            {
+                if (oldObject.ContainsKey(property))
+                {
+                    try
+                    {
+                        if (oldObject[property].GetType() == typeof(CustomObject))
+                            oldObject[property] = ((CustomObject)oldObject[property]).CustomData;
+                        simulatedProperties.Add(ToHourlyContinuousCollection(oldObject[property] as Dictionary<string, object>));
+                    }
+                    catch (Exception ex)
+                    {
+                        BH.Engine.Base.Compute.RecordError($"An error occurred while parsing the collection {property} of the ExternalComfort. Returning an empty collection in its place.\n The error: {ex}");
+                        simulatedProperties.Add(new HourlyContinuousCollection() { Values = Enumerable.Repeat<string>(null, 8760).ToList() });
+                    }
+                }
+                else
+                {
+                    BH.Engine.Base.Compute.RecordError($"The incoming json for ExternalComfort does not contain the key: {property}. Returning an empty collection in its place.");
+                    simulatedProperties.Add(new HourlyContinuousCollection() { Values = Enumerable.Repeat<string>(null, 8760).ToList() });
+                }
+            }
+
+            return new ExternalComfort()
+            {
+                SimulationResult = simulationResult,
+                Typology = typology,
+                DryBulbTemperature = simulatedProperties[0],
+                RelativeHumidity = simulatedProperties[1],
+                WindSpeed = simulatedProperties[2],
+                MeanRadiantTemperature = simulatedProperties[3],
+                UniversalThermalClimateIndex = simulatedProperties[4]
+            };
+        }
+
+        public static string FromExternalComfort(ExternalComfort externalComfort)
+        {
+            string type = "\"type\": \"ExternalComfort\", ";
+            string simulationResult = $"\"simulation_result\": {FromSimulationResult(externalComfort.SimulationResult)}, ";
+            string typology = $"\"typology\": {FromTypology(externalComfort.Typology)}";
+            List<string> properties = new List<string>();
+
+            if (externalComfort.DryBulbTemperature != null)
+                properties.Add("\"dry_bulb_temperature\": " + FromHourlyContinuousCollection(externalComfort.DryBulbTemperature));
+
+            if (externalComfort.RelativeHumidity != null)
+                properties.Add("\"relative_humidity\": " + FromHourlyContinuousCollection(externalComfort.RelativeHumidity));
+
+            if (externalComfort.WindSpeed != null)
+                properties.Add("\"wind_speed\": " + FromHourlyContinuousCollection(externalComfort.WindSpeed));
+
+            if (externalComfort.MeanRadiantTemperature != null)
+                properties.Add("\"mean_radiant_temperature\": " + FromHourlyContinuousCollection(externalComfort.MeanRadiantTemperature));
+
+            if (externalComfort.UniversalThermalClimateIndex != null)
+                properties.Add("\"universal_thermal_climate_index\": " + FromHourlyContinuousCollection(externalComfort.UniversalThermalClimateIndex));
+
+            if (properties.Count > 0)
+                properties[0] = ", " + properties[0];
+            string simulatedProperties = string.Join(", ", properties);
+            return "{" + type + simulationResult + typology + simulatedProperties + "}";
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/Simulation/SimulationResult.cs b/LadybugTools_Adapter/Convert/Simulation/SimulationResult.cs
new file mode 100644
index 00000000..d5e74bde
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/Simulation/SimulationResult.cs
@@ -0,0 +1,219 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.Engine.Base;
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static SimulationResult ToSimulationResult(Dictionary<string, object> oldObject)
+        {
+            string epwFile = "";
+            IEnergyMaterialOpaque groundMaterial = new EnergyMaterial();
+            IEnergyMaterialOpaque shadeMaterial = new EnergyMaterial();
+            string name = "";
+            List<HourlyContinuousCollection> simulatedProperties = new List<HourlyContinuousCollection>();
+            List<string> properties = new List<string>()
+            {
+                "shaded_down_temperature",
+                "shaded_up_temperature",
+                "shaded_radiant_temperature",
+                "shaded_longwave_mean_radiant_temperature_delta",
+                "shaded_shortwave_mean_radiant_temperature_delta",
+                "shaded_mean_radiant_temperature",
+                "unshaded_down_temperature",
+                "unshaded_up_temperature",
+                "unshaded_radiant_temperature",
+                "unshaded_longwave_mean_radiant_temperature_delta",
+                "unshaded_shortwave_mean_radiant_temperature_delta",
+                "unshaded_mean_radiant_temperature"
+            };
+
+            try
+            {
+                epwFile = (string)oldObject["epw_file"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred during parsing of the epw file path of the SimulationResult. Returning default value (\"\").\n The error: {ex}");
+            }
+
+            try
+            {
+                if (oldObject["ground_material"].GetType() == typeof(CustomObject))
+                    oldObject["ground_material"] = ((CustomObject)oldObject["ground_material"]).CustomData;
+                switch ((oldObject["ground_material"] as Dictionary<string, object>)["type"])
+                {
+                    case "EnergyMaterial":
+                        groundMaterial = ToEnergyMaterial(oldObject["ground_material"] as Dictionary<string, object>);
+                        break;
+                    case "EnergyMaterialVegetation":
+                        groundMaterial = ToEnergyMaterialVegetation(oldObject["ground_material"] as Dictionary<string, object>);
+                        break;
+                    default:
+                        BH.Engine.Base.Compute.RecordError($"The ground material given is not an IEnergyMaterialOpaque but {(oldObject["ground_material"] as Dictionary<string, object>)["type"]}, so cannot be explicitly converted to an EnergyMaterialOpaque. Trying convert to EnergyMaterial...");
+                        groundMaterial = ToEnergyMaterial(oldObject["ground_material"] as Dictionary<string, object>);
+                        break;
+                }
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when deserialising the ground material of the SimulationResult. returning a default EnergyMaterial.\n The error: {ex}");
+            }
+
+            try
+            {
+                name = (string)oldObject["identifier"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred while parsing the name of the SimulationResult. Returning default value (\"\").\n The error: {ex}");
+            }
+
+            try
+            {
+                if (oldObject["shade_material"].GetType() == typeof(CustomObject))
+                    oldObject["shade_material"] = ((CustomObject)oldObject["shade_material"]).CustomData;
+                switch ((oldObject["shade_material"] as Dictionary<string, object>)["type"])
+                {
+                    case "EnergyMaterial":
+                        shadeMaterial = ToEnergyMaterial(oldObject["shade_material"] as Dictionary<string, object>);
+                        break;
+                    case "EnergyMaterialVegetation":
+                        shadeMaterial = ToEnergyMaterialVegetation(oldObject["shade_material"] as Dictionary<string, object>);
+                        break;
+                    default:
+                        BH.Engine.Base.Compute.RecordWarning($"The shade material given is not an IEnergyMaterialOpaque but {(oldObject["shade_material"] as Dictionary<string, object>)["type"]}, so cannot be explicitly converted to an EnergyMaterialOpaque. Trying convert to EnergyMaterial...");
+                        shadeMaterial = ToEnergyMaterial(oldObject["shade_material"] as Dictionary<string, object>);
+                        break;
+                }
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred when deserialising the shade material of the SimulationResult. returning a default EnergyMaterial.\n The error: {ex}");
+            }
+
+            foreach (string property in properties)
+            {
+                if (oldObject.ContainsKey(property))
+                {
+                    try
+                    {
+                        if (oldObject[property].GetType() == typeof(CustomObject))
+                            oldObject[property] = ((CustomObject)oldObject[property]).CustomData;
+                        simulatedProperties.Add(ToHourlyContinuousCollection(oldObject[property] as Dictionary<string, object>));
+                    }
+                    catch (Exception ex)
+                    {
+                        BH.Engine.Base.Compute.RecordError($"An error occurred while parsing the collection {property} of the SimulationResult. Returning an empty collection in its place.\n The error: {ex}");
+                        simulatedProperties.Add(new HourlyContinuousCollection() { Values = Enumerable.Repeat<string>(null, 8760).ToList() });
+                    }
+                }
+                else
+                {
+                    BH.Engine.Base.Compute.RecordError($"The incoming json does not contain the key: {property}. Returning an empty collection in its place.");
+                    simulatedProperties.Add(new HourlyContinuousCollection() { Values = Enumerable.Repeat<string>(null, 8760).ToList() });
+                }
+            }
+
+            return new SimulationResult()
+            {
+                EpwFile = epwFile,
+                GroundMaterial = groundMaterial,
+                ShadeMaterial = shadeMaterial,
+                Name = name,
+                ShadedDownTemperature = simulatedProperties[0],
+                ShadedUpTemperature = simulatedProperties[1],
+                ShadedRadiantTemperature = simulatedProperties[2],
+                ShadedLongwaveMeanRadiantTemperatureDelta = simulatedProperties[3],
+                ShadedShortwaveMeanRadiantTemperatureDelta = simulatedProperties[4],
+                ShadedMeanRadiantTemperature = simulatedProperties[5],
+                UnshadedDownTemperature = simulatedProperties[6],
+                UnshadedUpTemperature = simulatedProperties[7],
+                UnshadedRadiantTemperature = simulatedProperties[8],
+                UnshadedLongwaveMeanRadiantTemperatureDelta = simulatedProperties[9],
+                UnshadedShortwaveMeanRadiantTemperatureDelta = simulatedProperties[10],
+                UnshadedMeanRadiantTemperature = simulatedProperties[11]
+            };
+        }
+
+        public static string FromSimulationResult(SimulationResult simulationResult)
+        {
+            string type = $"\"type\": \"SimulationResult\", ";
+            string epwFile = $"\"epw_file\": \"{simulationResult.EpwFile}\", ";
+            string groundMaterial = $"\"ground_material\": {FromBHoM(simulationResult.GroundMaterial)}, ";
+            string shadeMaterial = $"\"shade_material\": {FromBHoM(simulationResult.ShadeMaterial)}, ";
+            string name = $"\"identifier\": \"{simulationResult.Name}\"";
+            List<string> properties = new List<string>();
+
+            if (simulationResult.ShadedDownTemperature != null)
+                properties.Add("\"shaded_down_temperature\": " + FromHourlyContinuousCollection(simulationResult.ShadedDownTemperature));
+
+            if (simulationResult.ShadedUpTemperature != null)
+                properties.Add("\"shaded_up_temperature\": " + FromHourlyContinuousCollection(simulationResult.ShadedUpTemperature));
+
+            if (simulationResult.ShadedRadiantTemperature != null)
+                properties.Add("\"shaded_radiant_temperature\": " + FromHourlyContinuousCollection(simulationResult.ShadedRadiantTemperature));
+
+            if (simulationResult.ShadedLongwaveMeanRadiantTemperatureDelta != null)
+                properties.Add("\"shaded_longwave_mean_radiant_temperature_delta\": " + FromHourlyContinuousCollection(simulationResult.ShadedLongwaveMeanRadiantTemperatureDelta));
+
+            if (simulationResult.ShadedShortwaveMeanRadiantTemperatureDelta != null)
+                properties.Add("\"shaded_shortwave_mean_radiant_temperature_delta\": " + FromHourlyContinuousCollection(simulationResult.ShadedShortwaveMeanRadiantTemperatureDelta));
+
+            if (simulationResult.ShadedMeanRadiantTemperature != null)
+                properties.Add("\"shaded_mean_radiant_temperature\": " + FromHourlyContinuousCollection(simulationResult.ShadedMeanRadiantTemperature));
+
+            if (simulationResult.UnshadedUpTemperature != null)
+                properties.Add("\"unshaded_up_temperature\": " + FromHourlyContinuousCollection(simulationResult.UnshadedUpTemperature));
+
+            if (simulationResult.UnshadedDownTemperature != null)
+                properties.Add("\"unshaded_down_temperature\": " + FromHourlyContinuousCollection(simulationResult.UnshadedDownTemperature));
+
+            if (simulationResult.UnshadedRadiantTemperature != null)
+                properties.Add("\"unshaded_radiant_temperature\": " + FromHourlyContinuousCollection(simulationResult.UnshadedRadiantTemperature));
+
+            if (simulationResult.UnshadedLongwaveMeanRadiantTemperatureDelta != null)
+                properties.Add("\"unshaded_longwave_mean_radiant_temperature_delta\": " + FromHourlyContinuousCollection(simulationResult.UnshadedLongwaveMeanRadiantTemperatureDelta));
+
+            if (simulationResult.UnshadedShortwaveMeanRadiantTemperatureDelta != null)
+                properties.Add("\"unshaded_shortwave_mean_radiant_temperature_delta\": " + FromHourlyContinuousCollection(simulationResult.UnshadedShortwaveMeanRadiantTemperatureDelta));
+
+            if (simulationResult.UnshadedMeanRadiantTemperature != null)
+                properties.Add("\"unshaded_mean_radiant_temperature\": " + FromHourlyContinuousCollection(simulationResult.UnshadedMeanRadiantTemperature));
+
+            if (properties.Count > 0)
+                properties[0] = ", " + properties[0];
+
+            string simulatedProperties = string.Join(", ", properties);
+
+            return "{" + type + epwFile + groundMaterial + shadeMaterial + name + simulatedProperties + "}";
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/Simulation/Typology.cs b/LadybugTools_Adapter/Convert/Simulation/Typology.cs
new file mode 100644
index 00000000..75b8419f
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/Simulation/Typology.cs
@@ -0,0 +1,136 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using System;
+using System.Collections.Generic;
+using System.Data.Common;
+using System.Linq;
+using System.Text;
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static oM.LadybugTools.Typology ToTypology(Dictionary<string, object> oldObject)
+        {
+            string identifier = "";
+            List<Shelter> shelters = new List<Shelter>();
+            List<double> evaporativeCoolingEffect = Enumerable.Repeat(0.0, 8760).ToList();
+            List<double?> targetWindSpeed = Enumerable.Repeat<double?>(null, 8760).ToList();
+            List<double> radiantTemperatureAdjustment = Enumerable.Repeat(0.0, 8760).ToList();
+
+            try
+            {
+                identifier = (string)oldObject["identifier"];
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred while parsing the identifier of the typology. Returning default value (\"\").\n The error: {ex}");
+            }
+
+            try
+            {
+                foreach (var shelter in oldObject["shelters"] as List<object>)
+                    if (shelter.GetType() == typeof(CustomObject))
+                        shelters.Add(ToShelter(((CustomObject)shelter).CustomData));
+                    else
+                        shelters.Add(ToShelter((Dictionary<string, object>)shelter));
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred while parsing one of the shelters of the typology. Returning any shelters that could be parsed.\n The error: {ex}");
+            }
+
+            try
+            {
+                List<double> values = new List<double>();
+                foreach (object value in oldObject["evaporative_cooling_effect"] as List<object>)
+                    values.Add(double.Parse(value.ToString()));
+
+                evaporativeCoolingEffect = values;
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred while parsing the evaporative cooling effect of the typology. Returning a list of 0.0s of length 8760.\n The error: {ex}");
+            }
+
+            try
+            {
+                List<double?> values = new List<double?>();
+                foreach (object value in oldObject["target_wind_speed"] as List<object>)
+                {
+                    if (value == null)
+                        values.Add(null);
+                    else if (double.TryParse(value.ToString(), out double result))
+                        values.Add(result);
+                    else
+                        values.Add(null);
+                }
+
+                targetWindSpeed = values;
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred while parsing the target wind speed of the typology. Returning a list of nulls of length 8760.\n The error: {ex}");
+            }
+
+            try
+            {
+                List<double> values = new List<double>();
+                foreach (object value in oldObject["radiant_temperature_adjustment"] as List<object>)
+                    values.Add(double.Parse(value.ToString()));
+
+                radiantTemperatureAdjustment = values;
+            }
+            catch (Exception ex)
+            {
+                BH.Engine.Base.Compute.RecordError($"An error occurred while parsing the radiant temperature adjustment of the typology. Returning a list of 0.0s of length 8760.\n The error: {ex}");
+            }
+
+            return new Typology()
+{
+                Name = identifier,
+                Shelters = shelters,
+                EvaporativeCoolingEffect = evaporativeCoolingEffect,
+                TargetWindSpeed = targetWindSpeed,
+                RadiantTemperatureAdjustment = radiantTemperatureAdjustment
+            };
+        }
+
+        public static string FromTypology(oM.LadybugTools.Typology typology)
+    {
+            string identifier = typology.Name;
+            string shelters = "[" + string.Join(", ", typology.Shelters.Select(s => FromShelter(s)).ToList()) + "]";
+            string evaporativeCoolingEffect = "[" + string.Join(", ", typology.EvaporativeCoolingEffect) + "]";
+            string targetWindSpeed = "[" + string.Join(", ", typology.TargetWindSpeed.Select(x => x.ToString()).Select(x => x == "" ? x = "null": x)) + "]";
+            string radiantTemperatureAdjustment = "[" + string.Join(", ", typology.RadiantTemperatureAdjustment) + "]";
+            return @"{""type"": ""Typology"", " + 
+                $@"""identifier"": ""{identifier}"", " + 
+                $@"""shelters"": {shelters}, " + 
+                $@"""evaporative_cooling_effect"": {evaporativeCoolingEffect}, " + 
+                $@"""target_wind_speed"": {targetWindSpeed}, " + 
+                $@"""radiant_temperature_adjustment"": {radiantTemperatureAdjustment}" + "}";
+        }
+    }
+}
diff --git a/LadybugTools_Adapter/Convert/ToBHoM.cs b/LadybugTools_Adapter/Convert/ToBHoM.cs
new file mode 100644
index 00000000..bc0bd0f7
--- /dev/null
+++ b/LadybugTools_Adapter/Convert/ToBHoM.cs
@@ -0,0 +1,123 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.LadybugTools;
+using System;
+using BH.Engine.Serialiser;
+using System.Collections.Generic;
+using System.Collections.ObjectModel;
+using System.Text;
+using BH.oM.Adapter;
+using System.IO;
+using BH.Engine.Adapter;
+using BH.oM.Base;
+
+namespace BH.Adapter.LadybugTools
+{
+    public static partial class Convert
+    {
+        public static List<IBHoMObject> ToBHoM(this FileSettings jsonFile)
+        {
+            string json = File.ReadAllText(jsonFile.GetFullFileName());
+            if (!json.StartsWith("["))
+                json = "[" + json;
+                
+            if (!json.EndsWith("]"))
+                json = json + "]";
+            IEnumerable<object> objs = Engine.Serialiser.Convert.FromJsonArray(json);
+            List<IBHoMObject> returnObjects = new List<IBHoMObject>();
+            foreach (var obj in objs)
+            {
+                Dictionary<string, object> lbtObject = null;
+                if (obj.GetType() == typeof(CustomObject))
+                {
+                    lbtObject = (obj as CustomObject).CustomData;
+                }
+                else if (obj.GetType() == typeof(Dictionary<string, object>))
+                {
+                    BH.Engine.Base.Compute.RecordWarning("The object was not deserialised as a CustomObject, are you sure that this file came from a LadybugTools Python object? \n Trying to cast to Dictionary...");
+                    lbtObject = obj as Dictionary<string, object>;
+                }
+                else
+                {
+                    BH.Engine.Base.Compute.RecordWarning($"One of the objects in the json given already deserialises to a BHoM object of type: {obj.GetType().FullName}. Returning this object.");
+                    returnObjects.Add((IBHoMObject)obj);
+                    continue;
+                }
+                if (lbtObject.ContainsKey("type"))
+                {
+                    switch (lbtObject["type"] as string)
+                    {
+                        case "AnalysisPeriod":
+                            returnObjects.Add(ToAnalysisPeriod(lbtObject));
+                            break;
+                        case "GenericDataType":
+                            returnObjects.Add(ToDataType(lbtObject));
+                            break;
+                        case "DataType":
+                            returnObjects.Add(ToDataType(lbtObject));
+                            break;
+                        case "EnergyMaterial":
+                            returnObjects.Add(ToEnergyMaterial(lbtObject));
+                            break;
+                        case "EnergyMaterialVegetation":
+                            returnObjects.Add(ToEnergyMaterialVegetation(lbtObject));
+                            break;
+                        case "EPW":
+                            returnObjects.Add(ToEPW(lbtObject));
+                            break;
+                        case "ExternalComfort":
+                            returnObjects.Add(ToExternalComfort(lbtObject));
+                            break;
+                        case "Header":
+                            returnObjects.Add(ToHeader(lbtObject));
+                            break;
+                        case "HourlyContinuous":
+                            returnObjects.Add(ToHourlyContinuousCollection(lbtObject));
+                            break;
+                        case "Location":
+                            returnObjects.Add(ToLocation(lbtObject));
+                            break;
+                        case "Shelter":
+                            returnObjects.Add(ToShelter(lbtObject));
+                            break;
+                        case "SimulationResult":
+                            returnObjects.Add(ToSimulationResult(lbtObject));
+                            break;
+                        case "Typology":
+                            returnObjects.Add(ToTypology(lbtObject));
+                            break;
+                        default:
+                            BH.Engine.Base.Compute.RecordError($"Objects of type {lbtObject["type"]} are not yet supported for conversion to a LadybugTools object.");
+                            break;
+                    }
+                }
+                else
+                {
+                    BH.Engine.Base.Compute.RecordError("One of the objects in the json file given does not specify the type of the object contained.");
+                    return null;
+                }
+            }
+            return returnObjects;
+        }
+    }
+}
\ No newline at end of file
diff --git a/LadybugTools_Adapter/LadybugTools_Adapter.cs b/LadybugTools_Adapter/LadybugTools_Adapter.cs
index 96c3e62c..ffd8588e 100644
--- a/LadybugTools_Adapter/LadybugTools_Adapter.cs
+++ b/LadybugTools_Adapter/LadybugTools_Adapter.cs
@@ -28,17 +28,11 @@ namespace BH.Adapter.LadybugTools
 {
     public partial class LadybugToolsAdapter : BHoMAdapter
     {
-        [Description("Produces an LadybugTools Adapter to allow interoperability with Ladybug and the BHoM.")]
+        [Description("Produces a LadybugTools Adapter that converts objects between Ladybug compatible json and BHoM objects.")]
         [Output("adapter", "Adapter to a LadybugTools object.")]
         public LadybugToolsAdapter()
         {
             m_AdapterSettings.DefaultPushType = oM.Adapter.PushType.CreateOnly;
-
-            return;
         }
-
     }
 }
-
-
-
diff --git a/LadybugTools_Adapter/LadybugTools_Adapter.csproj b/LadybugTools_Adapter/LadybugTools_Adapter.csproj
index 5ea5006e..329c1cf5 100644
--- a/LadybugTools_Adapter/LadybugTools_Adapter.csproj
+++ b/LadybugTools_Adapter/LadybugTools_Adapter.csproj
@@ -1,115 +1,94 @@
-<?xml version="1.0" encoding="utf-8"?>
-<Project ToolsVersion="15.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
-  <Import Project="$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props" Condition="Exists('$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props')" />
+<Project Sdk="Microsoft.NET.Sdk">
+
   <PropertyGroup>
-    <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
-    <Platform Condition=" '$(Platform)' == '' ">AnyCPU</Platform>
-    <ProjectGuid>{D1014F13-0D21-47D4-BE0C-0ED2779F387F}</ProjectGuid>
-    <OutputType>Library</OutputType>
-    <RootNamespace>LadybugTools_Adapter</RootNamespace>
-    <AssemblyName>LadybugTools_Adapter</AssemblyName>
-    <TargetFrameworkVersion>v4.7.2</TargetFrameworkVersion>
-    <FileAlignment>512</FileAlignment>
-    <AutoGenerateBindingRedirects>true</AutoGenerateBindingRedirects>
-    <Deterministic>true</Deterministic>
-    <TargetFrameworkProfile />
-  </PropertyGroup>
-  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' ">
-    <PlatformTarget>AnyCPU</PlatformTarget>
-    <DebugSymbols>true</DebugSymbols>
-    <DebugType>full</DebugType>
-    <Optimize>false</Optimize>
-    <OutputPath>..\Build\</OutputPath>
-    <DefineConstants>DEBUG;TRACE</DefineConstants>
-    <ErrorReport>prompt</ErrorReport>
-    <WarningLevel>4</WarningLevel>
-    <RegisterForComInterop>false</RegisterForComInterop>
-  </PropertyGroup>
-  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|AnyCPU' ">
-    <PlatformTarget>AnyCPU</PlatformTarget>
-    <DebugType>pdbonly</DebugType>
-    <Optimize>true</Optimize>
+    <TargetFramework>netstandard2.0</TargetFramework>
+    <AssemblyVersion>7.0.0.0</AssemblyVersion>
+    <Description>https://github.com/BHoM/LadybugTools_Toolkit</Description>
+    <Version>6.0.0</Version>
+    <Authors>BHoM</Authors>
+    <Copyright>Copyright © https://github.com/BHoM</Copyright>
+    <RootNamespace>BH.Adapter.LadybugTools</RootNamespace>
+    <FileVersion>7.0.0.0</FileVersion>
     <OutputPath>..\Build\</OutputPath>
-    <DefineConstants>TRACE</DefineConstants>
-    <ErrorReport>prompt</ErrorReport>
-    <WarningLevel>4</WarningLevel>
-  </PropertyGroup>
-  <PropertyGroup>
-    <StartupObject />
   </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Microsoft.CSharp" Version="4.7.0" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\LadybugTools_Engine\LadybugTools_Engine.csproj" />
+    <ProjectReference Include="..\LadybugTools_oM\LadybugTools_oM.csproj" />
+  </ItemGroup>
+
   <ItemGroup>
     <Reference Include="Adapter_Engine">
-      <HintPath>C:\ProgramData\BHoM\Assemblies\Adapter_Engine.dll</HintPath>
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Adapter_Engine.dll</HintPath>
       <SpecificVersion>False</SpecificVersion>
       <Private>False</Private>
     </Reference>
     <Reference Include="Adapter_oM">
-      <HintPath>C:\ProgramData\BHoM\Assemblies\Adapter_oM.dll</HintPath>
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Adapter_oM.dll</HintPath>
       <SpecificVersion>False</SpecificVersion>
       <Private>False</Private>
     </Reference>
     <Reference Include="BHoM">
-      <HintPath>C:\ProgramData\BHoM\Assemblies\BHoM.dll</HintPath>
+      <HintPath>$(ProgramData)\BHoM\Assemblies\BHoM.dll</HintPath>
       <SpecificVersion>False</SpecificVersion>
       <Private>False</Private>
     </Reference>
     <Reference Include="BHoM_Adapter">
-      <HintPath>C:\ProgramData\BHoM\Assemblies\BHoM_Adapter.dll</HintPath>
+      <HintPath>$(ProgramData)\BHoM\Assemblies\BHoM_Adapter.dll</HintPath>
       <SpecificVersion>False</SpecificVersion>
       <Private>False</Private>
     </Reference>
     <Reference Include="BHoM_Engine">
-      <HintPath>C:\ProgramData\BHoM\Assemblies\BHoM_Engine.dll</HintPath>
+      <HintPath>$(ProgramData)\BHoM\Assemblies\BHoM_Engine.dll</HintPath>
       <SpecificVersion>False</SpecificVersion>
       <Private>False</Private>
     </Reference>
     <Reference Include="Data_oM">
-      <HintPath>C:\ProgramData\BHoM\Assemblies\Data_oM.dll</HintPath>
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Data_oM.dll</HintPath>
       <SpecificVersion>False</SpecificVersion>
       <Private>False</Private>
     </Reference>
     <Reference Include="Dimensional_oM">
-      <HintPath>C:\ProgramData\BHoM\Assemblies\Dimensional_oM.dll</HintPath>
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Dimensional_oM.dll</HintPath>
       <SpecificVersion>False</SpecificVersion>
       <Private>False</Private>
     </Reference>
     <Reference Include="Geometry_oM">
-      <HintPath>C:\ProgramData\BHoM\Assemblies\Geometry_oM.dll</HintPath>
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Geometry_oM.dll</HintPath>
       <SpecificVersion>False</SpecificVersion>
       <Private>False</Private>
     </Reference>
     <Reference Include="Physical_oM">
-      <HintPath>C:\ProgramData\BHoM\Assemblies\Physical_oM.dll</HintPath>
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Physical_oM.dll</HintPath>
       <SpecificVersion>False</SpecificVersion>
       <Private>False</Private>
     </Reference>
-    <Reference Include="System" />
-    <Reference Include="System.Core" />
-    <Reference Include="System.Xml.Linq" />
-    <Reference Include="System.Data.DataSetExtensions" />
-    <Reference Include="Microsoft.CSharp" />
-    <Reference Include="System.Data" />
-    <Reference Include="System.Net.Http" />
-    <Reference Include="System.Xml" />
-  </ItemGroup>
-  <ItemGroup>
-    <Compile Include="LadybugTools_Adapter.cs" />
-    <Compile Include="Properties\AssemblyInfo.cs" />
-  </ItemGroup>
-  <ItemGroup>
-    <ProjectReference Include="..\LadybugTools_Engine\LadybugTools_Engine.csproj">
-      <Project>{68ab279e-3e50-4ee3-9e9e-c5c100df5c58}</Project>
-      <Name>LadybugTools_Engine</Name>
-      <Private>False</Private>
-    </ProjectReference>
-    <ProjectReference Include="..\LadybugTools_oM\LadybugTools_oM.csproj">
-      <Project>{3778ee3e-f314-4eb5-b99f-cf4a8c254e80}</Project>
-      <Name>LadybugTools_oM</Name>
+    <Reference Include="Python_Engine">
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Python_Engine.dll</HintPath>
+	  <Private>False</Private>
+	  <SpecificVersion>False</SpecificVersion>
+    </Reference>
+    <Reference Include="Python_oM">
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Python_oM.dll</HintPath>
+	  <Private>False</Private>
+	  <SpecificVersion>False</SpecificVersion>
+    </Reference>
+    <Reference Include="Serialiser_Engine">
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Serialiser_Engine.dll</HintPath>
       <Private>False</Private>
-    </ProjectReference>
+      <SpecificVersion>False</SpecificVersion>
+    </Reference>  </ItemGroup>
+
+  <ItemGroup>
+    <Folder Include="Objects\" />
   </ItemGroup>
-  <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
-  <PropertyGroup>
-    <PostBuildEvent>xcopy "$(TargetDir)$(TargetFileName)"  "C:\ProgramData\BHoM\Assemblies" /C /Y</PostBuildEvent>
-  </PropertyGroup>
-</Project>
\ No newline at end of file
+
+  <Target Name="PostBuild" AfterTargets="PostBuildEvent">
+    <Exec Command="xcopy &quot;$(TargetDir)$(TargetFileName)&quot;  &quot;C:\ProgramData\BHoM\Assemblies&quot; /C /Y" />
+  </Target>
+
+</Project>
diff --git a/LadybugTools_Adapter/Properties/AssemblyInfo.cs b/LadybugTools_Adapter/Properties/AssemblyInfo.cs
deleted file mode 100644
index 1040d4a7..00000000
--- a/LadybugTools_Adapter/Properties/AssemblyInfo.cs
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * This file is part of the Buildings and Habitats object Model (BHoM)
- * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
- *
- * Each contributor holds copyright over their respective contributions.
- * The project versioning (Git) records all such contribution source information.
- *                                           
- *                                                                              
- * The BHoM is free software: you can redistribute it and/or modify         
- * it under the terms of the GNU Lesser General Public License as published by  
- * the Free Software Foundation, either version 3.0 of the License, or          
- * (at your option) any later version.                                          
- *                                                                              
- * The BHoM is distributed in the hope that it will be useful,              
- * but WITHOUT ANY WARRANTY; without even the implied warranty of               
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
- * GNU Lesser General Public License for more details.                          
- *                                                                            
- * You should have received a copy of the GNU Lesser General Public License     
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
- */
-
-using System.Reflection;
-using System.Runtime.CompilerServices;
-using System.Runtime.InteropServices;
-
-// General Information about an assembly is controlled through the following
-// set of attributes. Change these attribute values to modify the information
-// associated with an assembly.
-[assembly: AssemblyTitle("LadybugTools_Adapter")]
-[assembly: AssemblyDescription("https://github.com/BHoM/LadybugTools_Toolkit")]
-[assembly: AssemblyConfiguration("")]
-[assembly: AssemblyCompany("")]
-[assembly: AssemblyProduct("LadybugTools_Adapter")]
-[assembly: AssemblyCopyright("Copyright ©  2021")]
-[assembly: AssemblyTrademark("")]
-[assembly: AssemblyCulture("")]
-
-// Setting ComVisible to false makes the types in this assembly not visible
-// to COM components.  If you need to access a type in this assembly from
-// COM, set the ComVisible attribute to true on that type.
-[assembly: ComVisible(false)]
-
-// The following GUID is for the ID of the typelib if this project is exposed to COM
-[assembly: Guid("d1014f13-0d21-47d4-be0c-0ed2779f387f")]
-
-// Version information for an assembly consists of the following four values:
-//
-//      Major Version
-//      Minor Version
-//      Build Number
-//      Revision
-//
-// You can specify all the values or you can default the Build and Revision Numbers
-// by using the '*' as shown below:
-// [assembly: AssemblyVersion("1.0.*")]
-[assembly: AssemblyVersion("6.0.0.0")]
-[assembly: AssemblyFileVersion("6.3.0.0")]
-
diff --git a/LadybugTools_Engine/Compute/EPWtoCSV.cs b/LadybugTools_Engine/Compute/EPWtoCSV.cs
index 4d62a534..03f48558 100644
--- a/LadybugTools_Engine/Compute/EPWtoCSV.cs
+++ b/LadybugTools_Engine/Compute/EPWtoCSV.cs
@@ -20,12 +20,11 @@
  * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.
  */
 
-using BH.Engine.Python;
 using BH.oM.Python;
 using BH.oM.Base.Attributes;
 
-using System.Collections.Generic;
 using System.ComponentModel;
+using System.IO;
 
 namespace BH.Engine.LadybugTools
 {
@@ -39,7 +38,7 @@ public static string EPWtoCSV(string epwFile, bool includeAdditional = false)
         {
             if (epwFile == null)
             {
-                BH.Engine.Base.Compute.RecordError("epwFile input cannot be null.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(epwFile)} input cannot be null.");
                 return null;
             }
 
@@ -48,28 +47,25 @@ public static string EPWtoCSV(string epwFile, bool includeAdditional = false)
                 BH.Engine.Base.Compute.RecordError($"{epwFile} doesn't appear to exist!");
                 return null;
             }
-            
+
             PythonEnvironment env = InstallPythonEnv_LBT(true);
-            string additionalProperties = includeAdditional ? "True" : "False";
 
-            string pythonScript = string.Join("\n", new List<string>()
+            epwFile = System.IO.Path.GetFullPath(epwFile);
+            string csvFile = System.IO.Path.ChangeExtension(epwFile, ".csv");
+
+            string script = Path.Combine(Python.Query.DirectoryCode(), "LadybugTools_Toolkit\\src\\ladybugtools_toolkit\\bhom\\wrapped", "epw_to_csv.py");
+
+            // run the process
+            string command = $"{env.Executable} {script} -e \"{epwFile}\" -a \"{includeAdditional}\"";
+            string result = Python.Compute.RunCommandStdout(command: command, hideWindows: true);
+
+            if (!File.Exists(csvFile))
             {
-                "import traceback",
-                "from pathlib import Path",
-                "from ladybug.epw import EPW",
-                "",
-                $"epw_path = Path(r'{epwFile}')",
-                "csv_path = epw_path.with_suffix('.csv')",
-                "try:",
-                "    from ladybugtools_toolkit.ladybug_extension.epw import epw_to_dataframe",
-                $"    epw_to_dataframe(EPW(epw_path.as_posix()), include_additional={additionalProperties}).to_csv(csv_path.as_posix())",
-                "    print(csv_path)",
-                "except Exception as exc:",
-                "    print(traceback.format_exc())",
-            });
+                BH.Engine.Base.Compute.RecordError($"File conversion failed due to {result}");
+                return null;
+            }
 
-            return env.RunPythonString(pythonScript).Trim();
+            return csvFile;
         }
     }
-}
-
+}
\ No newline at end of file
diff --git a/LadybugTools_Engine/Compute/EPWtoCustomObject.cs b/LadybugTools_Engine/Compute/EPWtoCustomObject.cs
deleted file mode 100644
index 65771fc1..00000000
--- a/LadybugTools_Engine/Compute/EPWtoCustomObject.cs
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * This file is part of the Buildings and Habitats object Model (BHoM)
- * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
- *
- * Each contributor holds copyright over their respective contributions.
- * The project versioning (Git) records all such contribution source information.
- *
- *
- * The BHoM is free software: you can redistribute it and/or modify
- * it under the terms of the GNU Lesser General Public License as published by
- * the Free Software Foundation, either version 3.0 of the License, or
- * (at your option) any later version.
- *
- * The BHoM is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * GNU Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public License
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.
- */
-
-using BH.Engine.Python;
-using BH.oM.Base;
-using BH.oM.Python;
-using BH.oM.Base.Attributes;
-using System.Collections.Generic;
-using System.ComponentModel;
-
-namespace BH.Engine.LadybugTools
-{
-    public static partial class Compute
-    {
-        [Description("Convert an EPW file into a custom BHoM object.")]
-        [Input("epwFile", "An EPW file.")]
-        [Output("object", "A BHoM object wrapping a Ladybug EPW object.")]
-        public static CustomObject EPWtoCustomObject(string epwFile)
-        {
-            if (epwFile == null)
-            {
-                BH.Engine.Base.Compute.RecordError("epwFile input cannot be null.");
-                return null;
-            }
-
-            if (!System.IO.File.Exists(epwFile))
-            {
-                BH.Engine.Base.Compute.RecordError($"{epwFile} doesn't appear to exist!");
-                return null;
-            }
-
-            PythonEnvironment env = InstallPythonEnv_LBT(true);
-
-            string pythonScript = string.Join("\n", new List<string>()
-            {
-                "import traceback",
-                "import json",
-                "from pathlib import Path",
-                "from ladybug.epw import EPW",
-                "",
-                $"epw_path = Path(r'{epwFile}')",
-                "try:",
-                "    print(json.dumps(EPW(epw_path.as_posix()).to_dict()))",
-                "except Exception as exc:",
-                "    print(traceback.format_exc())",
-            });
-
-            string output = env.RunPythonString(pythonScript).Trim();
-
-            return Serialiser.Convert.FromJson(output) as CustomObject;
-        }
-    }
-}
-
diff --git a/LadybugTools_Engine/Compute/ExternalComfort.cs b/LadybugTools_Engine/Compute/ExternalComfort.cs
deleted file mode 100644
index 0c3d6ba7..00000000
--- a/LadybugTools_Engine/Compute/ExternalComfort.cs
+++ /dev/null
@@ -1,102 +0,0 @@
-/*
- * This file is part of the Buildings and Habitats object Model (BHoM)
- * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
- *
- * Each contributor holds copyright over their respective contributions.
- * The project versioning (Git) records all such contribution source information.
- *
- *
- * The BHoM is free software: you can redistribute it and/or modify
- * it under the terms of the GNU Lesser General Public License as published by
- * the Free Software Foundation, either version 3.0 of the License, or
- * (at your option) any later version.
- *
- * The BHoM is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * GNU Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public License
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.
- */
-
-using BH.Engine.Python;
-using BH.oM.Python;
-using BH.oM.Base.Attributes;
-using System;
-using System.Collections.Generic;
-using System.ComponentModel;
-using System.Linq;
-using BH.oM.LadybugTools;
-using BH.Engine.Serialiser;
-using BH.Engine.Geometry;
-using BH.Engine.Base;
-
-namespace BH.Engine.LadybugTools
-{
-    public static partial class Compute
-    {
-        [Description("Run an External Comfort simulation and return results.")]
-        [Input("simulationResult", "A simulation result object.")]
-        [Input("typology", "An ExternalComfortTypology.")]
-        [Output("externalComfort", "An external comfort result object containing simulation results.")]
-        public static ExternalComfort ExternalComfort(SimulationResult simulationResult, Typology typology)
-        {
-            if (simulationResult == null)
-            {
-                BH.Engine.Base.Compute.RecordError("simulationResult input cannot be null.");
-                return null;
-            }
-
-            if (typology == null)
-            {
-                BH.Engine.Base.Compute.RecordError("typology input cannot be null.");
-                return null;
-            }
-
-            foreach (Shelter shelter in typology.Shelters)
-            {
-                if (!BH.Engine.Geometry.Create.Polyline(shelter.Vertices).IsPlanar())
-                {
-                    BH.Engine.Base.Compute.RecordError("A shelter in this Typology is not planar.");
-                    return null;
-                }
-            }
-
-            // construct the base object
-            ExternalComfort externalComfort = new ExternalComfort()
-            {
-                SimulationResult = simulationResult,
-                Typology = typology,
-            };
-
-            // send to Python to simulate/load
-            string externalComfortJsonStr = System.Text.RegularExpressions.Regex.Unescape(externalComfort.ToJson());
-            PythonEnvironment env = InstallPythonEnv_LBT(true);
-            string pythonScript = string.Join("\n", new List<string>()
-            {
-                "import json",
-                "import traceback",
-                "try:",
-                "    from ladybugtools_toolkit.external_comfort.external_comfort import ExternalComfort",
-                $"    external_comfort = ExternalComfort.from_json('{externalComfortJsonStr}')",
-                "    print(external_comfort.to_json())",
-                "except Exception as exc:",
-                "    print(traceback.format_exc())",
-            });
-
-            string output = env.RunPythonString(pythonScript).Trim().Split(new string[] { "\r\n", "\r", "\n" }, StringSplitOptions.None).Last();
-
-            if (output.Substring(0, 12).Contains("error"))
-            {
-                BH.Engine.Base.Compute.RecordError(Serialiser.Convert.FromJson(output).PropertyValue("error").ToString());
-                return null;
-            }
-            
-            // reload from Python results
-            return (ExternalComfort)Serialiser.Convert.FromJson(output);
-        }
-    }
-}
-
-
diff --git a/LadybugTools_Engine/Compute/GEMtoHBJSON.cs b/LadybugTools_Engine/Compute/GEMtoHBJSON.cs
index c7da7122..1f11b0d8 100644
--- a/LadybugTools_Engine/Compute/GEMtoHBJSON.cs
+++ b/LadybugTools_Engine/Compute/GEMtoHBJSON.cs
@@ -20,13 +20,11 @@
  * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
  */
 
-using BH.Engine.Python;
 using BH.oM.Python;
 using BH.oM.Base.Attributes;
 
-using System;
-using System.Collections.Generic;
 using System.ComponentModel;
+using System.IO;
 
 namespace BH.Engine.LadybugTools
 {
@@ -39,7 +37,7 @@ public static string GEMtoHBJSON(string gem)
         {
             if (gem == null)
             {
-                BH.Engine.Base.Compute.RecordError("gem input cannot be null.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(gem)} input cannot be null.");
                 return null;
             }
 
@@ -51,26 +49,21 @@ public static string GEMtoHBJSON(string gem)
 
             PythonEnvironment env = InstallPythonEnv_LBT(true);
 
-            string gemFile = System.IO.Path.GetFullPath(gem);
-            string outputDirectory = System.IO.Path.GetDirectoryName(gem);
-            string fileName = System.IO.Path.GetFileNameWithoutExtension(gem);
+            gem = System.IO.Path.GetFullPath(gem);
+            string hbjsonFile = System.IO.Path.ChangeExtension(gem, ".hbjson");
 
-            string pythonScript = String.Join("\n", new List<string>()
+            string script = Path.Combine(Python.Query.DirectoryCode(), "LadybugTools_Toolkit\\src\\ladybugtools_toolkit\\bhom\\wrapped", "gem_to_hbjson.py");
+
+            // run the process
+            string command = $"{env.Executable} {script} -g \"{gem}\"";
+            string result = Python.Compute.RunCommandStdout(command: command, hideWindows: true);
+
+            if (!File.Exists(hbjsonFile))
             {
-                "import traceback",
-                "from honeybee.model import Model",
-                "from honeybee_ies.reader import model_from_ies",
-                "",
-                "try:",
-                $"    model = model_from_ies(r\"{gemFile}\")",
-                $"    hbjson_file = model.to_hbjson(folder=r\"{outputDirectory}\", name=\"{fileName}\")",
-                "    print(hbjson_file)",
-                "except Exception as exc:",
-                "    print(traceback.format_exc())",
-            });
+                BH.Engine.Base.Compute.RecordError($"File conversion failed due to {result}");
+            }
 
-            return env.RunPythonString(pythonScript).Trim();
+            return hbjsonFile;
         }
     }
 }
-
diff --git a/LadybugTools_Engine/Compute/HBJSONtoGEM.cs b/LadybugTools_Engine/Compute/HBJSONtoGEM.cs
index f5578c9d..94d9e9dd 100644
--- a/LadybugTools_Engine/Compute/HBJSONtoGEM.cs
+++ b/LadybugTools_Engine/Compute/HBJSONtoGEM.cs
@@ -20,13 +20,11 @@
  * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
  */
 
-using BH.Engine.Python;
 using BH.oM.Python;
 using BH.oM.Base.Attributes;
 
-using System;
-using System.Collections.Generic;
 using System.ComponentModel;
+using System.IO;
 
 namespace BH.Engine.LadybugTools
 {
@@ -39,7 +37,7 @@ public static string HBJSONtoGEM(string hbjson)
         {
             if (hbjson == null)
             {
-                BH.Engine.Base.Compute.RecordError("hbjson input cannot be null.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(hbjson)} input cannot be null.");
                 return null;
             }
 
@@ -51,26 +49,21 @@ public static string HBJSONtoGEM(string hbjson)
 
             PythonEnvironment env = InstallPythonEnv_LBT(true);
 
-            string hbjsonFile = System.IO.Path.GetFullPath(hbjson);
-            string outputDirectory = System.IO.Path.GetDirectoryName(hbjsonFile);
-            string fileName = System.IO.Path.GetFileNameWithoutExtension(hbjsonFile);
+            hbjson = System.IO.Path.GetFullPath(hbjson);
+            string gemFile = System.IO.Path.ChangeExtension(hbjson, ".gem");
+            
+            string script = Path.Combine(Python.Query.DirectoryCode(), "LadybugTools_Toolkit\\src\\ladybugtools_toolkit\\bhom\\wrapped", "hbjson_to_gem.py");
 
-            string pythonScript = String.Join("\n", new List<string>()
+            // run the process
+            string command = $"{env.Executable} {script} -j \"{hbjson}\"";
+            string result = Python.Compute.RunCommandStdout(command: command, hideWindows: true);
+
+            if (!File.Exists(gemFile))
             {
-                "import traceback",
-                "from honeybee.model import Model",
-                "from pathlib import Path",
-                "",
-                "try:",
-                $"    model = Model.from_hbjson(r\"{hbjsonFile}\")",
-                $"    gem_file = model.to_gem(r\"{outputDirectory}\", name=\"{fileName}\")",
-                "    print(gem_file)",
-                "except Exception as exc:",
-                "    print(traceback.format_exc())",
-            });
+                BH.Engine.Base.Compute.RecordError($"File conversion failed due to {result}");
+            }
 
-            return env.RunPythonString(pythonScript).Trim();
+            return gemFile;
         }
     }
 }
-
diff --git a/LadybugTools_Engine/Compute/InstallPythonEnv_LBT.cs b/LadybugTools_Engine/Compute/InstallPythonEnv_LBT.cs
index 434b025a..ee0de93a 100644
--- a/LadybugTools_Engine/Compute/InstallPythonEnv_LBT.cs
+++ b/LadybugTools_Engine/Compute/InstallPythonEnv_LBT.cs
@@ -35,47 +35,31 @@ public static partial class Compute
         [Input("run", "Run the installation process.")]
         [Input("reinstall", "Reinstall the environment if it already exists.")]
         [Output("env", "The LadybugTools_Toolkit Python Environment, with BHoM code accessible.")]
-        [PreviousVersion("6.3", "BH.Engine.LadybugTools.Compute.InstallPythonEnv_LBT(System.Boolean)")]
         public static PythonEnvironment InstallPythonEnv_LBT(bool run = false, bool reinstall = false)
         {
-            // check if referenced Python is installed
-            string referencedExecutable = @"C:\Program Files\ladybug_tools\python\python.exe";
-            if (!File.Exists(referencedExecutable))
-            {
-                Base.Compute.RecordError($"Could not find referenced python executable at {referencedExecutable}. Please install Pollination try again.");
-                return null;
-            }
-
             if (!run)
                 return null;
 
-            // find out whether this environment already exists
-            bool exists = Python.Query.VirtualEnvironmentExists(Query.ToolkitName());
-
-            if (reinstall)
-                Python.Compute.RemoveVirtualEnvironment(Query.ToolkitName());
-            
-            // obtain python version
+            // check if referenced Python is installed, and get executable and version if it is
+            if (!Query.IsPollinationInstalled())
+                return null;
+            string referencedExecutable = System.Environment.GetFolderPath(System.Environment.SpecialFolder.ProgramFiles) + @"\ladybug_tools\python\python.exe";
             PythonVersion pythonVersion = Python.Query.Version(referencedExecutable);
 
-            // create virtualenvironment
-            PythonEnvironment env = Python.Compute.VirtualEnvironment(version: pythonVersion, name: Query.ToolkitName(), reload: true);
+            // check if environment already exists. If it does, and no reinstall requested, load it
+            bool exists = Python.Query.VirtualEnvironmentExists(envName: Query.ToolkitName(), pythonVersion: pythonVersion);
+            if (run && exists && !reinstall)
+                return Python.Compute.VirtualEnvironment(version: pythonVersion, name: Query.ToolkitName(), reload: true);
 
-            // return null if environment could not be created/loaded
-            if (env == null)
-                return null;
+            // create environment from scratch
+            PythonEnvironment env = Python.Compute.VirtualEnvironment(version: pythonVersion, name: Query.ToolkitName(), reload: false);
 
-            // install packages if this is a reinstall, or the environment did not originally exist
-            if (reinstall || !exists)
-            {
-                // install local package
-                env.InstallPackageLocal(Path.Combine(Python.Query.DirectoryCode(), Query.ToolkitName()));
+            // install local package
+            env.InstallPackageLocal(Path.Combine(Python.Query.DirectoryCode(), Query.ToolkitName()));
 
-                // create requiremetns from referenced executable
-                string requirementsTxt = Python.Compute.RequirementsTxt(referencedExecutable, Path.Combine(Python.Query.DirectoryEnvironments(), $"requirements_{Query.ToolkitName()}.txt"));
-                env.InstallRequirements(requirementsTxt);
-                File.Delete(requirementsTxt);
-            }
+            // create requirements from referenced executable
+            string requirementsTxt = Python.Compute.RequirementsTxt(referencedExecutable, Path.Combine(Python.Query.DirectoryEnvironments(), $"requirements_{Query.ToolkitName()}.txt"));
+            env.InstallRequirements(requirementsTxt);
 
             return env;
         }
diff --git a/LadybugTools_Engine/Compute/SimulationId.cs b/LadybugTools_Engine/Compute/SimulationId.cs
index 8c3d65a7..5eb78d12 100644
--- a/LadybugTools_Engine/Compute/SimulationId.cs
+++ b/LadybugTools_Engine/Compute/SimulationId.cs
@@ -34,23 +34,24 @@ public static partial class Compute
         [Input("groundMaterial", "A material object.")]
         [Input("shadeMaterial", "A material object.")]
         [Output("id", "A simulation ID.")]
-        public static string SimulationId(string epwFile, ILadybugToolsMaterial groundMaterial, ILadybugToolsMaterial shadeMaterial)
+        [PreviousVersion("7.0", "BH.Engine.LadybugTools.Compute.SimulationId(System.String, BH.oM.LadybugTools.ILadybugToolsMaterial, BH.oM.LadybugTools.ILadybugToolsMaterial)")]
+        public static string SimulationID(string epwFile, IEnergyMaterialOpaque groundMaterial, IEnergyMaterialOpaque shadeMaterial)
         {
             if (epwFile == null)
             {
-                BH.Engine.Base.Compute.RecordError("epwFile input cannot be null.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(epwFile)} input cannot be null.");
                 return null;
             }
 
             if (groundMaterial == null)
             {
-                BH.Engine.Base.Compute.RecordError("groundMaterial input cannot be null.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(groundMaterial)} input cannot be null.");
                 return null;
             }
 
             if (shadeMaterial == null)
             {
-                BH.Engine.Base.Compute.RecordError("shadeMaterial input cannot be null.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(shadeMaterial)} input cannot be null.");
                 return null;
             }
 
@@ -61,8 +62,8 @@ public static string SimulationId(string epwFile, ILadybugToolsMaterial groundMa
             }
 
             string epwId = Convert.SanitiseString(Path.GetFileNameWithoutExtension(epwFile));
-            string groundMaterialId = Convert.SanitiseString(groundMaterial.Identifier);
-            string shadeMaterialId = Convert.SanitiseString(shadeMaterial.Identifier);
+            string groundMaterialId = Convert.SanitiseString(groundMaterial.Name);
+            string shadeMaterialId = Convert.SanitiseString(shadeMaterial.Name);
             return $"{epwId}__{groundMaterialId}__{shadeMaterialId}";
         }
     }
diff --git a/LadybugTools_Engine/Compute/SimulationResult.cs b/LadybugTools_Engine/Compute/SimulationResult.cs
deleted file mode 100644
index 37a5305a..00000000
--- a/LadybugTools_Engine/Compute/SimulationResult.cs
+++ /dev/null
@@ -1,107 +0,0 @@
-/*
- * This file is part of the Buildings and Habitats object Model (BHoM)
- * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
- *
- * Each contributor holds copyright over their respective contributions.
- * The project versioning (Git) records all such contribution source information.
- *
- *
- * The BHoM is free software: you can redistribute it and/or modify
- * it under the terms of the GNU Lesser General Public License as published by
- * the Free Software Foundation, either version 3.0 of the License, or
- * (at your option) any later version.
- *
- * The BHoM is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * GNU Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public License
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.
- */
-
-using BH.Engine.Python;
-using BH.oM.Python;
-using BH.oM.Base.Attributes;
-using System;
-using System.Collections.Generic;
-using System.ComponentModel;
-using System.Linq;
-using System.IO;
-using BH.oM.LadybugTools;
-using BH.Engine.Serialiser;
-using BH.Engine.Base;
-
-namespace BH.Engine.LadybugTools
-{
-    public static partial class Compute
-    {
-        [Description("Run a simulation and return results.")]
-        [Input("epwFile", "An EPW file.")]
-        [Input("groundMaterial", "A ground material.")]
-        [Input("shadeMaterial", "A shade material.")]
-        [Output("simulationResult", "An simulation result object containing simulation results.")]
-        public static SimulationResult SimulationResult(string epwFile, ILadybugToolsMaterial groundMaterial, ILadybugToolsMaterial shadeMaterial)
-        {
-            if (epwFile == null)
-            {
-                BH.Engine.Base.Compute.RecordError("epwFile input cannot be null.");
-                return null;
-            }
-
-            if (groundMaterial == null)
-            {
-                BH.Engine.Base.Compute.RecordError("groundMaterial input cannot be null.");
-                return null;
-            }
-
-            if (shadeMaterial == null)
-            {
-                BH.Engine.Base.Compute.RecordError("shadeMaterial input cannot be null.");
-                return null;
-            }
-
-            if (!System.IO.File.Exists(epwFile))
-            {
-                BH.Engine.Base.Compute.RecordError($"{epwFile} doesn't appear to exist!");
-                return null;
-            }
-
-            // construct the base object
-            SimulationResult simulationResult = new SimulationResult()
-            {
-                EpwFile = Path.GetFullPath(epwFile).Replace(@"\", "/"),
-                GroundMaterial = groundMaterial,
-                ShadeMaterial = shadeMaterial,
-                Identifier = Compute.SimulationId(epwFile, groundMaterial, shadeMaterial)
-            };
-
-            // send to Python to simulate/load
-            string simulationResultJsonStr = System.Text.RegularExpressions.Regex.Unescape(simulationResult.ToJson());
-            PythonEnvironment env = InstallPythonEnv_LBT(true);
-            string pythonScript = string.Join("\n", new List<string>()
-            {
-                "import json",
-                "import traceback",
-                "try:",
-                "    from ladybugtools_toolkit.external_comfort.simulate import SimulationResult",
-                $"    simulation_result = SimulationResult.from_json('{simulationResultJsonStr}').run()",
-                "    print(simulation_result.to_json())",
-                "except Exception as exc:",
-                "    print(json.dumps({'error': str(traceback.format_exc())}))",
-            });
-            string output = env.RunPythonString(pythonScript).Trim().Split(new string[] { "\r\n", "\r", "\n" }, StringSplitOptions.None).Last();
-
-            if (output.Substring(0, 12).Contains("error"))
-            {
-                BH.Engine.Base.Compute.RecordError(Serialiser.Convert.FromJson(output).PropertyValue("error").ToString());
-                return null;
-            }
-            
-            // reload from Python results
-            return (SimulationResult)Serialiser.Convert.FromJson(output);
-        }
-    }
-}
-
-
diff --git a/LadybugTools_Engine/Convert/FromHoneybeeSurface.cs b/LadybugTools_Engine/Convert/FromHoneybeeSurface.cs
deleted file mode 100644
index a9644bf4..00000000
--- a/LadybugTools_Engine/Convert/FromHoneybeeSurface.cs
+++ /dev/null
@@ -1,148 +0,0 @@
-/*
- * This file is part of the Buildings and Habitats object Model (BHoM)
- * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
- *
- * Each contributor holds copyright over their respective contributions.
- * The project versioning (Git) records all such contribution source information.
- *                                           
- *                                                                              
- * The BHoM is free software: you can redistribute it and/or modify         
- * it under the terms of the GNU Lesser General Public License as published by  
- * the Free Software Foundation, either version 3.0 of the License, or          
- * (at your option) any later version.                                          
- *                                                                              
- * The BHoM is distributed in the hope that it will be useful,              
- * but WITHOUT ANY WARRANTY; without even the implied warranty of               
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
- * GNU Lesser General Public License for more details.                          
- *                                                                            
- * You should have received a copy of the GNU Lesser General Public License     
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
- */
-
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-using System.Threading.Tasks;
-
-using BH.oM.Geometry;
-using BH.oM.Environment;
-using BH.oM.Environment.Elements;
-
-using BH.Engine.Geometry;
-using BH.Engine.Environment;
-
-using BH.oM.Base.Attributes;
-using System.ComponentModel;
-
-namespace BH.Engine.LadybugTools
-{
-    public static partial class Convert
-    {
-        [Description("Converts a Honeybee Surface object into an Environments Panel or Opening depending on the Honeybee Surface type.\n" +
-            "If the Honeybee Surface is a Window type, then it will convert to an Environments Opening, otherwise it will convert to an Environments Panel. If the Honeybee Surface is an AirWall type then it will convert to an Environments Panel which contains an Opening with the same edges, and the Opening will be set as the Hole type.\n" +
-            "Providing a collection of Honeybee Surfaces will return a collection of Environment objects, which may be mixed as Panels and Openings depending on the types.\n" +
-            "You can filter out Panels from Openings using the Panels(List<IBHoMObject>) and Openings(List<IBHoMObject>) query methods in the Environment Engine.")]
-        [Input("honeybeeSrf", "A Honeybee Surface object from the Honeybee Plus libraries")]
-        [Output("environmentObject", "Either an Environment Panel or an Environment Opening depending on the Honeybee Surface type.")]
-        public static IEnvironmentObject FromHoneybeeSurface(dynamic honeybeeSrf)
-        {
-            IGeometry geometry = null;
-            try
-            {
-                geometry = BH.Engine.Rhinoceros.Convert.IFromRhino(honeybeeSrf._geometry as dynamic);
-            }
-            catch(Exception e)
-            {
-                BH.Engine.Base.Compute.RecordError("Could not convert that Honeybee Surface to an Environments Panel - recorded error was: " + e.ToString());
-                return null;
-            }
-
-            if(geometry == null)
-            {
-                BH.Engine.Base.Compute.RecordError("Honeybee Surface does not contain any valid geometry");
-                return null;
-            }
-
-            Polyline boundary = null;
-            try
-            {
-                if (geometry is ISurface)
-                    boundary = (geometry as ISurface).IExternalEdges().ToList().IJoin().FirstOrDefault().CollapseToPolyline(BH.oM.Geometry.Tolerance.Angle);
-                else if (geometry is ICurve)
-                    boundary = (geometry as ICurve).ICollapseToPolyline(BH.oM.Geometry.Tolerance.Angle);
-            }
-            catch(Exception e)
-            {
-                BH.Engine.Base.Compute.RecordError("Honeybee Surface geometry could not be converted successfully - recorded error was: " + e.ToString());
-                return null;
-            }
-
-            if(boundary == null)
-            {
-                BH.Engine.Base.Compute.RecordError("Honeybee Surface geometry could not be converted to BHoM Geometry");
-                return null;
-            }
-
-            string panelType = string.Empty;
-            try
-            {
-                panelType = honeybeeSrf._surface_type.ToString();//.__class__.__doc__;
-            }
-            catch(Exception e)
-            {
-                BH.Engine.Base.Compute.RecordWarning("Honeybee Surface does not contain a valid panel type. Using PanelType.ExternalWall instead. Recorded error was: " + e.ToString());
-            }
-
-            if(panelType == string.Empty)
-                BH.Engine.Base.Compute.RecordWarning("Honeybee Surface does not contain a valid panel type. Using PanelType.ExternalWall instead");
-
-            if (panelType.EndsWith("Window"))
-                return ToOpening(boundary.ToEdges());
-            else
-                return ToPanel(boundary.ToEdges(), panelType);            
-        }
-
-        private static Panel ToPanel(List<Edge> edges, string panelType)
-        {
-            Panel returnPanel = new Panel();
-            returnPanel.ExternalEdges = edges;
-
-            PanelType type = PanelType.WallExternal;
-            if (panelType.EndsWith("Wall"))
-                type = PanelType.WallExternal;
-            else if (panelType.EndsWith("Roof"))
-                type = PanelType.Roof;
-            else if (panelType.EndsWith("Floor"))
-                type = PanelType.Floor;
-            else if (panelType.EndsWith("Ceiling"))
-                type = PanelType.Ceiling;
-
-            returnPanel.Type = type;
-
-            if (panelType.EndsWith("AirWall"))
-            {
-                //This should have a 100% opening on the wall
-                Opening airWall = new Opening();
-                airWall.Type = OpeningType.Undefined; //Change to Hole when implemented
-                airWall.Edges = edges;
-                returnPanel.Openings.Add(airWall);
-            }
-
-            return returnPanel;
-        }
-
-        private static Opening ToOpening(List<Edge> edges)
-        {
-            Opening returnOpening = new Opening();
-            returnOpening.Edges = edges;
-            returnOpening.Type = OpeningType.Window;
-
-            return returnOpening;
-        }
-    }
-}
-
-
-
diff --git a/LadybugTools_Engine/Convert/ToCase.cs b/LadybugTools_Engine/Convert/ToCase.cs
new file mode 100644
index 00000000..2c43241e
--- /dev/null
+++ b/LadybugTools_Engine/Convert/ToCase.cs
@@ -0,0 +1,257 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Base.Attributes;
+using System.ComponentModel;
+using BH.oM.LadybugTools;
+using System.Collections.Generic;
+using System.Globalization;
+using System.Text.RegularExpressions;
+using System;
+
+namespace BH.Engine.LadybugTools
+{
+    public static partial class Convert
+    {
+        [Description("Convert the case of the given text.")]
+        [Input("str", "Text to convert the case of.")]
+        [Input("stringCase", "The case to convert the text to.")]
+        [Input("ignoreList", "A list of strings to ignore when converting the case.")]
+        [Output("str", "Text in \"PascalCase\".")]
+        public static string ToCase(this string str, StringCase stringCase = StringCase.Undefined, List<string> ignoreList = null)
+        {
+            if (stringCase == StringCase.Pascal)
+            {
+                return ToPascalCase(str, ignoreList);
+            }
+
+            if (stringCase == StringCase.Snake)
+            {
+                return ToSnakeCase(str, ignoreList);
+            }
+
+            if (stringCase == StringCase.Camel)
+            {
+                return ToCamelCase(str, ignoreList);
+            }
+
+            return str;
+        }
+
+        [Description("Convert the case of the keys in the given dictionary.")]
+        [Input("dict", "The dictionary whose keys will be converted.")]
+        [Input("stringCase", "The case to convert the text to.")]
+        [Input("ignoreList", "A list of strings to ignore when converting the case.")]
+        [Output("dict", "The dictionary with converted keys.")]
+        public static Dictionary<string, object> ToCase(this Dictionary<string, object> dict, StringCase stringCase = StringCase.Undefined, List<string> ignoreList = null)
+        {
+            if (stringCase == StringCase.Pascal)
+            {
+                return ToPascalCase(dict, ignoreList);
+            }
+
+            if (stringCase == StringCase.Snake)
+            {
+                return ToSnakeCase(dict, ignoreList);
+            }
+
+            if (stringCase == StringCase.Camel)
+            {
+                return ToCamelCase(dict, ignoreList);
+            }
+
+            return dict;
+        }   
+
+        [Description("Convert the case of the given text.")]
+        [Input("str", "Text to convert the case of.")]
+        [Input("ignoreList", "A list of strings to ignore when converting the case.")]
+        [Output("str", "Text in \"PascalCase\".")]
+        public static string ToPascalCase(this string str, List<string> ignoreList = null)
+        {
+            if (ignoreList == null)
+            {
+                ignoreList = new List<string>()
+                {
+                    "_t",
+                    "_bhomVersion",
+                };
+            }
+
+            if (ignoreList.Contains(str))
+            {
+                return str;
+            }
+
+            TextInfo textInfo = new CultureInfo("en-US", false).TextInfo;
+            str = textInfo.ToTitleCase(str);
+            return Regex.Replace(str, @"(_)(\w)", m => m.Groups[2].Value.ToUpper());
+        }
+
+        [Description("Convert the case of the keys in the given dictionary.")]
+        [Input("dict", "The dictionary whose keys will be converted.")]
+        [Input("ignoreList", "A list of strings to ignore when converting the case.")]
+        [Output("dict", "The dictionary with converted keys.")]
+        public static Dictionary<string, object> ToPascalCase(this Dictionary<string, object> dict, List<string> ignoreList = null)
+        {
+            var convertedDict = new Dictionary<string, object>();
+            foreach (var pair in dict)
+            {
+                var originalKey = ToPascalCase(pair.Key, ignoreList);
+                var value = pair.Value;
+
+                if (value is Dictionary<string, object> nestedDict)
+                {
+                    value = ToPascalCase(nestedDict, ignoreList);
+                }
+                else if (value is List<object> list)
+                {
+                    for (int i = 0; i < list.Count; i++)
+                    {
+                        if (list[i] is Dictionary<string, object> listItemDict)
+                        {
+                            list[i] = ToPascalCase(listItemDict, ignoreList);
+                        }
+                    }
+                }
+
+                convertedDict.Add(originalKey, value);
+            }
+            return convertedDict;
+        }
+
+        [Description("Convert the case of the given text.")]
+        [Input("str", "Text to convert the case of.")]
+        [Input("ignoreList", "A list of strings to ignore when converting the case.")]
+        [Output("str", "Text in \"snakle_case\".")]
+        public static string ToSnakeCase(this string str, List<string> ignoreList = null)
+        {
+            if (ignoreList == null)
+            {
+                ignoreList = new List<string>()
+                {
+                    "_t",
+                    "_bhomVersion",
+                };
+            }
+
+            if (ignoreList.Contains(str))
+            {
+                return str;
+            }
+
+            return Regex.Replace(str, "(?<!^)([A-Z][a-z]|(?<=[a-z])[A-Z])", "_$1").ToLower();
+        }
+
+        [Description("Convert the case of the keys in the given dictionary.")]
+        [Input("dict", "The dictionary whose keys will be converted.")]
+        [Input("ignoreList", "A list of strings to ignore when converting the case.")]
+        [Output("dict", "The dictionary with converted keys.")]
+        public static Dictionary<string, object> ToSnakeCase(this Dictionary<string, object> dict, List<string> ignoreList = null)
+        {
+            var convertedDict = new Dictionary<string, object>();
+            foreach (var pair in dict)
+            {
+                var originalKey = ToSnakeCase(pair.Key, ignoreList);
+                var value = pair.Value;
+
+                if (value is Dictionary<string, object> nestedDict)
+                {
+                    value = ToSnakeCase(nestedDict, ignoreList);
+                }
+                else if (value is List<object> list)
+                {
+                    for (int i = 0; i < list.Count; i++)
+                    {
+                        if (list[i] is Dictionary<string, object> listItemDict)
+                        {
+                            list[i] = ToSnakeCase(listItemDict, ignoreList);
+                        }
+                    }
+                }
+
+                convertedDict.Add(originalKey, value);
+            }
+            return convertedDict;
+        }
+
+        [Description("Convert the case of the given text.")]
+        [Input("str", "Text to convert the case of.")]
+        [Input("ignoreList", "A list of strings to ignore when converting the case.")]
+        [Output("str", "Text in \"camelCase\".")]
+        public static string ToCamelCase(this string str, List<string> ignoreList)
+        {
+            if (ignoreList == null)
+            {
+                ignoreList = new List<string>()
+                {
+                    "_t",
+                    "_bhomVersion",
+                };
+            }
+
+            if (ignoreList.Contains(str))
+            {
+                return str;
+            }
+
+            TextInfo textInfo = new CultureInfo("en-US", false).TextInfo;
+            str = textInfo.ToTitleCase(str);
+            str = Regex.Replace(str, @"(_)(\w)", m => m.Groups[2].Value.ToUpper());
+
+            return Char.ToLowerInvariant(str[0]) + str.Substring(1);
+        }
+
+        [Description("Convert the case of the keys in the given dictionary.")]
+        [Input("dict", "The dictionary whose keys will be converted.")]
+        [Input("ignoreList", "A list of strings to ignore when converting the case.")]
+        [Output("dict", "The dictionary with converted keys.")]
+        public static Dictionary<string, object> ToCamelCase(this Dictionary<string, object> dict, List<string> ignoreList = null)
+        {
+            var convertedDict = new Dictionary<string, object>();
+            foreach (var pair in dict)
+            {
+                var originalKey = ToCamelCase(pair.Key, ignoreList);
+                var value = pair.Value;
+
+                if (value is Dictionary<string, object> nestedDict)
+                {
+                    value = ToCamelCase(nestedDict);
+                }
+                else if (value is List<object> list)
+                {
+                    for (int i = 0; i < list.Count; i++)
+                    {
+                        if (list[i] is Dictionary<string, object> listItemDict)
+                        {
+                            list[i] = ToCamelCase(listItemDict);
+                        }
+                    }
+                }
+
+                convertedDict.Add(originalKey, value);
+            }
+            return convertedDict;
+        }
+    }
+}
+
diff --git a/LadybugTools_Engine/Convert/ToShelter.cs b/LadybugTools_Engine/Convert/ToShelter.cs
new file mode 100644
index 00000000..1524283a
--- /dev/null
+++ b/LadybugTools_Engine/Convert/ToShelter.cs
@@ -0,0 +1,67 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Linq;
+
+using BH.Engine.Environment;
+using BH.Engine.Geometry;
+using BH.oM.Base.Attributes;
+using BH.oM.Environment.Elements;
+using BH.oM.Geometry;
+using BH.oM.LadybugTools;
+
+namespace BH.Engine.LadybugTools
+{
+    public static partial class Convert
+    {
+        [Description("Converts an Environment Panel to a LadybugTools Shelter.")]
+        [Input("panel", "Environment Panel to be converted into a shelter.")]
+        [Input("radiationPorosity", "Radiation porosity for this shelter. (0-1).")]
+        [Input("windPorosity", "Wind porosity for this shelter. (0-1).")]
+        [Output("shelter", "LadybugTools Shelter object.")]
+        public static Shelter ToShelter(this Panel panel, List<double> radiationPorosity = null, List<double> windPorosity = null)
+        {
+            if (panel == null)
+            {
+                Base.Compute.RecordError($"{nameof(panel)} is null. Shelter cannot be created.");
+                return null;
+            }
+
+            if (radiationPorosity == null)
+            {
+                radiationPorosity = Enumerable.Repeat(0.0, 8760).ToList();
+            }
+
+            if (windPorosity == null)
+            {
+                windPorosity = Enumerable.Repeat(0.0, 8760).ToList();
+            }
+
+            List<Point> vertices = panel.Vertices().ToList();
+            vertices.RemoveAt(vertices.Count - 1); // python Shelter object doesn't want a closed polyline
+
+            return BH.Engine.LadybugTools.Create.Shelter(vertices: vertices, windPorosity: windPorosity, radiationPorosity: radiationPorosity);
+        }
+    }
+}
diff --git a/LadybugTools_Engine/Create/AnalysisPeriod.cs b/LadybugTools_Engine/Create/AnalysisPeriod.cs
new file mode 100644
index 00000000..a145dd69
--- /dev/null
+++ b/LadybugTools_Engine/Create/AnalysisPeriod.cs
@@ -0,0 +1,82 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Base.Attributes;
+using BH.oM.LadybugTools;
+using System.Collections.Generic;
+using System.ComponentModel;
+
+namespace BH.Engine.LadybugTools
+{
+    public static partial class Create
+    {
+        [Description("Create an AnalysisPeriod object.")]
+        [Input("startMonth", "The starting month of the analysis period. 1-12.")]
+        [Input("startDay", "The starting day of the analysis period. 1-31.")]
+        [Input("startHour", "The starting hour of the analysis period. 0-23.")]
+        [Input("endMonth", "The ending month of the analysis period. 1-12.")]
+        [Input("endDay", "The ending day of the analysis period. 1-31.")]
+        [Input("endHour", "The ending hour of the analysis period. 0-23.")]
+        [Input("isLeapYear", "Whether the analysis period represents a leap year.")]
+        [Input("timestep", "The number of timesteps per hour. One of {1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 60}.")]
+        [Output("analysisPeriod", "An AnalysisPeriod object.")]
+        public static AnalysisPeriod AnalysisPeriod(int startMonth = 1, int startDay = 1, int startHour = 0, int endMonth = 12, int endDay = 31, int endHour = 23, bool isLeapYear = false, int timestep = 1)
+        {
+            if (startMonth < 1 || startMonth > 12 || endMonth < 1 || endMonth > 12)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(startMonth)} and {nameof(endMonth)} must be between 1 and 12.");
+                return null;
+            }
+
+            if (startDay < 1 || startDay > 31 || endDay < 1 || endDay > 31)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(startDay)} and {nameof(endDay)} must be between 1 and 31.");
+                return null;
+            }
+
+            if (startHour < 0 || startHour > 23 || endHour < 0 || endHour > 23)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(startHour)} and {nameof(endHour)} must be between 0 and 23.");
+                return null;
+            }
+
+            List<int> allowedTimesteps = new List<int>() { 1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 60 };
+            if (!allowedTimesteps.Contains(timestep))
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(timestep)} must be one of {string.Join(", ", allowedTimesteps)}.");
+                return null;
+            }
+
+            return new AnalysisPeriod()
+            {
+                StartMonth = startMonth,
+                StartDay = startDay,
+                StartHour = startHour,
+                EndMonth = endMonth,
+                EndDay = endDay,
+                EndHour = endHour,
+                IsLeapYear = isLeapYear,
+                TimeStep = timestep
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Engine/Create/EnergyMaterial.cs b/LadybugTools_Engine/Create/EnergyMaterial.cs
new file mode 100644
index 00000000..a0e28116
--- /dev/null
+++ b/LadybugTools_Engine/Create/EnergyMaterial.cs
@@ -0,0 +1,128 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Base.Attributes;
+using BH.oM.LadybugTools;
+using System.ComponentModel;
+
+namespace BH.Engine.LadybugTools
+{
+    public static partial class Create
+    {
+        [Description("Create an EnergyMaterial object.")]
+        [Input("identifier", "A unique identifier for the material. This identifier should not contain any '!' characters and should be less than 100 characters long.")]
+        [Input("thickness", "The thickness of the material in meters.")]
+        [Input("conductivity", "The conductivity of the material in W/m-K.")]
+        [Input("density", "The density of the material in kg/m3.")]
+        [Input("specificHeat", "The specific heat of the material in J/kg-K.")]
+        [Input("roughness", "The roughness of the material.")]
+        [Input("thermalAbsorptance", "The thermal absorptance of the material. 0-1.")]
+        [Input("solarAbsorptance", "The solar absorptance of the material. 0-1.")]
+        [Input("visibleAbsorptance", "The visible absorptance of the material. 0-1.")]
+        [Output("energyMaterial", "An EnergyMaterial object.")]
+        public static EnergyMaterial EnergyMaterial(
+            string identifier,
+            double thickness,
+            double conductivity,
+            double density,
+            double specificHeat,
+            Roughness roughness = Roughness.MediumRough,
+            double thermalAbsorptance = 0.9,
+            double solarAbsorptance = 0.7,
+            double visibleAbsorptance = 0.7
+        )
+        {
+            if (identifier.Contains("!"))
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(identifier)} cannot contain '!' character.");
+                return null;
+            }
+
+            if (identifier.Length > 100)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(identifier)} cannot be longer than 100 characters.");
+                return null;
+            }
+
+            if (specificHeat < 100)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(specificHeat)} must be greater than 100.");
+                return null;
+            }
+
+            if (conductivity < 0)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(conductivity)} must be greater than 0.");
+                return null;
+            }
+
+            if (density < 0)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(density)} must be greater than 0.");
+                return null;
+            }
+
+            if (thickness <= 0)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(thickness)} must be greater than 0.");
+                return null;
+            }
+
+            if (roughness == Roughness.Undefined)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(roughness)} must be defined.");
+                return null;
+            }
+
+            if (thermalAbsorptance < 0 || thermalAbsorptance > 1)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(thermalAbsorptance)} must be between 0 and 1.");
+                return null;
+            }
+
+            if (solarAbsorptance < 0 || solarAbsorptance > 1)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(solarAbsorptance)} must be between 0 and 1.");
+                return null;
+            }
+
+            if (visibleAbsorptance < 0 || visibleAbsorptance > 1)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(visibleAbsorptance)} must be between 0 and 1.");
+                return null;
+            }
+
+            return new oM.LadybugTools.EnergyMaterial()
+            {
+                Name = identifier,
+                Thickness = thickness,
+                Conductivity = conductivity,
+                Density = density,
+                SpecificHeat = specificHeat,
+                Roughness = roughness,
+                ThermalAbsorptance = thermalAbsorptance,
+                SolarAbsorptance = solarAbsorptance,
+                VisibleAbsorptance = visibleAbsorptance,
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Engine/Create/EnergyMaterialVegetation.cs b/LadybugTools_Engine/Create/EnergyMaterialVegetation.cs
new file mode 100644
index 00000000..76ab883b
--- /dev/null
+++ b/LadybugTools_Engine/Create/EnergyMaterialVegetation.cs
@@ -0,0 +1,173 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Base.Attributes;
+using BH.oM.LadybugTools;
+using System.ComponentModel;
+
+namespace BH.Engine.LadybugTools
+{
+    public static partial class Create
+    {
+        [Description("Create an EnergyMaterialVegetation object.")]
+        [Input("identifier", "The identifier for this material object.")]
+        [Input("thickness", "The thickness of the material in meters.")]
+        [Input("conductivity", "The conductivity of the material in W/m-K.")]
+        [Input("density", "The density of the material in kg/m3.")]
+        [Input("specificHeat", "The specific heat of the material in J/kg-K.")]
+        [Input("roughness", "The roughness of the material.")]
+        [Input("plantHeight", "The height of the vegetation in meters.")]
+        [Input("leafAreaIndex", "The leaf area index of the vegetation. Dimensionless value, typically between 0.001 and 5.0.")]
+        [Input("leafReflectivity", "The reflectivity of the leaves. Dimensionless value, typically between 0.18 and 0.25.")]
+        [Input("leafEmissivity", "The emissivity of the leaves. Dimensionless value, typically between 0.8 and 1.0.")]
+        [Input("minStomatalResist", "The minimum stomatal resistance of the leaves (resistance to moisture transport, in s/m, typically between 50 and 300.")]
+        [Input("soilThermalAbsorptance", "The thermal absorptance of the soil. 0-1.")]
+        [Input("soilSolarAbsorptance", "The solar absorptance of the soil. 0-1.")]
+        [Input("soilVisibleAbsorptance", "The visible absorptance of the soil. 0-1.")]
+        [Output("energyMaterialVegetation", "An EnergyMaterialVegetation object.")]
+        public static EnergyMaterialVegetation EnergyMaterialVegetation(
+            string identifier,
+            double thickness = 0.1,
+            double conductivity = 0.35,
+            double density = 1100,
+            double specificHeat = 1200,
+            Roughness roughness = Roughness.MediumRough,
+            double plantHeight = 0.2,
+            double leafAreaIndex = 1.0,
+            double leafReflectivity = 0.22,
+            double leafEmissivity = 0.95,
+            double minStomatalResist = 180,
+            double soilThermalAbsorptance = 0.9,
+            double soilSolarAbsorptance = 0.7,
+            double soilVisibleAbsorptance = 0.7
+        )
+        {
+            if (identifier.Contains("!"))
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(identifier)} cannot contain '!' character.");
+                return null;
+            }
+
+            if (identifier.Length > 100)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(identifier)} cannot be longer than 100 characters.");
+                return null;
+            }
+
+            if (specificHeat < 100)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(specificHeat)} must be greater than 100.");
+                return null;
+            }
+
+            if (conductivity < 0)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(conductivity)} must be greater than 0.");
+                return null;
+            }
+
+            if (density < 0)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(density)} must be greater than 0.");
+                return null;
+            }
+
+            if (thickness <= 0)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(thickness)} must be greater than 0.");
+                return null;
+            }
+
+            if (roughness == Roughness.Undefined)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(roughness)} must be defined.");
+                return null;
+            }
+
+            if (soilThermalAbsorptance < 0 || soilThermalAbsorptance > 1)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(soilThermalAbsorptance)} must be between 0 and 1.");
+                return null;
+            }
+
+            if (soilSolarAbsorptance < 0 || soilSolarAbsorptance > 1)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(soilSolarAbsorptance)} must be between 0 and 1.");
+                return null;
+            }
+
+            if (soilVisibleAbsorptance < 0 || soilVisibleAbsorptance > 1)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(soilVisibleAbsorptance)} must be between 0 and 1.");
+                return null;
+            }
+
+            if (plantHeight < 0.005 || plantHeight > 1)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(plantHeight)} must be between 0.005 and 1.");
+                return null;
+            }
+
+            if (leafAreaIndex < 0.001 || leafAreaIndex > 5)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(leafAreaIndex)} must be between 0.001 and 5.");
+                return null;
+            }
+
+            if (leafEmissivity < 0.8 || leafEmissivity > 1)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(leafEmissivity)} must be between 0.8 and 1.");
+                return null;
+            }
+
+            if (leafReflectivity < 0.05 || leafReflectivity > 0.5)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(leafReflectivity)} must be between 0.05 and 0.5.");
+                return null;
+            }
+
+            if (minStomatalResist < 50 || minStomatalResist > 300)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(minStomatalResist)} must be between 50 and 300.");
+                return null;
+            }
+
+            return new oM.LadybugTools.EnergyMaterialVegetation()
+            {
+                Name = identifier,
+                Thickness = thickness,
+                Conductivity = conductivity,
+                Density = density,
+                SpecificHeat = specificHeat,
+                Roughness = roughness,
+                PlantHeight = plantHeight,
+                LeafAreaIndex = leafAreaIndex,
+                LeafReflectivity = leafReflectivity,
+                LeafEmissivity = leafEmissivity,
+                SoilThermalAbsorptance = soilThermalAbsorptance,
+                SoilSolarAbsorptance = soilSolarAbsorptance,
+                SoilVisibleAbsorptance = soilVisibleAbsorptance,
+                MinimumStomatalResistance = minStomatalResist,
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Engine/Create/Location.cs b/LadybugTools_Engine/Create/Location.cs
new file mode 100644
index 00000000..a1ca7fbe
--- /dev/null
+++ b/LadybugTools_Engine/Create/Location.cs
@@ -0,0 +1,92 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Base.Attributes;
+using BH.oM.LadybugTools;
+using System.ComponentModel;
+
+namespace BH.Engine.LadybugTools
+{
+    public static partial class Create
+    {
+        [Description("Create a Location object.")]
+        [Input("city", "The city of the location.")]
+        [Input("state", "The state of the location.")]
+        [Input("country", "The country of the location.")]
+        [Input("latitude", "The latitude of the location in degrees, between -90 and 90.")]
+        [Input("longitude", "The longitude of the location in degrees, between -180 and 180.")]
+        [Input("timeZone", "The time zone of the location, in hours between -12 (west) and +14 (east).")]
+        [Input("elevation", "The elevation of the location in meters.")]
+        [Input("stationId", "The station ID of the location.")]
+        [Input("source", "The source of the location.")]
+        [Output("location", "A Location object.")]
+        public static Location Location(
+            string city = "-",
+            string state = "-",
+            string country = "-",
+            double latitude = 0,
+            double longitude = 0,
+            double timeZone = 0,
+            double elevation = 0,
+            string stationId = "",
+            string source = ""
+        )
+        {
+            if (latitude < -90 || latitude > 90)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(latitude)} must be between -90 and 90.");
+                return null;
+            }
+
+            if (longitude < -180 || longitude > 180)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(longitude)} must be between -180 and 180.");
+                return null;
+            }
+
+            if (elevation < -1000 || elevation > 10000)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(elevation)} must be between -1000 and 10000.");
+                return null;
+            }
+
+            if (timeZone < -12 || timeZone > 14)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(timeZone)} must be between -12 and 14.");
+                return null;
+            }
+
+            return new Location()
+            {
+                City = city,
+                State = state,
+                Country = country,
+                Latitude = latitude,
+                Longitude = longitude,
+                TimeZone = timeZone,
+                Elevation = elevation,
+                StationId = stationId,
+                Source = source,
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Engine/Create/Shelter.cs b/LadybugTools_Engine/Create/Shelter.cs
new file mode 100644
index 00000000..01c36b91
--- /dev/null
+++ b/LadybugTools_Engine/Create/Shelter.cs
@@ -0,0 +1,91 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.Engine.Geometry;
+using BH.oM.Base.Attributes;
+using BH.oM.Geometry;
+using BH.oM.LadybugTools;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Linq;
+
+namespace BH.Engine.LadybugTools
+{
+    public static partial class Create
+    {
+        [Description("Create a Shelter object.")]
+        [Input("vertices", "The vertices of the shelter.")]
+        [Input("windPorosity", "The hourly wind porosity of the shelter (0-1, where 0 is completely wind-blocking, and 1 allows all wind to pass through).")]
+        [Input("radiationPorosity", "The hourly radiation porosity of the shelter (0-1, where 0 is completely solar-radiation-blocking, and 1 allows all solar radiation to pass through).")]
+        [Output("shelter", "A Shelter object.")]
+        public static Shelter Shelter(List<Point> vertices, List<double> windPorosity = null, List<double> radiationPorosity = null)
+        {
+            if ((windPorosity.Count() == 0 && windPorosity.Sum() == 0) || windPorosity == null)
+                windPorosity = Enumerable.Repeat(0.0, 8760).ToList();
+
+            if (windPorosity.Count != 8760)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(windPorosity)} must be a list of 8760 values.");
+                return null;
+            }
+            if (windPorosity.Where(x => x < 0 || x > 1).Any())
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(windPorosity)} must be between 0 and 1.");
+                return null;
+            }
+
+            if ((radiationPorosity.Count() == 0 && radiationPorosity.Sum() == 0) || radiationPorosity == null)
+                radiationPorosity = Enumerable.Repeat(0.0, 8760).ToList();
+
+            if (radiationPorosity.Count != 8760)
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(radiationPorosity)} must be a list of 8760 values.");
+                return null;
+            }
+
+            if (radiationPorosity.Where(x => x < 0 || x > 1).Any())
+            {
+                BH.Engine.Base.Compute.RecordError($"{nameof(radiationPorosity)} must be between 0 and 1.");
+                return null;
+            }
+
+            if (radiationPorosity.Sum() + windPorosity.Sum() == 8760 * 2)
+            {
+                BH.Engine.Base.Compute.RecordError("This shelter is completely transmissive and would have no effect.");
+                return null;
+            }
+
+            if (!BH.Engine.Geometry.Create.Polyline(vertices).IsPlanar())
+            {
+                BH.Engine.Base.Compute.RecordError("Shelter vertices are not planar.");
+                return null;
+            }
+
+            return new Shelter()
+            {
+                Vertices = vertices,
+                WindPorosity = windPorosity,
+                RadiationPorosity = radiationPorosity
+            };
+        }
+    }
+}
diff --git a/LadybugTools_Engine/Create/Typology.cs b/LadybugTools_Engine/Create/Typology.cs
index 2410697a..5a228303 100644
--- a/LadybugTools_Engine/Create/Typology.cs
+++ b/LadybugTools_Engine/Create/Typology.cs
@@ -4,20 +4,20 @@
  *
  * Each contributor holds copyright over their respective contributions.
  * The project versioning (Git) records all such contribution source information.
- *
- *
- * The BHoM is free software: you can redistribute it and/or modify
- * it under the terms of the GNU Lesser General Public License as published by
- * the Free Software Foundation, either version 3.0 of the License, or
- * (at your option) any later version.
- *
- * The BHoM is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * GNU Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public License
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
  */
 
 using BH.oM.Base.Attributes;
@@ -30,65 +30,92 @@ namespace BH.Engine.LadybugTools
 {
     public static partial class Create
     {
-        [Description("Create a Typology object for external comfort simulations.")]
-        [Input("shelters", "A list of shelters modifying exposure to the elements.")]
-        [Input("name", "Optional name of this typology.")]
-        [Input("evaporativeCoolingEffect", "An amount of evaporative cooling to add to results calculated by this typology. Defaults to 0, corresponding to no additional water added to the air. A value of 0.1 might represent a nearby body of water, and a value of 0.3 might represent a nearby misting device.")]
-        [Input("windSpeedMultiplier", "A factor to multiply wind speed by. Defaults to 1. Can be used to account for wind speed reduction due to sheltering not accounted for by shelter objects, or to approximate effects of acceleration. Default = 1. Set to 0 for still air conditions.")]
-        [Input("radiantTemperatureAdjustment", "A change in MRT to be applied. Defaults to 0. A positive value will increase the MRT and a negative value will decrease it.")]
-        [Output("typology", "Typology object.")]
-        public static Typology Typology(List<Shelter> shelters = null, string name = "", double evaporativeCoolingEffect = 0, double windSpeedMultiplier = 1, double radiantTemperatureAdjustment = 0)
+        [Description("Create a Typology object.")]
+        [Input("identifier", "The identifier of the typology.")]
+        [Input("shelters", "The shelters of the typology.")]
+        [Input("evaporativeCoolingEffect", "A list of hourly-annual dimensionless values by which to adjust the additional of moisture into the air and modify the dry-bulb temperature and relative humidity values. A value 0 means no additional moisure added to air, wheras a value of 1 results in fully moisture saturated air at 100% relative humidity.")]
+        [Input("targetWindSpeed", "The hourly target wind speed of the typology, in m/s. This can also contain \"null\" values in which case the EPW file used alongside this object and the porosity of the shelters will be used to determine wind speed - otherwise, any value input here will overwrite those calculated wind speeds.")]
+        [Input("radiantTemperatureAdjustment", "A list of values in °C, one-per-hour to adjust the mean radiant temperature by.")]
+        [Output("typology", "A Typology object.")]
+        public static Typology Typology(
+            string identifier = null,
+            List<Shelter> shelters = null,
+            List<double> evaporativeCoolingEffect = null,
+            List<double?> targetWindSpeed = null,
+            List<double> radiantTemperatureAdjustment = null
+        )
         {
-            if (!evaporativeCoolingEffect.IsBetween(0, 1))
+            shelters = shelters ?? new List<Shelter>();
+
+            if ((evaporativeCoolingEffect.Count() == 0 && evaporativeCoolingEffect.Sum() == 0) || evaporativeCoolingEffect == null)
+                evaporativeCoolingEffect = Enumerable.Repeat(0.0, 8760).ToList();
+
+            if (evaporativeCoolingEffect.Count() != 8760)
             {
-                Base.Compute.RecordError($"{nameof(evaporativeCoolingEffect)} must be between 0 and 1, but is currently {evaporativeCoolingEffect}.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(evaporativeCoolingEffect)} must be a list of 8760 values.");
                 return null;
             }
 
-            if (windSpeedMultiplier < 0)
+            if (evaporativeCoolingEffect.Where(x => x < 0 || x > 1).Any())
             {
-                Base.Compute.RecordError($"{nameof(windSpeedMultiplier)} must be greater than or equal to 0, but is currently {windSpeedMultiplier}.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(evaporativeCoolingEffect)} must be between 0 and 1.");
                 return null;
             }
 
-            if (!radiantTemperatureAdjustment.IsBetween(-10, 10))
+            if ((targetWindSpeed.Count() == 0 && targetWindSpeed.Sum() == 0) || targetWindSpeed == null)
+                targetWindSpeed = Enumerable.Repeat<double?>(null, 8760).ToList();
+
+            if (targetWindSpeed.Count() != 8760)
             {
-                Base.Compute.RecordError($"{nameof(radiantTemperatureAdjustment)} must be between -10 and 10, but is currently {radiantTemperatureAdjustment}.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(targetWindSpeed)} must be a list of 8760 values.");
                 return null;
             }
 
-            if (shelters == null)
+            if (targetWindSpeed.Where(x => x != null && x.Value < 0).Any())
             {
-                shelters = new List<Shelter>();
+                BH.Engine.Base.Compute.RecordError($"{nameof(targetWindSpeed)} values must be greater than or equal to 0, or null if not relevant for that hour of the year.");
+                return null;
             }
 
-            string rtnName = name;
-            int shelterCount = shelters.Count(s => s != null);
-            if (rtnName == "" && shelterCount == 0)
+            if ((radiantTemperatureAdjustment.Count() == 0 && radiantTemperatureAdjustment.Sum() == 0) || radiantTemperatureAdjustment == null)
+                radiantTemperatureAdjustment = Enumerable.Repeat(0.0, 8760).ToList();
+
+            if (radiantTemperatureAdjustment.Count() != 8760)
             {
-                rtnName = $"ec{evaporativeCoolingEffect}_ws{windSpeedMultiplier}_mrt{radiantTemperatureAdjustment}";
-                Base.Compute.RecordNote($"This typology has been automatically named {rtnName}. This can be overriden with the 'name' parameter.");
+                BH.Engine.Base.Compute.RecordError($"{nameof(radiantTemperatureAdjustment)} must be a list of 8760 values.");
+                return null;
             }
-            else if (rtnName == "")
+
+            if (identifier == null)
             {
-                rtnName = $"shelters{shelterCount}_ec{evaporativeCoolingEffect}_ws{windSpeedMultiplier}_mrt{radiantTemperatureAdjustment}";
-                Base.Compute.RecordNote($"This typology has been automatically named {rtnName}. This can be overriden with the 'name' parameter.");
+                dynamic targetWindSpeedAvg;
+                if (targetWindSpeed.Where(x => x.HasValue).Count() == 0)
+                {
+                    targetWindSpeedAvg = "EPW";
+                }
+                else
+                {
+                    targetWindSpeedAvg = targetWindSpeed.Where(x => x.HasValue).Average(x => x.Value);
+                }
+                if (shelters.Count() == 0)
+                {
+                    identifier = $"ec{evaporativeCoolingEffect.Average()}_ws{targetWindSpeedAvg}_mrt{radiantTemperatureAdjustment.Average()}";
+                }
+                else
+                {
+                    identifier = $"shelters{shelters.Count()}_ec{evaporativeCoolingEffect.Average()}_ws{targetWindSpeedAvg}_mrt{radiantTemperatureAdjustment.Average()}";
+                }
+                Base.Compute.RecordNote($"This typology has been automatically named \"{identifier}\".");
             }
 
             return new Typology()
             {
-                Name = rtnName,
-                Shelters = shelters.Where(s => s != null).ToList(),
-                EvaporativeCoolingEffect = Enumerable.Repeat(evaporativeCoolingEffect, 8760).ToList(),
-                WindSpeedMultiplier = Enumerable.Repeat(windSpeedMultiplier, 8760).ToList(),
-                RadiantTemperatureAdjustment = Enumerable.Repeat(radiantTemperatureAdjustment, 8760).ToList(),
+                Name = identifier,
+                Shelters = shelters,
+                EvaporativeCoolingEffect = evaporativeCoolingEffect,
+                TargetWindSpeed = targetWindSpeed,
+                RadiantTemperatureAdjustment = radiantTemperatureAdjustment
             };
         }
-
-        private static bool IsBetween(this double val, double min, double max)
-        {
-            return (val >= min && val <= max);
-        }
     }
 }
-
diff --git a/LadybugTools_Engine/LadybugTools_Engine.csproj b/LadybugTools_Engine/LadybugTools_Engine.csproj
index 123004bb..53d6c13b 100644
--- a/LadybugTools_Engine/LadybugTools_Engine.csproj
+++ b/LadybugTools_Engine/LadybugTools_Engine.csproj
@@ -1,39 +1,27 @@
-<?xml version="1.0" encoding="utf-8"?>
-<Project ToolsVersion="15.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
-  <Import Project="$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props" Condition="Exists('$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props')" />
+<Project Sdk="Microsoft.NET.Sdk">
+
   <PropertyGroup>
-    <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
-    <Platform Condition=" '$(Platform)' == '' ">AnyCPU</Platform>
-    <ProjectGuid>{68AB279E-3E50-4EE3-9E9E-C5C100DF5C58}</ProjectGuid>
-    <OutputType>Library</OutputType>
-    <AppDesignerFolder>Properties</AppDesignerFolder>
-    <RootNamespace>BH.Engine.LadybugTools</RootNamespace>
-    <AssemblyName>LadybugTools_Engine</AssemblyName>
-    <TargetFrameworkVersion>v4.7.2</TargetFrameworkVersion>
-    <FileAlignment>512</FileAlignment>
-    <Deterministic>true</Deterministic>
-    <NuGetPackageImportStamp>
-    </NuGetPackageImportStamp>
-    <TargetFrameworkProfile />
-  </PropertyGroup>
-  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' ">
-    <DebugSymbols>true</DebugSymbols>
-    <DebugType>full</DebugType>
-    <Optimize>false</Optimize>
-    <OutputPath>..\Build\</OutputPath>
-    <DefineConstants>DEBUG;TRACE</DefineConstants>
-    <ErrorReport>prompt</ErrorReport>
-    <WarningLevel>4</WarningLevel>
-  </PropertyGroup>
-  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|AnyCPU' ">
-    <DebugType>pdbonly</DebugType>
-    <Optimize>true</Optimize>
+    <TargetFramework>netstandard2.0</TargetFramework>
+    <AssemblyVersion>7.0.0.0</AssemblyVersion>
+    <Description>https://github.com/BHoM/LadybugTools_Toolkit</Description>
+    <Version>6.0.0</Version>
+    <Authors>BHoM</Authors>
+    <Copyright>Copyright © https://github.com/BHoM</Copyright>
+    <RootNamespace>BH.Engine.LadyBugTools</RootNamespace>
+    <FileVersion>7.0.0.0</FileVersion>
     <OutputPath>..\Build\</OutputPath>
-    <DefineConstants>TRACE</DefineConstants>
-    <ErrorReport>prompt</ErrorReport>
-    <WarningLevel>4</WarningLevel>
   </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\LadybugTools_oM\LadybugTools_oM.csproj" />
+  </ItemGroup>
+
   <ItemGroup>
+    <Reference Include="Adapter_Engine">
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Adapter_Engine.dll</HintPath>
+	  <Private>False</Private>
+	  <SpecificVersion>False</SpecificVersion>
+    </Reference>
     <Reference Include="Analytical_oM">
       <HintPath>$(ProgramData)\BHoM\Assemblies\Analytical_oM.dll</HintPath>
       <Private>False</Private>
@@ -69,9 +57,6 @@
       <Private>False</Private>
       <SpecificVersion>False</SpecificVersion>
     </Reference>
-    <Reference Include="Eto, Version=2.5.0.0, Culture=neutral, PublicKeyToken=552281e97c755530, processorArchitecture=MSIL">
-      <HintPath>..\packages\RhinoCommon.6.33.20343.16431\lib\net45\Eto.dll</HintPath>
-    </Reference>
     <Reference Include="Geometry_Engine">
       <HintPath>$(ProgramData)\BHoM\Assemblies\Geometry_Engine.dll</HintPath>
       <Private>False</Private>
@@ -92,88 +77,19 @@
       <Private>False</Private>
       <SpecificVersion>False</SpecificVersion>
     </Reference>
-    <Reference Include="Rhino.UI, Version=6.33.20343.16430, Culture=neutral, PublicKeyToken=552281e97c755530, processorArchitecture=MSIL">
-      <HintPath>..\packages\RhinoCommon.6.33.20343.16431\lib\net45\Rhino.UI.dll</HintPath>
-    </Reference>
-    <Reference Include="Rhinoceros_Engine">
-      <HintPath>$(ProgramData)\BHoM\Assemblies\Rhinoceros_Engine.dll</HintPath>
-      <Private>False</Private>
-      <SpecificVersion>False</SpecificVersion>
-    </Reference>
-    <Reference Include="RhinoCommon, Version=6.33.20343.16430, Culture=neutral, PublicKeyToken=552281e97c755530, processorArchitecture=MSIL">
-      <HintPath>..\packages\RhinoCommon.6.33.20343.16431\lib\net45\RhinoCommon.dll</HintPath>
-    </Reference>
     <Reference Include="Serialiser_Engine">
       <HintPath>$(ProgramData)\BHoM\Assemblies\Serialiser_Engine.dll</HintPath>
       <Private>False</Private>
       <SpecificVersion>False</SpecificVersion>
     </Reference>
-    <Reference Include="System" />
-    <Reference Include="System.ComponentModel.DataAnnotations" />
-    <Reference Include="System.Core" />
-    <Reference Include="System.IO.Compression.FileSystem" />
-    <Reference Include="System.Xml.Linq" />
-    <Reference Include="System.Data.DataSetExtensions" />
-    <Reference Include="Microsoft.CSharp" />
-    <Reference Include="System.Data" />
-    <Reference Include="System.Net.Http" />
-    <Reference Include="System.Xml" />
   </ItemGroup>
-  <ItemGroup>
-    <Compile Include="Compute\GEMtoHBJSON.cs" />
-    <Compile Include="Compute\HBJSONtoGEM.cs" />
-    <Compile Include="Compute\ExternalComfort.cs" />
-    <Compile Include="Compute\InstallPythonEnv_LBT.cs" />
-    <Compile Include="Compute\EPWtoCSV.cs" />
-    <Compile Include="Compute\EPWtoCustomObject.cs" />
-    <Compile Include="Compute\SimulationResult.cs" />
-    <Compile Include="Convert\FromHoneybeeSurface.cs" />
-    <Compile Include="Convert\PascalCaseToSnakeCase.cs" />
-    <Compile Include="Convert\SanitiseString.cs" />
-    <Compile Include="Convert\SnakeCaseToPascalCase.cs" />
-    <Compile Include="Compute\SimulationId.cs" />
-    <Compile Include="Create\Typology.cs" />
-    <Compile Include="Properties\AssemblyInfo.cs" />
-    <Compile Include="Query\GetMaterial.cs" />
-    <Compile Include="Query\GetTypology.cs" />
-    <Compile Include="Query\ToolkitName.cs" />
-  </ItemGroup>
-  <ItemGroup>
-    <None Include="packages.config" />
-    <None Include="Python\README.md" />
-    <Content Include="Python\requirements.txt" />
-  </ItemGroup>
-  <ItemGroup>
-    <ProjectReference Include="..\LadybugTools_oM\LadybugTools_oM.csproj">
-      <Project>{3778EE3E-F314-4EB5-B99F-CF4A8C254E80}</Project>
-      <Name>LadybugTools_oM</Name>
-      <Private>False</Private>
-    </ProjectReference>
-  </ItemGroup>
-  <ItemGroup />
-  <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
-  <PropertyGroup>
-    <PostBuildEvent>
-      xcopy "$(TargetDir)$(TargetFileName)" "C:\\ProgramData\\BHoM\\Assemblies" /Y
-    </PostBuildEvent>
-  </PropertyGroup>
-  <PropertyGroup>
-    <PreBuildEvent>
-      if not exist "C:\ProgramData\BHoM\Extensions\PythonEnvironments" mkdir "C:\ProgramData\BHoM\Extensions\PythonEnvironments"
-      if not exist "C:\ProgramData\BHoM\Extensions\PythonCode" mkdir "C:\ProgramData\BHoM\Extensions\PythonCode"
-      
-      if exist "C:\ProgramData\BHoM\Extensions\PythonCode\$(SolutionName)" rmdir "C:\ProgramData\BHoM\Extensions\PythonCode\$(SolutionName)" /S /Q
-      mkdir "C:\ProgramData\BHoM\Extensions\PythonCode\$(SolutionName)"
 
-      robocopy "$(ProjectDir)Python" "C:\ProgramData\BHoM\Extensions\PythonCode\$(SolutionName)" /mir /xf "*.pyc" "*.ipynb" /xd "__*__" ".*" &gt; output.log
-      del output.log
-    </PreBuildEvent>
-  </PropertyGroup>
-  <Import Project="..\packages\RhinoCommon.6.33.20343.16431\build\net45\RhinoCommon.targets" Condition="Exists('..\packages\RhinoCommon.6.33.20343.16431\build\net45\RhinoCommon.targets')" />
-  <Target Name="EnsureNuGetPackageBuildImports" BeforeTargets="PrepareForBuild">
-    <PropertyGroup>
-      <ErrorText>This project references NuGet package(s) that are missing on this computer. Use NuGet Package Restore to download them.  For more information, see http://go.microsoft.com/fwlink/?LinkID=322105. The missing file is {0}.</ErrorText>
-    </PropertyGroup>
-    <Error Condition="!Exists('..\packages\RhinoCommon.6.33.20343.16431\build\net45\RhinoCommon.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\RhinoCommon.6.33.20343.16431\build\net45\RhinoCommon.targets'))" />
+  <Target Name="PreBuild" BeforeTargets="PreBuildEvent">
+    <Exec Command="xcopy $(ProjectDir)Convert\System.Web.Extensions.dll $(ProgramData)\BHoM\Assemblies /Y&#xD;&#xA;&#xD;&#xA;if not exist $(ProgramData)\BHoM\Extensions\PythonEnvironments mkdir $(ProgramData)\BHoM\Extensions\PythonEnvironments&#xD;&#xA;if not exist $(ProgramData)\BHoM\Extensions\PythonCode mkdir $(ProgramData)\BHoM\Extensions\PythonCode&#xD;&#xA;      &#xD;&#xA;if exist $(ProgramData)\BHoM\Extensions\PythonCode\$(SolutionName) rmdir $(ProgramData)\BHoM\Extensions\PythonCode\$(SolutionName) /S /Q&#xD;&#xA;mkdir $(ProgramData)\BHoM\Extensions\PythonCode\$(SolutionName)&#xD;&#xA;&#xD;&#xA;robocopy $(ProjectDir)Python $(ProgramData)\BHoM\Extensions\PythonCode\$(SolutionName) /mir /xf &quot;*.pyc&quot; &quot;*.ipynb&quot; /xd &quot;__*__&quot; &quot;.*&quot; &gt; output.log&#xD;&#xA;del output.log" />
   </Target>
+
+  <Target Name="PostBuild" AfterTargets="PostBuildEvent">
+    <Exec Command="xcopy $(TargetDir)$(TargetFileName) $(ProgramData)\BHoM\Assemblies /Y" />
+  </Target>
+
 </Project>
\ No newline at end of file
diff --git a/LadybugTools_Engine/Properties/AssemblyInfo.cs b/LadybugTools_Engine/Properties/AssemblyInfo.cs
deleted file mode 100644
index c57002b7..00000000
--- a/LadybugTools_Engine/Properties/AssemblyInfo.cs
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * This file is part of the Buildings and Habitats object Model (BHoM)
- * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
- *
- * Each contributor holds copyright over their respective contributions.
- * The project versioning (Git) records all such contribution source information.
- *                                           
- *                                                                              
- * The BHoM is free software: you can redistribute it and/or modify         
- * it under the terms of the GNU Lesser General Public License as published by  
- * the Free Software Foundation, either version 3.0 of the License, or          
- * (at your option) any later version.                                          
- *                                                                              
- * The BHoM is distributed in the hope that it will be useful,              
- * but WITHOUT ANY WARRANTY; without even the implied warranty of               
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
- * GNU Lesser General Public License for more details.                          
- *                                                                            
- * You should have received a copy of the GNU Lesser General Public License     
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
- */
-
-using System.Reflection;
-using System.Runtime.CompilerServices;
-using System.Runtime.InteropServices;
-
-// General Information about an assembly is controlled through the following
-// set of attributes. Change these attribute values to modify the information
-// associated with an assembly.
-[assembly: AssemblyTitle("LadybugTools_Engine")]
-[assembly: AssemblyDescription("https://github.com/BHoM/LadybugTools_Toolkit")]
-[assembly: AssemblyConfiguration("")]
-[assembly: AssemblyCompany("")]
-[assembly: AssemblyProduct("LadybugTools_Engine")]
-[assembly: AssemblyCopyright("Copyright © https://github.com/BHoM")]
-[assembly: AssemblyTrademark("")]
-[assembly: AssemblyCulture("")]
-
-// Setting ComVisible to false makes the types in this assembly not visible
-// to COM components.  If you need to access a type in this assembly from
-// COM, set the ComVisible attribute to true on that type.
-[assembly: ComVisible(false)]
-
-// The following GUID is for the ID of the typelib if this project is exposed to COM
-[assembly: Guid("68ab279e-3e50-4ee3-9e9e-c5c100df5c58")]
-
-// Version information for an assembly consists of the following four values:
-//
-//      Major Version
-//      Minor Version
-//      Build Number
-//      Revision
-//
-// You can specify all the values or you can default the Build and Revision Numbers
-// by using the '*' as shown below:
-// [assembly: AssemblyVersion("1.0.*")]
-[assembly: AssemblyVersion("6.0.0.0")]
-[assembly: AssemblyFileVersion("6.3.0.0")]
-
-
-
diff --git a/LadybugTools_Engine/Python/.pylintrc b/LadybugTools_Engine/Python/.pylintrc
index 3e9905a2..ca99331c 100644
--- a/LadybugTools_Engine/Python/.pylintrc
+++ b/LadybugTools_Engine/Python/.pylintrc
@@ -2,39 +2,51 @@
 load-plugins=pylint.extensions.docparams
 
 [BASIC]
-
 # Good variable names which should always be accepted, separated by a comma.
 good-names=a, ap, ax, b, c, cb, cc, cf, ch, ci, cl, d, df, dt, e, es, f, fp, fph, g, gs, h, ha, ha, hr, i, id, im, j, k, kw, kw, l, ls, m, ms, n, nn, o, p, pc, pt, q, r, rh, s, sd, sm, t, ta, ti, tk, tr, u, v, va, w, wd, wr, ws, x, xs, xx, xy, y, ys, yy, z, zs
+
+# suppress warnings for specific modules
 ignored-modules=win32api,cv2
 
 [FORMAT]
+# set the maximum number of characters on a single line
 max-line-length=120
 
+# set the maximum number of lines in a module before docstring checks apply
+docstring-min-length=10
+
 [MASTER]
+extension-pkg-whitelist = pydantic
+
+# disable all but a single rule
 ; pylint --disable=all --enable=W0611
 
+# Use multiple processes to speed up Pylint.
+jobs=4
 
+# disable specific warnings
 disable=
     R0902, # too-many-instance-attributes
     R0904, # too-many-public-methods
+    R0903, # too-few-public-methods
     R0912, # too-many-branches
     R0913, # too-many-arguments
     R0914, # too-many-locals
     R0915, # too-many-statements
-    C0103, # invalid-name
-    C0114, # missing-module-docstring
+    ; C0103, # invalid-name
+    ; C0114, # missing-module-docstring
     ; C0115, # missing-class-docstring
-    C0301, # line-too-long
-    C0302, # too-many-lines
+    ; C0301, # line-too-long
+    ; C0302, # too-many-lines
     ; E1101, # no-member
-    ; R0801, # duplicate-code
-    W0212, # protected-access
-    W0511, # fixme
-    W0702, # bare-except
-    W0703, # broad-except
-    W1203, # logging-fstring-interpolation
+    R0801, # duplicate-code
+    ; W0212, # protected-access
+    ; W0511, # fixme
+    ; W0702, # bare-except
+    ; W0703, # broad-except
+    ; W1203, # logging-fstring-interpolation
     ; W9006, # raise-missing-from
     ; W9015, # raise-missing-from
     ; W9017, # differing-param-doc
     ; W9018, # differing-type-doc
-
+    ; W0611, # unused-import
diff --git a/LadybugTools_Engine/Python/README.md b/LadybugTools_Engine/Python/README.md
index aa9e519b..63393eff 100644
--- a/LadybugTools_Engine/Python/README.md
+++ b/LadybugTools_Engine/Python/README.md
@@ -1,3 +1,7 @@
 # LadybugTools_Toolkit
 
-The code contained here is intended to be used in conjunction with the LadybugTools_Toolkit, as part of a wider [BHoM](https://bhom.xyz/) installation. Its main purpose is to augment capabilities of the code provided by [Ladybug Tools](https://github.com/ladybug-tools).
\ No newline at end of file
+The code contained here is intended to be used in conjunction with the LadybugTools_Toolkit, as part of a wider [BHoM](https://bhom.xyz/) installation. Its main purpose is to provide shortcuts to shorten workflows using code provided by [Ladybug Tools](https://github.com/ladybug-tools).
+
+## Installation
+
+For this toolkit to work successfully, [Pollination](https://www.pollination.cloud/) should be installed first, after which the method `BH.Engine.LadybugTools.Compute.InstallPythonEnv_LBT` should be run. This will create a mirror-image of the Pollination Python environment within the BHoM installation directory (`C:\ProgramData\BHoM\Extensions\PythonEnvironments\LadybugTools_Toolkit`) from which the code here can be accessed.
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/requirements.txt b/LadybugTools_Engine/Python/requirements.txt
index 6e27e247..124b61c5 100644
--- a/LadybugTools_Engine/Python/requirements.txt
+++ b/LadybugTools_Engine/Python/requirements.txt
@@ -1,13 +1,18 @@
 case-converter
-ladybug-charts
+meteostat
 openpyxl
 pyarrow
+pyet
 pylint
 pytest
 pytest-cov
 pytest-order
 scikit-learn
 scipy
+Sphinx
+sphinx_bootstrap_theme
+sphinxcontrib-fulltoc
+sphinxcontrib-napoleon
 tables
 tqdm
 xlrd
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/requirements_src.txt b/LadybugTools_Engine/Python/requirements_src.txt
deleted file mode 100644
index 747c8aa1..00000000
--- a/LadybugTools_Engine/Python/requirements_src.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-dragonfly-core
-dragonfly-doe2
-dragonfly-energy
-dragonfly-radiance
-dragonfly-schema
-dragonfly-uwg
-honeybee-core
-honeybee-display
-honeybee-energy
-honeybee-energy-standards
-honeybee-ies
-honeybee-radiance
-honeybee-radiance-command
-honeybee-radiance-folder
-honeybee-radiance-postprocess
-honeybee-schema
-honeybee-standards
-honeybee-vtk
-ladybug-comfort
-ladybug-core
-ladybug-display
-ladybug-geometry
-ladybug-geometry-polyskel
-ladybug-radiance
-ladybug-rhino
-lbt-dragonfly
-lbt-honeybee
-lbt-ladybug
-lbt-recipes
-pollination-handlers
-queenbee
-queenbee-local
-uwg
diff --git a/LadybugTools_Engine/Python/run_coverage.bat b/LadybugTools_Engine/Python/run_coverage.bat
deleted file mode 100644
index 38b264c2..00000000
--- a/LadybugTools_Engine/Python/run_coverage.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-C:\ProgramData\BHoM\Extensions\PythonEnvironments\LadybugTools_Toolkit\Scripts\python.exe -m pytest --cov ladybugtools_toolkit C:\ProgramData\BHoM\Extensions\PythonCode\LadybugTools_Toolkit\tests
-cmd /k
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/run_documentation.bat b/LadybugTools_Engine/Python/run_documentation.bat
new file mode 100644
index 00000000..e39cca93
--- /dev/null
+++ b/LadybugTools_Engine/Python/run_documentation.bat
@@ -0,0 +1,8 @@
+set mypath=%cd%
+cd C:\ProgramData\BHoM\Extensions\PythonCode\LadybugTools_Toolkit\src
+call C:\ProgramData\BHoM\Extensions\PythonEnvironments\LadybugTools_Toolkit\Scripts\activate.bat
+sphinx-apidoc -f -e -d 4 -o ./docs ./ladybugtools_toolkit
+sphinx-build -b html ./docs ./docs/_build/docs
+cd %mypath%
+call C:\ProgramData\BHoM\Extensions\PythonCode\LadybugTools_Toolkit\src\docs\_build\docs\index.html
+cmd /k
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/run_tests.bat b/LadybugTools_Engine/Python/run_tests.bat
index 961efc21..8f595e9b 100644
--- a/LadybugTools_Engine/Python/run_tests.bat
+++ b/LadybugTools_Engine/Python/run_tests.bat
@@ -1,2 +1,7 @@
-C:\ProgramData\BHoM\Extensions\PythonEnvironments\LadybugTools_Toolkit\Scripts\python.exe -m pytest C:\ProgramData\BHoM\Extensions\PythonCode\LadybugTools_Toolkit\tests
+SET PYTHON_EXE=C:\ProgramData\BHoM\Extensions\PythonEnvironments\LadybugTools_Toolkit\Scripts\python.exe
+SET TEST_DIR=C:\ProgramData\BHoM\Extensions\PythonCode\LadybugTools_Toolkit\tests
+
+@REM C:\ProgramData\BHoM\Extensions\PythonEnvironments\LadybugTools_Toolkit\Scripts\python.exe -m pytest --cov-report term --cov-report html:cov_html --cov ladybugtools_toolkit -v C:\ProgramData\BHoM\Extensions\PythonCode\LadybugTools_Toolkit\tests
+
+"%PYTHON_EXE%" -m pytest --cov-report term --cov-report html:cov_html --cov ladybugtools_toolkit -v "%TEST_DIR%"
 cmd /k
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/src/data/ICE_database_sources.xlsx b/LadybugTools_Engine/Python/src/data/ICE_database_sources.xlsx
new file mode 100644
index 00000000..3d1e6483
Binary files /dev/null and b/LadybugTools_Engine/Python/src/data/ICE_database_sources.xlsx differ
diff --git a/LadybugTools_Engine/Python/src/data/vegetation.json b/LadybugTools_Engine/Python/src/data/vegetation.json
new file mode 100644
index 00000000..563bceb8
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/data/vegetation.json
@@ -0,0 +1,204 @@
+{
+    "ACER_PLATANOIDES": {
+        "average_height": 13.0,
+        "average_radius": 5.715,
+        "common_name": "Norway maple",
+        "drop_period": [
+            "2017-10-12 00:00:00",
+            "2017-11-05 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.9,
+        "porosity_leafed": 0.08654791885,
+        "regrowth_period": [
+            "2017-03-26 00:00:00",
+            "2017-04-28 00:00:00"
+        ]
+    },
+    "AESCULUS_HIPPOCASTANUM": {
+        "average_height": 19.0,
+        "average_radius": 6.8575,
+        "common_name": "European horse-chestnut",
+        "drop_period": [
+            "2017-10-11 00:00:00",
+            "2017-12-08 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.85,
+        "porosity_leafed": 0.10719667189999998,
+        "regrowth_period": [
+            "2017-03-26 00:00:00",
+            "2017-04-28 00:00:00"
+        ]
+    },
+    "BETULA_PENDULA": {
+        "average_height": 13.5,
+        "average_radius": 3.0475000000000003,
+        "common_name": "Silver birch",
+        "drop_period": [
+            "2017-10-12 00:00:00",
+            "2017-12-08 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.9,
+        "porosity_leafed": 0.08347785888,
+        "regrowth_period": [
+            "2017-03-26 00:00:00",
+            "2017-04-28 00:00:00"
+        ]
+    },
+    "CARPINUS_BETULUS": {
+        "average_height": 15.0,
+        "average_radius": 5.25,
+        "common_name": "European hornbeam",
+        "drop_period": [
+            "2017-10-11 00:00:00",
+            "2017-12-08 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.95,
+        "porosity_leafed": 0.07943651235,
+        "regrowth_period": [
+            "2017-02-26 00:00:00",
+            "2017-03-21 00:00:00"
+        ]
+    },
+    "FAGUS_SYLVATICA": {
+        "average_height": 19.0,
+        "average_radius": 7.5,
+        "common_name": "European beech",
+        "drop_period": [
+            "2017-12-05 00:00:00",
+            "2017-12-31 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.9,
+        "porosity_leafed": 0.14234635129999998,
+        "regrowth_period": [
+            "2017-05-15 00:00:00",
+            "2017-05-27 00:00:00"
+        ]
+    },
+    "FRAXINUS_AMERICANA": {
+        "average_height": 19.5,
+        "average_radius": 7.619999999999999,
+        "common_name": "White ash",
+        "drop_period": [
+            "2017-10-12 00:00:00",
+            "2017-11-05 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.95,
+        "porosity_leafed": 0.1688392104,
+        "regrowth_period": [
+            "2017-03-26 00:00:00",
+            "2017-04-28 00:00:00"
+        ]
+    },
+    "MAGNOLIA_KOBUS": {
+        "average_height": 10.5,
+        "average_radius": 4.5725,
+        "common_name": "Kobushi magnolia",
+        "drop_period": [
+            "2017-10-11 00:00:00",
+            "2017-12-08 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.95,
+        "porosity_leafed": 0.1522853718,
+        "regrowth_period": [
+            "2017-03-26 00:00:00",
+            "2017-04-28 00:00:00"
+        ]
+    },
+    "MALUS_FLORIBUNDA": {
+        "average_height": 6.8,
+        "average_radius": 3.0475000000000003,
+        "common_name": "Japanese flowering crabapple",
+        "drop_period": [
+            "2017-12-05 00:00:00",
+            "2017-12-31 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.9,
+        "porosity_leafed": 0.2644426818,
+        "regrowth_period": [
+            "2017-02-26 00:00:00",
+            "2017-03-31 00:00:00"
+        ]
+    },
+    "PHOENIX_DACTYLIFERA": {
+        "average_height": 12.5,
+        "average_radius": 3.0,
+        "common_name": "Date palm",
+        "drop_period": [],
+        "deciduous": false,
+        "porosity_bare": 0.5,
+        "porosity_leafed": 0.5,
+        "regrowth_period": []
+    },
+    "PRUNUS_CERASIFERA": {
+        "average_height": 5.25,
+        "average_radius": 3.0475000000000003,
+        "common_name": "Cherry plum",
+        "drop_period": [
+            "2017-12-05 00:00:00",
+            "2017-12-31 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.95,
+        "porosity_leafed": 0.1621928373,
+        "regrowth_period": [
+            "2017-02-26 00:00:00",
+            "2017-03-31 00:00:00"
+        ]
+    },
+    "PRUNUS_SERRULATA": {
+        "average_height": 8.5,
+        "average_radius": 3.0475000000000003,
+        "common_name": "East asian cherry",
+        "drop_period": [
+            "2017-10-11 00:00:00",
+            "2017-12-08 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.9,
+        "porosity_leafed": 0.31095862879999997,
+        "regrowth_period": [
+            "2017-03-26 00:00:00",
+            "2017-04-28 00:00:00"
+        ]
+    },
+    "QUERCUS_PALUSTRIS": {
+        "average_height": 20.5,
+        "average_radius": 5.715,
+        "common_name": "Spanish oak",
+        "drop_period": [
+            "2017-12-05 00:00:00",
+            "2017-12-31 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.85,
+        "porosity_leafed": 0.16041758990000002,
+        "regrowth_period": [
+            "2017-05-15 00:00:00",
+            "2017-05-27 00:00:00"
+        ]
+    },
+    "TILIA_X_EUCHLORA": {
+        "average_height": 13.5,
+        "average_radius": 3.81,
+        "common_name": "Caucasian lime",
+        "drop_period": [
+            "2017-10-12 00:00:00",
+            "2017-11-05 00:00:00"
+        ],
+        "deciduous": true,
+        "porosity_bare": 0.9,
+        "porosity_leafed": 0.052953939559999996,
+        "regrowth_period": [
+            "2017-03-26 00:00:00",
+            "2017-04-28 00:00:00"
+        ]
+    }
+}
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/src/docs/_build/README.md b/LadybugTools_Engine/Python/src/docs/_build/README.md
new file mode 100644
index 00000000..3a642cf9
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/docs/_build/README.md
@@ -0,0 +1 @@
+# documentation
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/__init__.py b/LadybugTools_Engine/Python/src/docs/_build/docs/.nojekyll
similarity index 100%
rename from LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/__init__.py
rename to LadybugTools_Engine/Python/src/docs/_build/docs/.nojekyll
diff --git a/LadybugTools_Engine/Python/src/docs/_build/docs/README.md b/LadybugTools_Engine/Python/src/docs/_build/docs/README.md
new file mode 100644
index 00000000..3a642cf9
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/docs/_build/docs/README.md
@@ -0,0 +1 @@
+# documentation
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci/__init__.py b/LadybugTools_Engine/Python/src/docs/_static/custom.css
similarity index 100%
rename from LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci/__init__.py
rename to LadybugTools_Engine/Python/src/docs/_static/custom.css
diff --git a/LadybugTools_Engine/Python/src/docs/_templates/not_a_layout.html b/LadybugTools_Engine/Python/src/docs/_templates/not_a_layout.html
new file mode 100644
index 00000000..0e240fea
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/docs/_templates/not_a_layout.html
@@ -0,0 +1,110 @@
+{% extends "basic/layout.html" %}
+
+{% if theme_bootstrap_version == "3" %}
+{% set bootstrap_version, navbar_version = "3.3.7", "" %}
+{% set bs_span_prefix = "col-md-" %}
+{% else %}
+{% set bootstrap_version, navbar_version = "2.3.2", "-2" %}
+{% set bs_span_prefix = "span" %}
+{% endif %}
+
+{%- set render_sidebar = (not embedded) and (not theme_nosidebar|tobool) and sidebars %}
+
+{%- set bs_content_width = render_sidebar and "8" or "12"%}
+
+{%- block doctype -%}
+<!DOCTYPE html>
+{%- endblock %}
+
+{# Sidebar: Rework into our Bootstrap nav section. #}
+{% macro navBar() %}
+{% include "navbar" + navbar_version + ".html" %}
+{% endmacro %}
+
+{% if theme_bootstrap_version == "3" %}
+{%- macro bsidebar() %}
+{%- if render_sidebar %}
+<div class="{{ bs_span_prefix }}4">
+    <div id="sidebar" class="bs-sidenav" role="complementary">
+        {%- for sidebartemplate in sidebars %}
+        {%- include sidebartemplate %}
+        {%- endfor %}
+    </div>
+</div>
+{%- endif %}
+{%- endmacro %}
+{% else %}
+{%- macro bsidebar() %}
+{%- if render_sidebar %}
+<div class="{{ bs_span_prefix }}4">
+    <div id="sidebar" class="bs-sidenav well" data-spy="affix">
+        {%- for sidebartemplate in sidebars %}
+        {%- include sidebartemplate %}
+        {%- endfor %}
+    </div>
+</div>
+{%- endif %}
+{%- endmacro %}
+{% endif %}
+
+{%- block extrahead %}
+<meta charset='utf-8'>
+<meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
+<meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1'>
+<meta name="apple-mobile-web-app-capable" content="yes">
+<script type="text/javascript" src="{{ pathto('_static/js/jquery-1.11.0.min.js', 1) }} "></script>
+<script type="text/javascript" src="{{ pathto('_static/js/jquery-fix.js', 1) }} "></script>
+<script type="text/javascript"
+    src="{{ pathto('_static', 1) + '/bootstrap-' + bootstrap_version + '/js/bootstrap.min.js' }} "></script>
+<script type="text/javascript" src="{{ pathto('_static/bootstrap-sphinx.js', 1) }} "></script>
+{% endblock %}
+
+{# Silence the sidebar's, relbar's #}
+{% block header %}{% endblock %}
+{% block relbar1 %}{% endblock %}
+{% block relbar2 %}{% endblock %}
+{% block sidebarsourcelink %}{% endblock %}
+
+{%- block content %}
+{{ navBar() }}
+<div class="container">
+    <div class="row">
+        {%- block sidebar1 %}{{ bsidebar() }}{% endblock %}
+        <div class="body {{ bs_span_prefix }}{{ bs_content_width }} content" role="main">
+            {% block body %}{% endblock %}
+        </div>
+        {% block sidebar2 %} {# possible location for sidebar #} {% endblock %}
+    </div>
+</div>
+{%- endblock %}
+
+{%- block footer %}
+<footer class="footer">
+    <div class="container">
+        <p class="pull-right">
+            <a href="#">Back to top</a>
+            {% if theme_source_link_position == "footer" %}
+            <br />
+            {% include "sourcelink.html" %}
+            {% endif %}
+        </p>
+        <p>
+            {%- if show_copyright %}
+            {%- if hasdoc('copyright') %}
+            {% trans path=pathto('copyright'), copyright=copyright|e %}&copy; <a href="{{ path }}">Copyright</a> {{
+            copyright }}.{% endtrans %}<br />
+            {%- else %}
+            {% trans copyright=copyright|e %}&copy; Copyright {{ copyright }}.{% endtrans %}<br />
+            {%- endif %}
+            {%- endif %}
+            {%- if last_updated %}
+            {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}<br />
+            {%- endif %}
+            {%- if show_sphinx %}
+            {% trans sphinx_version=sphinx_version|e %}Created using <a href="http://sphinx-doc.org/">Sphinx</a> {{
+            sphinx_version }}.{% endtrans %}<br />
+            {%- endif %}
+        </p>
+    </div>
+</footer>
+{%- endblock %}
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/src/docs/conf.py b/LadybugTools_Engine/Python/src/docs/conf.py
new file mode 100644
index 00000000..eb9c3dd4
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/docs/conf.py
@@ -0,0 +1,73 @@
+# -*- coding: utf-8 -*-
+#
+# Configuration file for the Sphinx documentation builder.
+
+# -- Path setup --------------------------------------------------------------
+
+import os
+import re
+import sys
+from datetime import datetime
+
+from sphinx.application import Sphinx
+
+sys.path.insert(0, os.path.abspath("../"))
+
+# -- Project information -----------------------------------------------------
+
+project = "ladybugtools_toolkit"
+copyright = f"{datetime.today().year}, BHoM"
+author = "BHoM"
+release = ""
+version = ""
+
+# -- General configuration ---------------------------------------------------
+
+add_module_names = False
+
+extensions = [
+    "sphinx.ext.napoleon",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.doctest",
+    "sphinx.ext.todo",
+    "sphinx.ext.coverage",
+    "sphinx.ext.imgmath",
+    "sphinx.ext.ifconfig",
+    "sphinx.ext.viewcode",
+    "sphinxcontrib.fulltoc",
+]
+
+templates_path = ["_templates"]
+
+source_suffix = ".rst"
+
+master_doc = "index"
+language = "en"
+exclude_patterns = []
+pygments_style = None
+
+# -- Options for HTML output -------------------------------------------------
+
+# html_theme = 'alabaster'
+html_theme = "classic"
+html_theme_options = {
+    # "collapsiblesidebar": True,
+}
+
+# Stylesheet path
+html_static_path = ["_static"]
+html_sidebars = {"**": ["localtoc.html"]}
+
+# -----------------------------------------------------------------------------
+
+
+def setup(app: Sphinx):
+    """Run custom code with access to the Sphinx application object
+
+    Args:
+        app (Sphinx):
+            The Sphinx application object.
+    """
+
+    # Add custom stylesheet
+    app.add_css_file("custom.css")
diff --git a/LadybugTools_Engine/Python/src/docs/index.rst b/LadybugTools_Engine/Python/src/docs/index.rst
new file mode 100644
index 00000000..64b6d5d3
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/docs/index.rst
@@ -0,0 +1,17 @@
+Welcome to LadybugTools_Toolkit's documentation!
+================================================
+
+LadybugTools_Toolkit is an extension (hiding inside BHoM) to the LadybugTools Python library that simplifies a few processes.
+
+.. toctree::
+   :maxdepth: 3
+   :caption: Contents:
+
+.. include:: modules.rst
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/src/docs/modules.rst b/LadybugTools_Engine/Python/src/docs/modules.rst
new file mode 100644
index 00000000..aee131a9
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/docs/modules.rst
@@ -0,0 +1,7 @@
+ladybugtools_toolkit
+====================
+
+.. toctree::
+   :maxdepth: 3
+
+   ladybugtools_toolkit
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/__init__.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/__init__.py
index bb84c0d1..2513fa5a 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/__init__.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/__init__.py
@@ -1,15 +1,15 @@
-import getpass
+"""Base module for the ladybugtools_toolkit package."""
+# pylint: disable=E0401
+import getpass
 import os
-import sys
 from pathlib import Path
 
+# pylint: disable=E0401
+
 import matplotlib.pyplot as plt
 
 # override "HOME" in case IT has set this to something other than default
 os.environ["HOME"] = (Path("C:/Users/") / getpass.getuser()).as_posix()
 
-# set toolkit name for any logging
-TOOLKIT_NAME = "LadybugTools_Toolkit"
-
 # set plotting style for modules within this toolkit
-plt.style.use(Path(__file__).parent / "bhomutil" / "bhom.mplstyle")
+plt.style.use(Path(__file__).parent / "bhom" / "bhom.mplstyle")
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/__init__.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/__init__.py
new file mode 100644
index 00000000..0a3d66bc
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/__init__.py
@@ -0,0 +1,21 @@
+"""Root for the bhom subpackage."""
+
+from pathlib import Path  # pylint: disable=E0401
+from os import path
+
+from win32api import HIWORD, LOWORD, GetFileVersionInfo
+
+BHOM_ASSEMBLIES_DIRECTORY = Path(path.expandvars("%PROGRAMDATA%/BHoM/Assemblies"))
+BHOM_DIRECTORY = Path(path.expandvars("%PROGRAMDATA%/BHoM"))
+BHOM_LOG_FOLDER = Path(path.expandvars("%PROGRAMDATA%/BHoM/Logs"))
+
+PYTHON_CODE_DIRECTORY = Path(path.expandvars("%PROGRAMDATA%/BHoM/Extensions/PythonCode"))
+PYTHON_ENVIRONMENTS_DIRECTORY = Path(path.expandvars("%PROGRAMDATA%/BHoM/Extensions/PythonEnvironments"))
+
+TOOLKIT_NAME = "LadybugTools_Toolkit"
+
+_file_version_ms = GetFileVersionInfo(
+    (BHOM_ASSEMBLIES_DIRECTORY / "BHoM.dll").as_posix(), "\\"
+)["FileVersionMS"]
+
+BHOM_VERSION = f"{HIWORD(_file_version_ms)}.{LOWORD(_file_version_ms)}"
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/analytics.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/analytics.py
new file mode 100644
index 00000000..c8c9be8b
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/analytics.py
@@ -0,0 +1,95 @@
+"""BHoM analytics decorator."""
+# pylint: disable=E0401
+import inspect
+import json
+import sys
+import uuid
+from functools import wraps
+from typing import Any, Callable
+
+# pylint: enable=E0401
+
+from .logging import ANALYTICS_LOGGER
+from .util import csharp_ticks
+from . import BHOM_VERSION, TOOLKIT_NAME
+
+
+def bhom_analytics() -> Callable:
+    """Decorator for capturing usage data.
+
+    Returns
+    -------
+    Callable
+        The decorated function.
+    """
+
+    def decorator(function: Callable):
+        """A decorator to capture usage data for called methods/functions.
+
+        Arguments
+        ---------
+        function : Callable
+            The function to decorate.
+
+        Returns
+        -------
+        Callable
+            The decorated function.
+        """
+
+        @wraps(function)
+        def wrapper(*args, **kwargs) -> Any:
+            """A wrapper around the function that captures usage analytics."""
+
+            _id = uuid.uuid4()
+
+            # get the data being passed to the function, expected dtype and return type
+            argspec = inspect.getfullargspec(function)[-1]
+            argspec.pop("return", None)
+
+            _args = [f'{{"_t": "{argspec[k]}", "Name": "{k}"}}' for k in argspec.keys()]
+
+            exec_metadata = {
+                "BHoMVersion": BHOM_VERSION,
+                "BHoM_Guid": _id,
+                "CallerName": function.__name__,
+                "ComponentId": _id,
+                "CustomData": {},
+                "Errors": [],
+                "FileId": "",
+                "FileName": "",
+                "Fragments": [],
+                "Name": "",
+                # TODO - get project properties from another function/logging
+                # method (or from BHoM DLL analytics capture ...)
+                "ProjectID": "",
+                "SelectedItem": {
+                    "MethodName": function.__name__,
+                    "Parameters": _args,
+                    "TypeName": f"{function.__module__}.{function.__qualname__}",
+                    "_bhomVersion": BHOM_VERSION,
+                    "_t": "Python",
+                },
+                "Time": {
+                    "$date": csharp_ticks(short=True),
+                },
+                "UI": "Python",
+                "UiVersion": TOOLKIT_NAME,
+                "_t": "BH.oM.UI.UsageLogEntry",
+            }
+
+            try:
+                result = function(*args, **kwargs)
+            except Exception as exc:  # pylint: disable=broad-except
+                exec_metadata["Errors"].extend(sys.exc_info())
+                raise exc
+            finally:
+                ANALYTICS_LOGGER.info(
+                    json.dumps(exec_metadata, default=str, indent=None)
+                )
+
+            return result
+
+        return wrapper
+
+    return decorator
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/bhom.mplstyle b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/bhom.mplstyle
similarity index 97%
rename from LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/bhom.mplstyle
rename to LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/bhom.mplstyle
index 5ab82259..0c1d79cf 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/bhom.mplstyle
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/bhom.mplstyle
@@ -1,3 +1,5 @@
+# Default matplotlib settings for this toolkit.
+
 # Set custom colors. All colors are in web style hex format.
 axes.prop_cycle: cycler('color', ['702F8A', 'E63187', '00A9E0', 'FFCF04', '6CC24E', 'EB671C', '00A499', 'D50032', '24135F', '6D104E', '006DA8', 'D06A13', '5D822D', 'F0AC1B', '1C3660', 'BC204B', '8F72B0', 'FCD16D', '8DB9CA', 'EE7837', 'AFC1A2', 'B72B77', 'A0D2C9', 'E6484D'])
 
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/logging/__init__.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/logging/__init__.py
new file mode 100644
index 00000000..4e9eae56
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/logging/__init__.py
@@ -0,0 +1,2 @@
+from .file import ANALYTICS_LOGGER
+from .console import CONSOLE_LOGGER
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/logging/console.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/logging/console.py
new file mode 100644
index 00000000..d36178b8
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/logging/console.py
@@ -0,0 +1,19 @@
+"""Logging utilities for BHoM analytics."""
+
+# pylint: disable=E0401
+import logging
+import sys
+
+# pylint: enable=E0401
+
+from .. import TOOLKIT_NAME
+
+formatter = logging.Formatter("%(name)s - %(levelname)s - %(message)s")
+handler = logging.StreamHandler(sys.stdout)
+handler.setLevel(logging.DEBUG)
+handler.setFormatter(formatter)
+
+CONSOLE_LOGGER = logging.getLogger(f"{TOOLKIT_NAME}[console]")
+CONSOLE_LOGGER.propagate = False
+CONSOLE_LOGGER.setLevel(logging.DEBUG)
+CONSOLE_LOGGER.addHandler(handler)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/logging/file.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/logging/file.py
new file mode 100644
index 00000000..9c39acc7
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/logging/file.py
@@ -0,0 +1,28 @@
+"""Logging utilities for BHoM analytics."""
+
+# pylint: disable=E0401
+import logging
+from datetime import datetime
+from logging.handlers import RotatingFileHandler
+
+# pylint: enable=E0401
+
+
+from .. import TOOLKIT_NAME, BHOM_LOG_FOLDER
+
+formatter = logging.Formatter("%(message)s")
+handler = RotatingFileHandler(
+    BHOM_LOG_FOLDER / f"{TOOLKIT_NAME}_{datetime.now().strftime('%Y%m%d')}.log",
+    mode="a",
+    maxBytes=25 * 1024 * 1024,  # 25mb max before file overwritten
+    backupCount=1,
+    encoding="utf-8",
+    delay=True,  # wait until all logs collected before writing
+)
+handler.setLevel(logging.DEBUG)
+handler.setFormatter(formatter)
+
+ANALYTICS_LOGGER = logging.getLogger(f"{TOOLKIT_NAME}")
+ANALYTICS_LOGGER.propagate = False
+ANALYTICS_LOGGER.setLevel(logging.DEBUG)
+ANALYTICS_LOGGER.addHandler(handler)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/to_bhom.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/to_bhom.py
new file mode 100644
index 00000000..4b6ea541
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/to_bhom.py
@@ -0,0 +1,155 @@
+"""Convert methods for objects generic to Ladybug."""
+
+from honeybee_energy.material.opaque import EnergyMaterial, EnergyMaterialVegetation
+from ladybug.analysisperiod import AnalysisPeriod
+from ladybug.datacollection import HourlyContinuousCollection
+from ladybug.epw import EPW, Location
+from ladybug.header import DataTypeBase, Header
+from ladybug_geometry.geometry3d.pointvector import Point3D
+
+
+def material_to_bhom(obj: EnergyMaterial | EnergyMaterialVegetation) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+
+    if isinstance(obj, EnergyMaterial):
+        return energymaterial_to_bhom(obj)
+    elif isinstance(obj, EnergyMaterialVegetation):
+        return energymaterialvegetation_to_bhom(obj)
+    else:
+        raise ValueError(
+            f"Unexpected type {type(obj)} for material_to_bhom. "
+            "Expected EnergyMaterial or EnergyMaterialVegetation."
+        )
+
+
+def energymaterial_to_bhom(obj: EnergyMaterial) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+    return {
+        "_t": "BH.oM.LadybugTools.EnergyMaterial",
+        "Type": "EnergyMaterial",
+        "Identifier": obj.identifier,
+        "Roughness": obj.roughness,
+        "Thickness": obj.thickness,
+        "Conductivity": obj.conductivity,
+        "Density": obj.density,
+        "SpecificHeat": obj.specific_heat,
+        "ThermalAbsorptance": obj.thermal_absorptance,
+        "SolarAbsorptance": obj.solar_absorptance,
+        "VisibleAbsorptance": obj.visible_absorptance,
+    }
+
+
+def energymaterialvegetation_to_bhom(obj: EnergyMaterialVegetation) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+    return {
+        "_t": "BH.oM.LadybugTools.EnergyMaterialVegetation",
+        "Type": "EnergyMaterialVegetation",
+        "Identifier": obj.identifier,
+        "Thickness": obj.thickness,
+        "Conductivity": obj.conductivity,
+        "Density": obj.density,
+        "SpecificHeat": obj.specific_heat,
+        "Roughness": obj.roughness,
+        "SoilThermalAbsorptance": obj.soil_thermal_absorptance,
+        "SoilSolarAbsorptance": obj.soil_solar_absorptance,
+        "SoilVisibleAbsorptance": obj.soil_visible_absorptance,
+        "PlantHeight": obj.plant_height,
+        "LeafAreaIndex": obj.leaf_area_index,
+        "LeafReflectivity": obj.leaf_reflectivity,
+        "LeafEmissivity": obj.leaf_emissivity,
+        "MinStomatalResist": obj.min_stomatal_resist,
+    }
+
+
+def point3d_to_bhom(obj: Point3D) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+    return {
+        "_t": "BH.oM.Geometry.Point",
+        "X": obj.x,
+        "Y": obj.y,
+        "Z": obj.z,
+    }
+
+
+def analysisperiod_to_bhom(obj: AnalysisPeriod) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+    return {
+        "_t": "BH.oM.LadybugTools.AnalysisPeriod",
+        "Type": "AnalysisPeriod",
+        "StHour": obj.st_hour,
+        "EndHour": obj.end_hour,
+        "StDay": obj.st_day,
+        "EndDay": obj.end_day,
+        "StMonth": obj.st_month,
+        "EndMonth": obj.end_month,
+        "IsLeapYear": obj.is_leap_year,
+        "Timestep": obj.timestep,
+    }
+
+
+def datatype_to_bhom(obj: DataTypeBase) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+    return {
+        "_t": "BH.oM.LadybugTools.DataType",
+        "Type": "DataType",
+        "Name": obj.to_dict()["name"],
+        "Data_Type": obj.to_dict()["data_type"],
+    }
+
+
+def header_to_bhom(obj: Header) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+    return {
+        "_t": "BH.oM.LadybugTools.Header",
+        "Type": "Header",
+        "DataType": datatype_to_bhom(obj.data_type),
+        "Unit": obj.unit,
+        "AnalysisPeriod": analysisperiod_to_bhom(obj.analysis_period),
+        "Metadata": obj.metadata,
+    }
+
+
+def hourlycontinuouscollection_to_bhom(obj: HourlyContinuousCollection) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+    return {
+        "_t": "BH.oM.LadybugTools.HourlyContinuousCollection",
+        "Type": "HourlyContinuous",
+        "Header": header_to_bhom(obj.header),
+        "Values": obj.values,
+    }
+
+
+def location_to_bhom(obj: Location) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+    return {
+        "_t": "BH.oM.LadybugTools.Location",
+        "Type": "Location",
+        "City": obj.city,
+        "State": obj.state,
+        "Country": obj.country,
+        "Latitude": obj.latitude,
+        "Longitude": obj.longitude,
+        "TimeZone": obj.time_zone,
+        "Elevation": obj.elevation,
+        "StationID": obj.station_id,
+        "Source": obj.source,
+    }
+
+
+def epw_to_bhom(obj: EPW) -> dict:
+    """Convert this object into a BHOM deserialisable dictionary."""
+
+    # call to property required to populate slots
+    obj.dry_bulb_temperature  # pylint: disable=pointless-statement
+
+    collections = []
+    for collection in obj._data:  # pylint: disable=protected-access
+        if isinstance(collection[0], (int, float)):
+            collections.append(hourlycontinuouscollection_to_bhom(collection))
+    return {
+        "_t": "BH.oM.LadybugTools.EPW",
+        "Type": "EPW",
+        "Location": location_to_bhom(obj.location),
+        "Metadata": obj.metadata,
+        "DataCollections": collections,
+    }
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/util.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/util.py
new file mode 100644
index 00000000..3200384c
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/util.py
@@ -0,0 +1,24 @@
+"""General utility functions."""
+# pylint: disable=E0401
+from datetime import datetime
+
+# pylint: enable=E0401
+
+
+def csharp_ticks(date_time: datetime = datetime.utcnow(), short: bool = False) -> int:
+    """Python implementation of C# DateTime.UtcNow.Ticks.
+
+    Args:
+        date_time (datetime, optional): The datetime to convert to ticks. Defaults to datetime.utcnow().
+        short (bool, optional): Whether to return the short ticks. Defaults to False.
+
+    Returns:
+        int: The ticks.
+    """
+
+    _ticks = (date_time - datetime(1, 1, 1)).total_seconds()
+
+    if short:
+        return int(_ticks)
+
+    return int(_ticks * (10**7))
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/__init__.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/__init__.py
similarity index 100%
rename from LadybugTools_Engine/Python/tests/external_comfort/__init__.py
rename to LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/__init__.py
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/epw_to_csv.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/epw_to_csv.py
new file mode 100644
index 00000000..7e091a12
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/epw_to_csv.py
@@ -0,0 +1,43 @@
+"""Method to wrap for conversion of EPW to CSV file."""
+# pylint: disable=C0415,E0401,W0703
+import argparse
+import traceback
+from pathlib import Path
+
+
+def main(epw_file: str, include_additional: bool) -> None:
+    """Create a CSV file version of an EPW."""
+    try:
+        from ladybugtools_toolkit.ladybug_extension.epw import epw_to_dataframe, EPW
+
+        epw = EPW(epw_file)
+        df = epw_to_dataframe(epw=epw, include_additional=include_additional)
+        df.to_csv(Path(epw_file).with_suffix(".csv"))
+
+    except Exception as e:
+        print(e)
+        print(traceback.format_exc())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=(
+            "Given an EPW file path, convert to CSV with optional inclusion of calculated additional data."
+        )
+    )
+    parser.add_argument(
+        "-e",
+        "--epw_file",
+        help="The EPW file to write as a CSV.",
+        type=str,
+        required=True,
+    )
+    parser.add_argument(
+        "-a",
+        "--include_additional",
+        help="Whether to include additional calculated data (such as hourly ground temperature, sky temperature, sun position, ...).",
+        type=bool,
+        required=True,
+    )
+    args = parser.parse_args()
+    main(args.epw_file, args.include_additional)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/external_comfort.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/external_comfort.py
new file mode 100644
index 00000000..d8597ff1
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/external_comfort.py
@@ -0,0 +1,37 @@
+"""Method to wrap for access to pre-defined materials."""
+# pylint: disable=C0415,E0401,W0703
+import argparse
+import traceback
+
+
+def main(json_file: str) -> None:
+    """From a json file represention of an ExternalComfort, run the calculation."""
+    try:
+        from ladybugtools_toolkit.external_comfort._externalcomfortbase import (
+            ExternalComfort,
+        )
+
+        ec = ExternalComfort.from_file(json_file)
+        ec.to_file(json_file)
+
+    except Exception as e:
+        print(e)
+        print(traceback.format_exc())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=(
+            "Given a JSON file containing the string represention of a ExternalComfort object, "
+            "run all calculations Python-side for that object."
+        )
+    )
+    parser.add_argument(
+        "-j",
+        "--json_file",
+        help="The JSON file to convert into a ExternalComfort object Python-side.",
+        type=str,
+        required=True,
+    )
+    args = parser.parse_args()
+    main(args.json_file)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/gem_to_hbjson.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/gem_to_hbjson.py
new file mode 100644
index 00000000..8acaaecc
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/gem_to_hbjson.py
@@ -0,0 +1,34 @@
+"""Method to wrap for conversion of IES GEM to HBJSON file."""
+# pylint: disable=C0415,E0401,W0703
+import argparse
+import traceback
+from pathlib import Path
+
+
+def main(gem_file: str) -> None:
+    """Create a HBJSON file from an IES GEM file."""
+    try:
+        from honeybee_ies.reader import model_from_ies
+
+        gem_file_path = Path(gem_file)
+        model = model_from_ies(gem_file_path.as_posix())
+        model.to_hbjson(folder=gem_file_path.parent.as_posix(), name=gem_file_path.stem)
+
+    except Exception as e:
+        print(e)
+        print(traceback.format_exc())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=("Given a GEM file path, convert to a HBJSON file.")
+    )
+    parser.add_argument(
+        "-g",
+        "--gem_file",
+        help="The GEM file to convert to HBJSON.",
+        type=str,
+        required=True,
+    )
+    args = parser.parse_args()
+    main(args.gem_file)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/get_material.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/get_material.py
new file mode 100644
index 00000000..720c0798
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/get_material.py
@@ -0,0 +1,35 @@
+"""Method to wrap for access to pre-defined materials."""
+# pylint: disable=C0415,E0401,W0703
+import argparse
+import traceback
+import json
+
+
+def main(json_file: str) -> None:
+    """Create a file containing all default materials."""
+    try:
+        from ladybugtools_toolkit.external_comfort.material import Materials
+
+        with open(json_file, "w") as f:
+            json.dump([material.value.to_dict() for material in Materials], f)
+
+    except Exception as e:
+        print(e)
+        print(traceback.format_exc())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=(
+            "Given a JSON file path, write the pre-defined materials for the External Comfort workflow."
+        )
+    )
+    parser.add_argument(
+        "-j",
+        "--json_file",
+        help="The JSON file to write material objects into.",
+        type=str,
+        required=True,
+    )
+    args = parser.parse_args()
+    main(args.json_file)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/get_typology.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/get_typology.py
new file mode 100644
index 00000000..46c7cd30
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/get_typology.py
@@ -0,0 +1,38 @@
+"""Method to wrap for access to pre-defined typologies."""
+# pylint: disable=C0415,E0401,W0703
+import argparse
+import traceback
+import json
+
+
+def main(json_file: str) -> None:
+    """Create a file containing all default typologies."""
+    try:
+        from ladybugtools_toolkit.external_comfort.typology import Typologies
+
+        ds = []
+        for typ in Typologies:
+            ds.append(typ.value.to_dict())
+        with open(json_file, "w") as f:
+            json.dump(ds, f)
+
+    except Exception as e:
+        print(e)
+        print(traceback.format_exc())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=(
+            "Given a JSON file path, write the pre-defined typologies for the External Comfort workflow."
+        )
+    )
+    parser.add_argument(
+        "-j",
+        "--json_file",
+        help="The JSON file to write Typology objects into.",
+        type=str,
+        required=True,
+    )
+    args = parser.parse_args()
+    main(args.json_file)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/hbjson_to_gem.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/hbjson_to_gem.py
new file mode 100644
index 00000000..bae6b2cc
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/hbjson_to_gem.py
@@ -0,0 +1,37 @@
+"""Method to wrap for conversion of HBJSON to GEM file."""
+# pylint: disable=C0415,E0401,W0703
+import argparse
+import traceback
+from pathlib import Path
+
+
+def main(hbjson_file: str) -> None:
+    """Create an IES GEM file from an HBJSON file."""
+    try:
+        from honeybee.model import Model
+        from honeybee_ies.writer import model_to_ies
+
+        hbjson_file_path = Path(hbjson_file)
+        model = Model.from_hbjson(hbjson_file_path.as_posix())
+        model_to_ies(
+            model, folder=hbjson_file_path.parent.as_posix(), name=hbjson_file_path.stem
+        )
+
+    except Exception as e:
+        print(e)
+        print(traceback.format_exc())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=("Given an HBJSON file path, convert to a GEM file.")
+    )
+    parser.add_argument(
+        "-j",
+        "--hbjson_file",
+        help="The HBJSON file to convert to GEM.",
+        type=str,
+        required=True,
+    )
+    args = parser.parse_args()
+    main(args.hbjson_file)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/simulation_result.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/simulation_result.py
new file mode 100644
index 00000000..dd78bd0b
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhom/wrapped/simulation_result.py
@@ -0,0 +1,34 @@
+"""Method to wrap for access to pre-defined materials."""
+# pylint: disable=C0415,E0401,W0703
+import argparse
+import traceback
+
+
+def main(json_file: str) -> None:
+    """From a json file represention of a SimulationResult, run the simulation."""
+    try:
+        from ladybugtools_toolkit.external_comfort._simulatebase import SimulationResult
+
+        res = SimulationResult.from_file(json_file)
+        res.to_file(json_file)
+
+    except Exception as e:  # pylint: disable=W0703
+        print(e)
+        print(traceback.format_exc())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=(
+            "Given a JSON file containing the string represention of a SimulationResult, run the simulation."
+        )
+    )
+    parser.add_argument(
+        "-j",
+        "--json_file",
+        help="The JSON file to convert into a SimulationResult object Python-side.",
+        type=str,
+        required=True,
+    )
+    args = parser.parse_args()
+    main(args.json_file)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/analytics.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/analytics.py
deleted file mode 100644
index 252016a2..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/analytics.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import inspect
-import json
-import sys
-import traceback
-import uuid
-from datetime import datetime, timezone
-from functools import wraps
-from typing import Callable
-
-from .encoder import BHoMEncoder
-from .log import _bhom_version, _console_logger, _file_logger
-
-BHOM_VERSION = _bhom_version()  # defined here to save calling method multiple times
-
-FILE_LOGGER = _file_logger()
-CONSOLE_LOGGER = _console_logger()
-
-
-def bhom_analytics(func: Callable) -> Callable:
-    """The BHoM decorator used to capture usage analytics for called methods/functions."""
-
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        """A wrapper around the function that captures the usage analytics."""
-        _id = uuid.uuid4()
-
-        d = {
-            "BHoMVersion": BHOM_VERSION,
-            "BHoM_Guid": _id,
-            "CallerName": inspect.getmodule(func).__name__,
-            "ComponentId": _id,
-            "CustomData": {},
-            "Errors": [],
-            "FileId": "",
-            "FileName": "",
-            "Fragments": [],
-            "Name": "",
-            "ProjectID": "",
-            "SelectedItem": {
-                "Name": func.__name__,
-                "_bhomVersion": BHOM_VERSION,
-                "_t": "Python",
-            },
-            "Tags": [],
-            "Time": {
-                "$date": datetime.now(tz=timezone.utc),
-            },
-            "UI": "Python",
-            "UiVersion": sys.version,
-            "_bhomVersion": BHOM_VERSION,
-            "_t": "BH.oM.UI.UsageLogEntry",
-        }
-
-        try:
-            result = func(*args, **kwargs)
-            FILE_LOGGER.info(json.dumps(d, sort_keys=True, cls=BHoMEncoder))
-            # CONSOLE_LOGGER.info(json.dumps(d, sort_keys=True, cls=BHoMEncoder))
-        except Exception as exc:
-            d["Errors"].append(traceback.format_exc())
-            FILE_LOGGER.info(json.dumps(d, sort_keys=True, cls=BHoMEncoder))
-            # CONSOLE_LOGGER.info(json.dumps(d, sort_keys=True, cls=BHoMEncoder))
-            raise exc
-
-        return result
-
-    return wrapper
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/bhom_object.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/bhom_object.py
deleted file mode 100644
index bdf5535c..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/bhom_object.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from __future__ import annotations
-
-import json
-import math
-from dataclasses import dataclass, field
-from types import FunctionType, MethodType
-from typing import Any, Dict
-
-import numpy as np
-
-from .analytics import bhom_analytics
-from .encoder import (
-    BHoMEncoder,
-    inf_dtype_to_inf_str,
-    inf_str_to_inf_dtype,
-    keys_to_pascalcase,
-    keys_to_snakecase,
-)
-
-
-def is_decorated(func):
-    """Check if a function is decorated with a BHoM analytics decorator."""
-    return hasattr(func, "__wrapped__") or func.__name__ not in globals()
-
-
-@dataclass(init=True)
-class BHoMObject:
-    """The base object all objects within this toolkit should inherit from."""
-
-    _t: str = field(init=False, repr=False, default="BH.oM.LadybugTools.BHoMObject")
-
-    def __post_init__(self):
-        """Replace each method in class with a decorated version of that method."""
-        if "SpatialComfort" not in str(self):
-            for key in dir(self):
-                if key.startswith("_"):
-                    continue
-                if isinstance(getattr(self, key), (FunctionType, MethodType)):
-                    if is_decorated(getattr(self, key)):
-                        continue
-                    setattr(self, key, bhom_analytics(getattr(self, key)))
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Return the BHoM-flavoured dictionary representation of this object."""
-        dictionary = {}
-        for k, v in self.__dict__.items():
-            if isinstance(getattr(self, k), FunctionType):
-                continue
-            dictionary[k] = v
-
-        dictionary = dict_to_bhom_dict(dictionary)
-        dictionary["_t"] = self._t
-
-        return dictionary
-
-    def to_json(self) -> str:
-        """Return the BHoM-flavoured JSON string representation of this object."""
-        return json.dumps(self.to_dict(), cls=BHoMEncoder)
-
-
-def dict_to_bhom_dict(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-    """Convert a regular Python-flavoured dictionary into a BHoM-flavoured
-    dictionary.
-
-    Args:
-        dictionary (Dict[str, Any]): The dictionary to be BHoM-ified.
-
-    Returns:
-        Dict[str, Any]: The BHoM-flavoured dictionary.
-    """
-
-    return keys_to_pascalcase(inf_dtype_to_inf_str(dictionary))
-
-
-def bhom_dict_to_dict(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-    """Convert a BHoM-flavoured dictionary into a regular Python-flavoured
-    dictionary.
-
-    Args:
-        dictionary (Dict[str, Any]): The dictionary to be Python-ified.
-
-    Returns:
-        Dict[str, Any]: The Python-flavoured dictionary.
-    """
-    d = keys_to_snakecase(inf_str_to_inf_dtype(dictionary))
-
-    # check here to replace "x": { "_t": "BH.XYZ", "_v": [0, ..., n] } with "x": [0, ..., n]
-    #
-    return d
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/encoder.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/encoder.py
deleted file mode 100644
index dfd269a7..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/encoder.py
+++ /dev/null
@@ -1,703 +0,0 @@
-import json
-import math
-from pathlib import Path
-from typing import Any, Dict, List
-
-import numpy as np
-import pandas as pd
-from caseconverter import pascalcase, snakecase
-from honeybee._base import _Base as HB_Base
-from honeybee_energy.material._base import _EnergyMaterialBase
-from ladybug._datacollectionbase import BaseCollection
-from ladybug.epw import EPW, AnalysisPeriod, Header, HourlyContinuousCollection
-from ladybug_geometry.geometry3d.pointvector import Point3D, Vector3D
-
-
-def inf_dtype_to_inf_str(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-    """Convert any values in a dict that are "Infinite" or "-Infinite"
-    into a string equivalent."""
-
-    def to_inf_str(v: Any):
-        if isinstance(v, dict):
-            return inf_dtype_to_inf_str(v)
-        if isinstance(v, (list, tuple, np.ndarray)):
-            return [to_inf_str(item) for item in v]
-        if v in (math.inf, -math.inf):
-            return json.dumps(v)
-        return v
-
-    return {k: to_inf_str(v) for k, v in dictionary.items()}
-
-
-def inf_str_to_inf_dtype(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-    """Convert any values in a dict that are "inf" or "-inf" into a numeric
-    equivalent."""
-    if not isinstance(dictionary, dict):
-        return dictionary
-
-    def to_inf_dtype(v: Any):
-        if isinstance(v, dict):
-            return inf_str_to_inf_dtype(v)
-        if isinstance(v, (list, tuple, np.ndarray)):
-            return [to_inf_dtype(item) for item in v]
-        if v in ("inf", "-inf"):
-            return json.loads(v)
-        return v
-
-    return {k: to_inf_dtype(v) for k, v in dictionary.items()}
-
-
-def keys_to_pascalcase(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-    """Convert all keys in a dictionary into pascalcase."""
-    return {
-        pascalcase(k)
-        if k != "_t"
-        else k: keys_to_pascalcase(v)
-        if isinstance(v, dict)
-        else v
-        for k, v in dictionary.items()
-    }
-
-
-def keys_to_snakecase(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-    """Convert all keys in a dictionary into snakecase."""
-    return {
-        snakecase(k)
-        if k != "_t"
-        else k: keys_to_snakecase(v)
-        if isinstance(v, dict)
-        else v
-        for k, v in dictionary.items()
-    }
-
-
-def fix_bhom_jsondict(dictionary: Dict[str, Any]) -> List[Any]:
-    """Convert any values in a dict that in the form x: {"_t": "...", "_v": [...]} into x: [...]"""
-    if not isinstance(dictionary, dict):
-        return dictionary
-    if dictionary.keys() == {"_t", "_v"}:
-        dictionary = dictionary["_v"]
-        return dictionary
-    if dictionary.keys() == {"BHoM_Guid"}:
-        dictionary = None
-        return dictionary
-    return dictionary
-
-
-class BHoMEncoder(json.JSONEncoder):
-    """A custom encoder for converting objects from this toolkit into serialisable JSON with _t flag where necessary for automatic conversion BHoM-side.
-
-    Args:
-        o (_type_):
-            The object for conversion.
-    """
-
-    # pylint : disable=super-with-arguments;too-many-return-statements
-    def default(self, dct):
-        """_"""
-        # Path encoding
-        if isinstance(dct, Path):
-            return dct.as_posix()
-
-        # NumPy encoding
-        if isinstance(dct, (np.number, np.inexact, np.floating, np.complexfloating)):
-            return float(dct)
-        if isinstance(dct, (np.integer, np.signedinteger, np.unsignedinteger)):
-            return int(dct)
-        if isinstance(dct, (np.character)):
-            return str(dct)
-        if isinstance(dct, np.ndarray):
-            return dct.tolist()
-
-        # Pandas encoding
-        if isinstance(dct, (pd.DataFrame, pd.Series, pd.DatetimeIndex)):
-            return dct.to_dict()
-
-        # Ladybug encoding
-        if isinstance(dct, EPW):
-            return dct.to_dict()
-        if isinstance(dct, BaseCollection):
-            return inf_dtype_to_inf_str(dct.to_dict())
-
-        # Honeybee encoding
-        if isinstance(dct, HB_Base):
-            return dct.to_dict()
-        if isinstance(dct, _EnergyMaterialBase):
-            return dct.to_dict()
-
-        # Ladybug geometry objects
-        if isinstance(dct, Point3D):
-            return {"_t": "BH.oM.Geometry.Point", "X": dct.x, "Y": dct.y, "Z": dct.z}
-        if isinstance(dct, Vector3D):
-            return {"_t": "BH.oM.Geometry.Vector", "X": dct.x, "Y": dct.y, "Z": dct.z}
-        # TODO - Add more conversions for other geometry types into BHoM geometry types
-
-        # # LBTools_toolkit specific objects
-        # if isinstance(dct, OpaqueMaterial):
-        #     return {
-        #         "_t": "BH.oM.LadybugTools.OpaqueMaterial",
-        #         "Identifier": dct.Identifier,
-        #         "Source": dct.Source,
-        #         "Thickness": dct.Thickness,
-        #         "Conductivity": dct.Conductivity,
-        #         "Density": dct.Density,
-        #         "SpecificHeat": dct.SpecificHeat,
-        #         "Roughness": dct.Roughness,
-        #         "SolarAbsorptance": dct.SolarAbsorptance,
-        #         "VisibleAbsorptance": dct.VisibleAbsorptance,
-        #     }
-        # if isinstance(dct, OpaqueVegetationMaterial):
-        #     return {
-        #         "_t": "BH.oM.LadybugTools.OpaqueVegetationMaterial",
-        #         "Identifier": dct.Identifier,
-        #         "Source": dct.Source,
-        #         "Thickness": dct.Thickness,
-        #         "Conductivity": dct.Conductivity,
-        #         "Density": dct.Density,
-        #         "SpecificHeat": dct.SpecificHeat,
-        #         "Roughness": dct.Roughness,
-        #         "SoilThermalAbsorptance": dct.SoilThermalAbsorptance,
-        #         "SoilSolarAbsorptance": dct.SoilSolarAbsorptance,
-        #         "SoilVisibleAbsorptance": dct.SoilVisibleAbsorptance,
-        #         "PlantHeight": dct.PlantHeight,
-        #         "LeafAreaIndex": dct.LeafAreaIndex,
-        #         "LeafReflectivity": dct.LeafReflectivity,
-        #         "LeafEmissivity": dct.LeafEmissivity,
-        #         "MinStomatalResist": dct.MinStomatalResist,
-        #     }
-
-        # Catch-all for any other object that has a "to_dict" method
-        try:
-            return dct.to_dict()
-        except AttributeError:
-            try:
-                return str(dct)
-            except Exception:  # pylint: disable=broad-except
-                pass
-
-        return super(BHoMEncoder, self).default(dct)
-
-    # pylint : enable=super-with-arguments;too-many-return-statements
-
-
-# class BHoMDecoder(json.JSONDecoder):
-#     """A custom encoder for converting objects from BHoM into native objects within this Python package.
-
-#     Args:
-#         o (_type_):
-#             The object for conversion.
-#     """
-
-#     def default(self, dct):
-#         """_"""
-
-#         # handle objectable dicts
-#         if isinstance(dct, dict):
-#             if "_t" in dct.keys():
-#                 # This is a BHoM-style object, and might be convertible into a native object
-#                 if dct["_t"] == "BH.oM.Geometry.Point":
-#                     return Point3D(dct["X"], dct["Y"], dct["Z"])
-#                 if dct["_t"] == "BH.oM.Geometry.Vector":
-#                     return Vector3D(dct["X"], dct["Y"], dct["Z"])
-#                 # if dct["_t"] == "BH.oM.LadybugTools.OpaqueMaterial":
-#                 #     return OpaqueMaterial(
-#                 #         Identifier=dct["Identifier"],
-#                 #         Source=dct["Source"],
-#                 #         Thickness=dct["Thickness"],
-#                 #         Conductivity=dct["Conductivity"],
-#                 #         Density=dct["Density"],
-#                 #         SpecificHeat=dct["SpecificHeat"],
-#                 #         Roughness=dct["Roughness"],
-#                 #         SolarAbsorptance=dct["SolarAbsorptance"],
-#                 #         VisibleAbsorptance=dct["VisibleAbsorptance"],
-#                 #     )
-#                 # if dct["_t"] == "BH.oM.LadybugTools.OpaqueVegetationMaterial":
-#                 #     return OpaqueVegetationMaterial(
-#                 #         Identifier=dct["Identifier"],
-#                 #         Source=dct["Source"],
-#                 #         Thickness=dct["Thickness"],
-#                 #         Conductivity=dct["Conductivity"],
-#                 #         Density=dct["Density"],
-#                 #         SpecificHeat=dct["SpecificHeat"],
-#                 #         Roughness=dct["Roughness"],
-#                 #         SoilThermalAbsorptance=dct["SoilThermalAbsorptance"],
-#                 #         SoilSolarAbsorptance=dct["SoilSolarAbsorptance"],
-#                 #         SoilVisibleAbsorptance=dct["SoilVisibleAbsorptance"],
-#                 #         PlantHeight=dct["PlantHeight"],
-#                 #         LeafAreaIndex=dct["LeafAreaIndex"],
-#                 #         LeafReflectivity=dct["LeafReflectivity"],
-#                 #         LeafEmissivity=dct["LeafEmissivity"],
-#                 #         MinStomatalResist=dct["MinStomatalResist"],
-#                 #     )
-
-#             if "type" in dct.keys():
-#                 # This is a Ladybug-style object, and might be convertible into a native object
-#                 if dct["type"] == "Header":
-#                     return Header.from_dict(dct)
-#                 if dct["type"] == "AnalysisPeriod":
-#                     return AnalysisPeriod.from_dict(dct)
-#                 if dct["type"] == "HourlyContinuous":
-#                     return HourlyContinuousCollection.from_dict(dct)
-
-#         return super(BHoMDecoder, self).default(dct)
-
-
-# """
-# """Encoding methods enabling data to be passed from Python-style LB/HB objects to BHoM objects and vice versa.
-# """
-
-# # pylint : disable=super-with-arguments;too-many-return-statements
-
-# import json
-# import math
-# from datetime import datetime
-# from pathlib import Path
-
-# import numpy as np
-# import pandas as pd
-# from honeybee._base import _Base as HB_Base
-# from honeybee_energy.material._base import _EnergyMaterialBase
-# from ladybug._datacollectionbase import BaseCollection
-# from ladybug.epw import EPW, AnalysisPeriod, Header, HourlyContinuousCollection
-# from ladybug_geometry.geometry2d import LineSegment2D, Point2D, Polyline2D, Vector2D
-# from ladybug_geometry.geometry3d import (
-#     Face3D,
-#     LineSegment3D,
-#     Plane,
-#     Point3D,
-#     Polyline3D,
-#     Vector3D,
-# )
-
-# from .helpers import fix_bhom_jsondict, inf_dtype_to_inf_str, inf_str_to_inf_dtype
-
-
-# class CompositeJsonEncoder:
-#     """
-#     Combine multiple JSON encoders
-#     """
-
-#     def __init__(self, *encoders):
-#         self.encoders = encoders
-#         self.args = ()
-#         self.kwargs = {}
-
-#     def default(self, obj):
-#         """_"""
-#         for encoder in self.encoders:
-#             try:
-#                 return encoder(*self.args, **self.kwargs).default(obj)
-#             except TypeError:
-#                 pass
-#         raise TypeError(
-#             f"Object of type {obj.__class__.__name__} is not JSON serializable"
-#         )
-
-#     def __call__(self, *args, **kwargs):
-#         self.args = args
-#         self.kwargs = kwargs
-#         enc = json.JSONEncoder(*args, **kwargs)
-#         enc.default = self.default
-#         return enc
-
-
-# class CompositeJsonDecoder:
-#     """
-#     Combine multiple JSON decoders
-#     """
-
-#     def __init__(self, *decoders):
-#         self.decoders = decoders
-#         self.args = ()
-#         self.kwargs = {}
-
-#         raise NotImplementedError()
-
-#     def object_hook(self, obj):
-#         """_"""
-#         for decoder in self.decoders:
-#             try:
-#                 return decoder(*self.args, **self.kwargs).object_hook(obj)
-#             except TypeError:
-#                 pass
-#         return obj
-
-#     def __call__(self, *args, **kwargs):
-#         self.args = args
-#         self.kwargs = kwargs
-#         dec = json.JSONDecoder(*args, **kwargs)
-#         dec.object_hook = self.object_hook
-#         return dec
-
-
-# class PathEncoder(json.JSONEncoder):
-#     """A custom encoder for converting pathlib.Path objects into serialisable JSON."""
-
-#     def default(self, o):
-#         """_"""
-#         if isinstance(o, Path):
-#             return o.resolve().as_posix()
-#         return super().default(o)  # pylint: disable=E1101
-
-
-# class PathDecoder(json.JSONDecoder):
-#     """A custom decoder for converting path-like strings into a Path object."""
-
-#     def __init__(self, *args, **kwargs):
-#         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-#     def object_hook(self, obj):
-#         return obj
-
-
-# class DateEncoder(json.JSONEncoder):
-#     """A custom encoder for converting datetime objects into serialisable JSON."""
-
-#     def default(self, o):
-#         """_"""
-#         if isinstance(o, datetime):
-#             return o.isoformat()
-#         return super().default(o)  # pylint: disable=E1101
-
-
-# class DateDecoder(json.JSONDecoder):
-#     """A custom decoder for converting datetime-like strings into a datetime object."""
-
-#     def __init__(self, *args, **kwargs):
-#         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-#     def object_hook(self, obj):
-#         return datetime.fromisoformat(obj)
-
-
-# class NumpyEncoder(json.JSONEncoder):
-#     """A custom encoder for converting numpy objects into serialisable JSON."""
-
-#     def default(self, o):
-#         """_"""
-#         if isinstance(o, (np.number, np.inexact, np.floating, np.complexfloating)):
-#             return float(o)
-#         if isinstance(o, (np.integer, np.signedinteger, np.unsignedinteger)):
-#             return int(o)
-#         if isinstance(o, (np.character)):
-#             return str(o)
-#         if isinstance(o, np.ndarray):
-#             return o.tolist()
-#         return super().default(o)  # pylint: disable=E1101
-
-
-# class NumpyDecoder(json.JSONDecoder):
-#     """A custom decoder for converting numpy-like objects into a numpy object."""
-
-#     def __init__(self, *args, **kwargs):
-#         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-#     def object_hook(self, obj):
-#         return obj
-
-
-# class InfinityEncoder(json.JSONEncoder):
-#     """A custom encoder for converting infinity objects into serialisable JSON."""
-
-#     def default(self, o):
-#         """_"""
-#         if o == math.inf:
-#             return "Infinity"
-#         if o == -math.inf:
-#             return "-Infinity"
-#         return super().default(o)  # pylint: disable=E1101
-
-
-# class InfinityDecoder(json.JSONDecoder):
-#     """A custom decoder for converting infinity-like strings into a infinity object."""
-
-#     def __init__(self, *args, **kwargs):
-#         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-#     def object_hook(self, obj):
-#         """_"""
-#         if obj == "Infinity":
-#             return math.inf
-#         if obj == "-Infinity":
-#             return -math.inf
-#         return obj
-
-
-# class BHoMListEncoder(json.JSONEncoder):
-#     """A custom encoder for converting BHoM objects into serialisable JSON."""
-
-#     def default(self, o):
-#         """_"""
-#         return super().default(o)  # pylint: disable=E1101
-
-
-# class BHOMListDecoder(json.JSONDecoder):
-#     """A custom decoder for converting BHoM-like list objects into a proper Python list object."""
-
-#     def __init__(self, *args, **kwargs):
-#         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-#     def object_hook(self, obj):
-#         """_"""
-#         if isinstance(obj, dict):
-#             if obj.keys() == {"_t", "_v"}:
-#                 return obj["_v"]
-#         return obj
-
-
-# class PandasEncoder(json.JSONEncoder):
-#     """A custom encoder for converting pandas objects into serialisable JSON."""
-
-#     def default(self, o):
-#         """_"""
-#         if isinstance(o, pd.DataFrame):
-#             return {**o.to_dict(), **{"pytype": "pandas.DataFrame"}}
-#         if isinstance(o, pd.Series):
-#             return {**o.to_dict(), **{"pytype": "pandas.Series"}}
-#         return super().default(o)  # pylint: disable=E1101
-
-
-# class PandasDecoder(json.JSONDecoder):
-#     """A custom decoder for converting pandas-like objects into a pandas object."""
-
-#     def __init__(self, *args, **kwargs):
-#         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-#     def object_hook(self, obj):
-#         if "pytype" in obj.keys() and obj["pytype"].startswith("pandas."):
-#             obj.pop("type")
-#             if obj["type"] == "pandas.DataFrame":
-#                 return pd.DataFrame.from_dict(obj)
-#             if obj["type"] == "pandas.Series":
-#                 return pd.Series.from_dict(obj)
-#         return obj
-
-
-# class LadybugEncoder(json.JSONEncoder):
-#     """A custom encoder for converting ladybug datacollection-like objects into serialisable JSON."""
-
-#     def default(self, o):
-#         """_"""
-#         if isinstance(o, EPW):
-#             return o.to_dict()
-#         if isinstance(o, BaseCollection):
-#             return inf_dtype_to_inf_str(o.to_dict())
-#         if isinstance(o, Header):
-#             return o.to_dict()
-#         if isinstance(o, AnalysisPeriod):
-#             return o.to_dict()
-#         return super().default(o)  # pylint: disable=E1101
-
-
-# class LadybugDecoder(json.JSONDecoder):
-#     """A custom decoder for converting ladybug-like objects into a ladybug object."""
-
-#     def __init__(self, *args, **kwargs):
-#         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-#     def object_hook(self, obj):
-#         if "type" in obj.keys():
-#             if obj["type"] == "EPW":
-#                 # fix any inf values
-#                 d = inf_str_to_inf_dtype(obj)
-#                 return EPW.from_dict(d)
-#             if obj["type"] == "Header":
-#                 return Header.from_dict(obj)
-#             if obj["type"] == "AnalysisPeriod":
-#                 return AnalysisPeriod.from_dict(obj)
-#             if obj["type"] == "HourlyContinuous":
-#                 # fix any inf values
-#                 d = inf_str_to_inf_dtype(obj)
-#                 return HourlyContinuousCollection.from_dict(d)
-#         return obj
-
-
-# class LadybugGeometryEncoder(json.JSONEncoder):
-#     """A custom encoder for converting ladybug datacollection-like objects into serialisable JSON for BHoM."""
-
-#     def default(self, o):
-#         """_"""
-#         if isinstance(o, LineSegment2D):
-#             return {
-#                 "_t": "BH.oM.Geometry.Line",
-#                 "Start": o.p1,
-#                 "End": o.p2,
-#             }
-#         if isinstance(o, LineSegment3D):
-#             return {
-#                 "_t": "BH.oM.Geometry.Line",
-#                 "Start": o.p1,
-#                 "End": o.p2,
-#             }
-#         if isinstance(o, Point2D):
-#             return {"_t": "BH.oM.Geometry.Point", "X": o.x, "Y": o.y, "Z": 0}
-#         if isinstance(o, Polyline2D):
-#             return {
-#                 "_t": "BH.oM.Geometry.Polyline",
-#                 "ControlPoints": o.vertices,
-#             }
-#         if isinstance(o, Vector2D):
-#             return {"_t": "BH.oM.Geometry.Vector", "X": o.x, "Y": o.y}
-#         if isinstance(o, Point3D):
-#             return {"_t": "BH.oM.Geometry.Point", "X": o.x, "Y": o.y, "Z": o.z}
-#         if isinstance(o, Vector3D):
-#             return {"_t": "BH.oM.Geometry.Vector", "X": o.x, "Y": o.y, "Z": o.z}
-#         if isinstance(o, Plane):
-#             return {"_t": "BH.oM.Geometry.Plane", "Origin": o.o, "Normal": o.n}
-#         if isinstance(o, Polyline3D):
-#             return {
-#                 "_t": "BH.oM.Geometry.Polyline",
-#                 "ControlPoints": o.vertices,
-#             }
-#         if isinstance(o, Face3D):
-#             _d = {
-#                 "_t": "BH.oM.Geometry.PlanarSurface",
-#                 "ExternalBoundary": Polyline3D.from_array(
-#                     list(o.vertices) + [o.vertices[0]]
-#                 ),
-#                 "InternalBoundaries": [],
-#             }
-#             if o.holes:
-#                 for hole in o.holes:
-#                     _d["InternalBoundaries"].append(
-#                         Polyline3D.from_array(list(hole) + [hole[0]])
-#                     )
-#             return _d
-
-
-# class LadybugGeometryDecoder(json.JSONDecoder):
-#     """A custom decoder for converting ladybug-like objects into a ladybug object."""
-
-#     def __init__(self, *args, **kwargs):
-#         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-#     def object_hook(self, obj):
-#         if "_t" in obj.keys():
-#             if obj["_t"] == "BH.oM.Geometry.Point":
-#                 return Point3D.from_array([obj["X"], obj["Y"], obj["Z"]])
-#             if obj["_t"] == "BH.oM.Geometry.Vector":
-#                 return Vector3D.from_array([obj["X"], obj["Y"], obj["Z"]])
-#             if obj["_t"] == "BH.oM.Geometry.Plane":
-#                 return Plane(o=obj["Origin"], n=obj["Normal"])
-#             if obj["_t"] == "BH.oM.Geometry.Line":
-#                 return LineSegment3D.from_end_points(
-#                     p1=obj["Start"],
-#                     p2=obj["End"],
-#                 )
-#             if obj["_t"] == "BH.oM.Geometry.Polyline":
-#                 return Polyline3D(vertices=obj["ControlPoints"])
-#             if obj["_t"] == "BH.oM.Geometry.PlanarSurface":
-#                 holes = []
-#                 for hole in obj["InternalBoundaries"]:
-#                     holes.append(hole["ControlPoints"])
-#                 return Face3D(boundary=obj["ExternalBoundary"], holes=holes)
-#         return obj
-
-
-# BHoMEncoder = CompositeJsonEncoder(
-#     json.JSONEncoder,
-#     PathEncoder,
-#     DateEncoder,
-#     NumpyEncoder,
-#     PandasEncoder,
-#     LadybugEncoder,
-#     LadybugGeometryEncoder,
-#     InfinityEncoder,
-# )
-
-## helpers
-# import json
-# import math
-# import re
-# from typing import Any, Dict, List
-
-# import numpy as np
-# from caseconverter import pascalcase, snakecase
-
-
-# def is_decorated(func: callable):
-#     """Determine whether a callable function is already decorated."""
-#     return hasattr(func, "__wrapped__") or func.__name__ not in globals()
-
-
-# def is_pascal_case(s: str) -> bool:
-#     """Check whether a string is compliant with PascalCase naming conventions."""
-#     if len(s) == 1:
-#         return s.isupper()
-#     return (
-#         True
-#         if re.match("([A-Z][a-z0-9]+)((\d)|([A-Z0-9][a-z0-9]+))*([A-Z])?", s)
-#         and "_" not in s
-#         else False
-#     )
-
-
-# def inf_dtype_to_inf_str(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-#     """Convert any values in a dict that are "Infinite" or "-Infinite"
-#     into a string equivalent."""
-
-#     def to_inf_str(v: Any):
-#         if isinstance(v, dict):
-#             return inf_dtype_to_inf_str(v)
-#         if isinstance(v, (list, tuple, np.ndarray)):
-#             return [to_inf_str(item) for item in v]
-#         if v in (math.inf, -math.inf):
-#             return json.dumps(v)
-#         return v
-
-#     return {k: to_inf_str(v) for k, v in dictionary.items()}
-
-
-# def inf_str_to_inf_dtype(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-#     """Convert any values in a dict that are "inf" or "-inf" into a numeric
-#     equivalent."""
-#     if not isinstance(dictionary, dict):
-#         return dictionary
-
-#     def to_inf_dtype(v: Any):
-#         if isinstance(v, dict):
-#             return inf_str_to_inf_dtype(v)
-#         if isinstance(v, (list, tuple, np.ndarray)):
-#             return [to_inf_dtype(item) for item in v]
-#         if v in ("inf", "-inf"):
-#             return json.loads(v)
-#         return v
-
-#     return {k: to_inf_dtype(v) for k, v in dictionary.items()}
-
-
-# def keys_to_pascalcase(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-#     """Convert all keys in a dictionary into pascalcase."""
-#     return {
-#         pascalcase(k)
-#         if k != "_t"
-#         else k: keys_to_pascalcase(v)
-#         if isinstance(v, dict)
-#         else v
-#         for k, v in dictionary.items()
-#     }
-
-
-# def keys_to_snakecase(dictionary: Dict[str, Any]) -> Dict[str, Any]:
-#     """Convert all keys in a dictionary into snakecase."""
-#     return {
-#         snakecase(k)
-#         if k != "_t"
-#         else k: keys_to_snakecase(v)
-#         if isinstance(v, dict)
-#         else v
-#         for k, v in dictionary.items()
-#     }
-
-
-# def fix_bhom_jsondict(dictionary: Dict[str, Any]) -> List[Any]:
-#     """Convert any values in a dict in the form x: {"_t": "...", "_v": [...]} into x: [...]"""
-#     if not isinstance(dictionary, dict):
-#         return dictionary
-#     if dictionary.keys() == {"_t", "_v"}:
-#         dictionary = dictionary["_v"]
-#         return dictionary
-#     if dictionary.keys() == {"BHoM_Guid"}:
-#         dictionary = None
-#         return dictionary
-#     return dictionary
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/example_function.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/example_function.py
deleted file mode 100644
index 2eb2b67e..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/example_function.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .analytics import bhom_analytics
-
-
-@bhom_analytics
-def example_function(a: int, b: int) -> int:
-    """A decorated example function, returning a result and adding to the
-    BHoM Analytics log."""
-    return a + b
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/example_object.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/example_object.py
deleted file mode 100644
index 47dfc073..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/example_object.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# pylint: disable=no-member
-
-from .bhom_object import BHoMObject
-from .example_function import example_function
-
-
-class ExampleObject(BHoMObject):
-    """An example object inheriting from BHoMObject."""
-
-    # pylint disable=no-member
-    def __init__(self, a: int):
-        self.a = a
-        super().__init__()
-
-    def example_object_function(self) -> int:
-        """A dummy method used to demonstrate automatic application of BHoM
-        analytics via decoration."""
-        return 12
-
-    def another_example_object_function(self) -> int:
-        """A method demonstrating the calling of another method, accessing the
-        original form, not the wrapped form"""
-        return self.example_object_function.__wrapped__() + 3
-
-    def imported_example_function(self) -> int:
-        """An imported function that was decorated where it was defined. It is
-        wrapped here to avoid double counting it's usage."""
-        return example_function.__wrapped__(1, 2)
-
-    @property
-    def example_property(self) -> str:
-        """An example property."""
-        return "an example property!"
-
-    @classmethod
-    def from_int(cls, num: int):
-        """An example class method."""
-        return cls(num)
-
-    # pylint enable=no-member
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/interface.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/interface.py
deleted file mode 100644
index 4dd47c7c..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/interface.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import tempfile
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-from ladybug.epw import EPW
-
-from ..helpers import figure_to_base64
-from ..ladybug_extension.datacollection import collection_to_series
-from ..plot import heatmap
-
-
-def plot_example(epw_file: Path, output_type: str = "path") -> str:
-    """An example method that generates a matplotlib figure object and
-    returns either a path to a file on disk, or a base64 encoded string of
-    that image."""
-
-    if output_type not in ["path", "base64"]:
-        raise ValueError('output_type must be one of "path" or "base64".')
-
-    series = collection_to_series(EPW(epw_file).dry_bulb_temperature)
-
-    fig, ax = plt.subplots(1, 1)
-    heatmap(series=series, ax=ax)
-
-    if output_type == "path":
-        save_path = Path(tempfile.gettempdir()) / "example.png"
-        fig.savefig(save_path, transparent=True)
-        return save_path.as_posix()
-
-    return figure_to_base64(fig)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/log.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/log.py
deleted file mode 100644
index 17e014ca..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/bhomutil/log.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import logging
-import sys
-from datetime import datetime
-from logging.handlers import RotatingFileHandler
-from pathlib import Path
-
-from win32api import HIWORD, LOWORD, GetFileVersionInfo
-
-
-def _ui_name() -> str:
-    """Return the name of the current Python BHoM toolkit."""
-    return "LadybugTools_Toolkit"
-
-
-def _bhom_dir() -> Path:
-    """Return the root BHoM directory as a Path object."""
-    return Path(r"C:\ProgramData\BHoM")
-
-
-def _log_folder() -> Path:
-    """Return the BHoM Analytics logging directory as a Path object (and create it if it doesnt
-    already exist)."""
-    log_directory = _bhom_dir() / "Logs"
-    log_directory.mkdir(exist_ok=True, parents=True)
-    return log_directory
-
-
-def _bhom_version() -> str:
-    """Return the version of BHoM installed (using the BHoM.dll in the root BHoM directory."""
-    bhom_dll: Path = _bhom_dir() / "Assemblies" / "BHoM.dll"
-    info = GetFileVersionInfo(bhom_dll.as_posix(), "\\")
-    ms = info["FileVersionMS"]
-    ls = info["FileVersionLS"]
-    return f"{HIWORD(ms)}.{LOWORD(ms)}.{HIWORD(ls)}.{LOWORD(ls)}"
-
-
-def _ticks(dt: datetime = datetime.utcnow(), short: bool = False) -> int:
-    """Python implementation of C# DateTime.UtcNow.Ticks."""
-    _ticks = (dt - datetime(1, 1, 1)).total_seconds()
-
-    if short:
-        return int(_ticks)
-
-    return int(_ticks * (10**7))
-
-
-def _console_logger() -> logging.Logger:
-    """Create/return a console logger, for outputting messages to the the console."""
-    logger = logging.getLogger(f"{_ui_name()}_Console")
-    logger.setLevel(logging.DEBUG)
-    logger.propagate = False
-    stream_handler = logging.StreamHandler(sys.stdout)
-    # stream_handler.setFormatter(formatter)
-    stream_format = logging.Formatter("%(name)s - %(levelname)s - %(message)s")
-    stream_handler.setFormatter(stream_format)
-    stream_handler.setLevel(logging.DEBUG)
-
-    logger.addHandler(stream_handler)
-
-    return logger
-
-
-def _file_logger() -> logging.Logger:
-    """Create/return a file logger, for outputting BHoM analytics to a file."""
-    logger = logging.getLogger(f"{_ui_name()}")
-    logger.setLevel(logging.DEBUG)
-    logger.propagate = False
-    file_handler = RotatingFileHandler(
-        _log_folder() / f"Usage_{_ui_name()}_{_ticks(short=True)}.log",
-        encoding="utf-8",
-        mode="a",
-        delay=True,  # wait until all logs collected before writing
-        maxBytes=25 * 1024 * 1024,  # 25mb max before file overwritten
-        backupCount=0,
-    )
-    file_format = logging.Formatter("%(message)s")
-    file_handler.setFormatter(file_format)
-    file_handler.setLevel(logging.DEBUG)
-
-    logger.addHandler(file_handler)
-
-    return logger
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/__init__.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/__init__.py
index c1733133..dc5cad2b 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/__init__.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/__init__.py
@@ -1 +1 @@
-from .categorical import Categorical
+"""Root for the categorical subpackage."""
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/categorical.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/categorical.py
index b02015a4..93e8bba5 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/categorical.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/categorical.py
@@ -1,6 +1,12 @@
+"""Categorical objects for grouping data into bins."""
+# pylint: disable=W0212
+# pylint: disable=E0401
+import calendar
 from dataclasses import dataclass, field
-from typing import Any, Dict, List, Tuple, Union
+from enum import Enum, auto
+from typing import Any
 
+# pylint: enable=E0401
 import numpy as np
 import pandas as pd
 from ladybug.legend import Color
@@ -10,13 +16,16 @@
     BoundaryNorm,
     Colormap,
     ListedColormap,
-    is_color_like,
     to_hex,
     to_rgba,
 )
 from matplotlib.legend import Legend
+import matplotlib.ticker as mticker
 
-from ..helpers import rolling_window
+from ..bhom.analytics import bhom_analytics
+from ..helpers import rolling_window, validate_timeseries
+from ..plot.utilities import contrasting_color
+from ..plot._heatmap import heatmap
 
 
 @dataclass(init=True, repr=True)
@@ -24,20 +33,20 @@ class Categorical:
     """A class to hold categorical data.
 
     Args:
-        bins (Tuple[float], optional):
-            The bin edges for the categories. These are righ-inclusive, with the exception of the first bin which is
+        bins (tuple[float], optional):
+            The bin edges for the categories. These are right-inclusive, with the exception of the first bin which is
             also left-inclusive.
-        bin_names (Tuple[str], optional):
+        bin_names (tuple[str], optional):
             The names of the categories.
-        colors (Tuple[Union[str, Tuple]], optional):
+        colors (tuple[str | tuple], optional):
             The colors of the categories.
         name (str, optional):
             The name of the categories.
     """
 
-    bins: Tuple[float] = field(default_factory=tuple, repr=False)
-    bin_names: Tuple[str] = field(default_factory=tuple)
-    colors: Tuple[str] = field(default_factory=tuple, repr=True)
+    bins: tuple[float] = field(default_factory=tuple, repr=False)
+    bin_names: tuple[str] = field(default_factory=tuple)
+    colors: tuple[str] = field(default_factory=tuple, repr=True)
     name: str = field(default="GenericCategories")
 
     def __post_init__(self):
@@ -47,9 +56,6 @@ def __post_init__(self):
             while len(cycle) < len(self.bins):
                 cycle += cycle
             self.colors = cycle[: len(self.bins) - 1]
-        for color in self.colors:
-            if not is_color_like(color):
-                raise ValueError(f"{color} is not a valid color.")
         self.colors = tuple(to_hex(i, keep_alpha=True) for i in self.colors)
 
         # ensure bin names are valid
@@ -59,7 +65,9 @@ def __post_init__(self):
         # ensure the number of bins, colors and bin names are consistent
         if len(set([len(self.bin_names), len(self.colors), (len(self.bins) - 1)])) > 1:
             raise ValueError(
-                f"The number of colors ({len(self.colors)}) and bin names ({len(self.bin_names)}) must be one less than the number of bins ({len(self.bins) - 1})."
+                f"The number of colors ({len(self.colors)}) and bin names "
+                f"({len(self.bin_names)}) must be one less than the number of "
+                f"bins ({len(self.bins) - 1})."
             )
 
     def __repr__(self):
@@ -81,19 +89,19 @@ def __getitem__(self, item):
     @classmethod
     def from_cmap(
         cls,
-        bins: Tuple[float],
+        bins: tuple[float],
         cmap: Colormap,
-        bin_names: Tuple[str] = (),
+        bin_names: tuple[str] = (),
         name: str = "",
     ):
         """Create a categorical from a colormap.
 
         Args:
-            bins (Tuple[float]):
+            bins (tuple[float]):
                 The bin edges for the categories.
             cmap (Colormap):
                 The colormap to use.
-            bin_names (Tuple[str], optional):
+            bin_names (tuple[str], optional):
                 The names for each of the bins. Defaults to () which names each bin using its boundaries.
             name (str, optional):
                 The name for this categories object. Defaults to "" which uses the colormap name.
@@ -130,14 +138,22 @@ def interval_index(self) -> pd.IntervalIndex:
         return pd.IntervalIndex.from_breaks(self.bins)
 
     @property
-    def descriptions(self) -> List[str]:
+    def descriptions(self) -> list[str]:
         """The descriptions of the categories.
 
         Returns:
             List[str]:
                 The descriptions of the categories.
         """
-        return [f"{i.left}<x<={i.right}" for i in self]
+        return [f"{i.left} to {i.right}" for i in self]
+
+    @property
+    def bin_names_detailed(self) -> list[str]:
+        """The detailed bin names."""
+        return [
+            f"{nom} ({desc})"
+            for desc, nom in list(zip(*[self.descriptions, self.bin_names]))
+        ]
 
     @property
     def cmap(self) -> Colormap:
@@ -152,20 +168,13 @@ def cmap(self) -> Colormap:
 
         if np.isinf(self.bins[0]) and np.isinf(self.bins[-1]):
             colors = self.colors[1:-1]
-            # above_color = self.colors[-1]
-            # below_color = self.colors[0]
         elif np.isinf(self.bins[0]) and not np.isinf(self.bins[-1]):
             colors = self.colors[1:]
-            # above_color = self.colors[-1]
-            # below_color = self.colors[0]
         elif not np.isinf(self.bins[0]) and np.isinf(self.bins[-1]):
             colors = self.colors[:-1]
-            # above_color = self.colors[-1]
-            # below_color = self.colors[0]
         else:
             colors = self.colors
-            # above_color = self.colors[-1]
-            # below_color = self.colors[0]
+        colors = [to_rgba(i) for i in colors]
 
         cmap = ListedColormap(colors=colors, name=self.name)
         cmap.set_over(self.colors[-1])
@@ -174,7 +183,7 @@ def cmap(self) -> Colormap:
 
     @property
     def norm(self) -> BoundaryNorm:
-        """Return the boundary-norm associate with this comfort metric.
+        """Return the boundary-norm associate with this categorical.
 
         Returns:
             BoundaryNorm:
@@ -189,14 +198,28 @@ def norm(self) -> BoundaryNorm:
             boundaries = boundaries[:-1]
         else:
             pass
+        if len(boundaries) == 1:
+            raise ValueError(
+                "The current Categorical object has unbounded edges and cannot be used to create a BoundaryNorm."
+            )
         return BoundaryNorm(boundaries=boundaries, ncolors=self.cmap.N)
 
     @property
-    def lb_colors(self) -> Tuple[Color]:
+    def bins_finite(self) -> tuple[float]:
+        """The finite bins excluding any which are infinite.
+
+        Returns:
+            Tuple[float]:
+                The finite bins.
+        """
+        return tuple(i for i in self.bins if not np.isinf(i))
+
+    @property
+    def lb_colors(self) -> tuple[Color]:
         """The ladybug colors.
 
         Returns:
-            Tuple[Color]:
+            tuple[Color]:
                 The ladybug color objects.
         """
         return tuple(
@@ -207,25 +230,36 @@ def lb_colors(self) -> Tuple[Color]:
         )
 
     @property
-    def _bin_name_interval(self) -> Dict[str, pd.Interval]:
+    def _bin_name_interval(self) -> dict[str, pd.Interval]:
         """The bin name to interval dictionary.
 
         Returns:
-            Dict[str, pd.Interval]:
+            dict[str, pd.Interval]:
                 The bin name to interval dictionary.
         """
         return dict(zip(self.bin_names, self.interval_index))
 
     @property
-    def _interval_bin_name(self) -> Dict[pd.Interval, str]:
+    def _detailed_bin_name_interval(self) -> dict[str, pd.Interval]:
+        """The bin name to interval dictionary.
+
+        Returns:
+            dict[str, pd.Interval]:
+                The bin name to interval dictionary.
+        """
+        return dict(zip(self.bin_names_detailed, self.interval_index))
+
+    @property
+    def _interval_bin_name(self) -> dict[pd.Interval, str]:
         """The interval to bin name dictionary.
 
         Returns:
-            Dict[pd.Interval, str]:
+            dict[pd.Interval, str]:
                 The interval to bin name dictionary.
         """
         return dict(zip(self.interval_index, self.bin_names))
 
+    @bhom_analytics()
     def interval_from_bin_name(self, bin_name: str) -> pd.Interval:
         """Return the interval from the bin name.
 
@@ -237,8 +271,12 @@ def interval_from_bin_name(self, bin_name: str) -> pd.Interval:
             pd.Interval:
                 The interval associated with the bin name.
         """
-        return self._bin_name_interval[bin_name]
+        try:
+            return self._bin_name_interval[bin_name]
+        except KeyError:
+            return self._detailed_bin_name_interval[bin_name]
 
+    @bhom_analytics()
     def bin_name_from_interval(self, interval: pd.Interval) -> str:
         """Return the bin name from the interval.
 
@@ -252,6 +290,7 @@ def bin_name_from_interval(self, interval: pd.Interval) -> str:
         """
         return self._interval_bin_name[interval]
 
+    @bhom_analytics()
     def color_from_bin_name(self, bin_name: str) -> str:
         """Return the color from the bin name.
 
@@ -265,11 +304,12 @@ def color_from_bin_name(self, bin_name: str) -> str:
         """
         return dict(zip(self.bin_names, self.colors))[bin_name]
 
-    def get_color(self, value: Union[float, int], as_array: bool = False) -> str:
+    @bhom_analytics()
+    def get_color(self, value: float | int, as_array: bool = False) -> str:
         """Return the color associated with the categorised value.
 
         Args:
-            value (Union[float, int]):
+            value (float | int):
                 The value to get the color for.
             as_array (bool, optional):
                 Whether to return the color as an array or a hex string.
@@ -280,13 +320,15 @@ def get_color(self, value: Union[float, int], as_array: bool = False) -> str:
         """
         if value <= self.bins[0] or value > self.bins[-1]:
             raise ValueError(
-                f"The input value/s are outside the range of the categories ({min(self).left} < x <= {max(self).right})."
+                "The input value/s are outside the range of the categories "
+                f"({min(self).left} < x <= {max(self).right})."
             )
         color = self.cmap(self.norm(value))
         if not as_array:
-            return to_hex(color)
+            return to_hex(color, keep_alpha=True)
         return color
 
+    @bhom_analytics()
     def categorise(self, data: Any) -> pd.Categorical:
         """Categorise the data.
 
@@ -299,7 +341,7 @@ def categorise(self, data: Any) -> pd.Categorical:
                 The categorised data.
         """
         categorical = pd.cut(
-            data, self.bins, labels=self.bin_names, include_lowest=True
+            data, self.bins, labels=self.bin_names_detailed, include_lowest=True
         )
         if categorical.isna().any():
             raise ValueError(
@@ -307,6 +349,7 @@ def categorise(self, data: Any) -> pd.Categorical:
             )
         return categorical
 
+    @bhom_analytics()
     def value_counts(
         self,
         data: Any,
@@ -324,11 +367,12 @@ def value_counts(
             pd.Series:
                 The number of counts within each categorical bin.
         """
-        result = self.categorise(data).value_counts()[list(self.bin_names)]
+        result = self.categorise(data).value_counts()[list(self.bin_names_detailed)]
         if density:
             return result / len(data)
         return result
 
+    @bhom_analytics()
     def timeseries_summary_monthly(
         self, series: pd.Series, density: bool = False
     ) -> pd.DataFrame:
@@ -344,6 +388,7 @@ def timeseries_summary_monthly(
             pd.DataFrame:
                 A table summary of the categories over each month
         """
+
         if not isinstance(series, pd.Series):
             raise ValueError("The series must be a pandas series.")
 
@@ -352,12 +397,14 @@ def timeseries_summary_monthly(
 
         counts = (
             self.categorise(series).groupby(series.index.month).value_counts().unstack()
-        ).sort_index(axis=0)[list(self.bin_names)]
+        ).sort_index(axis=0)
+        counts.columns.name = None
         counts.index.name = "Month"
         if density:
             return counts.div(counts.sum(axis=1), axis=0)
         return counts
 
+    @bhom_analytics()
     def summarise(
         self,
         data: Any,
@@ -376,16 +423,17 @@ def summarise(
         result_density = result / result.sum()
 
         statements = []
-        for desc, (idx, val) in list(zip(*[self.bin_names, result.iteritems()])):
+        for desc, (idx, val) in list(zip(*[self.bin_names, result.items()])):
             statements.append(
-                f'"{desc}" occurs {val} times ({result_density[idx]:0.1%}*{len(data)}).'
+                f'"{desc}" occurs {val} times ({result_density[idx]:0.1%}).'
             )
         return "\n".join(statements)
 
+    @bhom_analytics()
     def create_legend(
         self, ax: plt.Axes = None, verbose: bool = True, **kwargs
     ) -> Legend:
-        """Create a legend for this categoical.
+        """Create a legend for this categorical.
 
         Args:
             ax (plt.Axes, optional):
@@ -418,3 +466,193 @@ def create_legend(
             labels.append(description if verbose else iidx)
         lgd = ax.legend(handles=handles, labels=labels, **kwargs)
         return lgd
+
+    @bhom_analytics()
+    def annual_monthly_histogram(
+        self,
+        series: pd.Series,
+        ax: plt.Axes = None,
+        show_legend: bool = False,
+        show_labels: bool = False,
+        **kwargs,
+    ) -> plt.Axes:
+        """Create a monthly histogram of a pandas Series.
+
+        Args:
+            series (pd.Series):
+                The pandas Series to plot. Must have a datetime index.
+            ax (plt.Axes, optional):
+                An optional plt.Axes object to populate. Defaults to None, which creates a new plt.Axes object.
+            show_legend (bool, optional):
+                Whether to show the legend. Defaults to False.
+            show_labels (bool, optional):
+                Whether to show the labels on the bars. Defaults to False.
+            **kwargs:
+                Additional keyword arguments to pass to plt.bar.
+
+        Returns:
+            plt.Axes:
+                The populated plt.Axes object.
+        """
+
+        validate_timeseries(series)
+
+        if ax is None:
+            ax = plt.gca()
+
+        t = self.timeseries_summary_monthly(series, density=True)
+        t.plot(
+            ax=ax,
+            kind="bar",
+            stacked=True,
+            color=self.colors,
+            width=kwargs.pop("width", 1),
+            legend=False,
+            **kwargs,
+        )
+        ax.set_xlim(-0.5, len(t) - 0.5)
+        ax.set_ylim(0, 1)
+        ax.set_xticklabels(
+            [calendar.month_abbr[int(i._text)] for i in ax.get_xticklabels()],
+            ha="center",
+            rotation=0,
+        )
+        for spine in ["top", "right", "left", "bottom"]:
+            ax.spines[spine].set_visible(False)
+        ax.yaxis.set_major_formatter(mticker.PercentFormatter(1))
+
+        if show_legend:
+            ax.legend(
+                bbox_to_anchor=(1, 1),
+                loc="upper left",
+                borderaxespad=0.0,
+                frameon=False,
+                title=self.name,
+            )
+
+        if show_labels:
+            for i, c in enumerate(ax.containers):
+                label_colors = [contrasting_color(i.get_facecolor()) for i in c.patches]
+                labels = [
+                    f"{v.get_height():0.1%}" if v.get_height() > 0.15 else "" for v in c
+                ]
+                ax.bar_label(
+                    c,
+                    labels=labels,
+                    label_type="center",
+                    color=label_colors[i],
+                    fontsize="x-small",
+                )
+
+        return ax
+
+    @bhom_analytics()
+    def annual_heatmap(
+        self, series: pd.Series, ax: plt.Axes = None, **kwargs
+    ) -> plt.Axes:
+        """Create a heatmap showing the annual hourly categorical assignment for the given series.
+
+        Args:
+            series (pd.Series):
+                A time-indexed pandas Series object.
+            ax (plt.Axes, optional):
+                A matplotlib Axes object to plot on. Defaults to None.
+            **kwargs:
+                Additional keyword arguments to pass to the heatmap function.
+
+        Returns:
+            plt.Axes:
+                A matplotlib Axes object.
+        """
+
+        validate_timeseries(series)
+
+        if ax is None:
+            ax = plt.gca()
+
+        heatmap(
+            series,
+            ax=ax,
+            cmap=self.cmap,
+            norm=self.norm,
+            extend="both",
+            **kwargs,
+        )
+
+        return ax
+
+
+class ComfortClass(Enum):
+    """Thermal comfort categories."""
+
+    TOO_COLD = auto()
+    COMFORTABLE = auto()
+    TOO_HOT = auto()
+
+    @property
+    def color(self) -> str:
+        """Get the associatd color."""
+        d = {
+            ComfortClass.TOO_COLD: "#3C65AF",
+            ComfortClass.COMFORTABLE: "#2EB349",
+            ComfortClass.TOO_HOT: "#C31F25",
+        }
+        return d[self]
+
+    @property
+    def text(self) -> str:
+        """Get the associated text."""
+        d = {
+            ComfortClass.TOO_COLD: "Too cold",
+            ComfortClass.COMFORTABLE: "Comfortable",
+            ComfortClass.TOO_HOT: "Too hot",
+        }
+        return d[self]
+
+
+@dataclass(init=True, repr=True)
+class CategoricalComfort(Categorical):
+    """A class to hold categorical comfort data.
+
+    Args:
+        comfort_classes (tuple[ComfortClass]):
+            The comfort classes to use.
+    """
+
+    comfort_classes: tuple[ComfortClass] = field(default_factory=tuple, repr=False)
+
+    def __post_init__(self):
+        if len(self.comfort_classes) == 0:
+            raise ValueError("The comfort classes cannot be empty.")
+        if len(self.comfort_classes) != len(self):
+            raise ValueError(
+                "The number of comfort classes must match the number of bins."
+            )
+        return super().__post_init__()
+
+    @bhom_analytics()
+    def simplify(self) -> "CategoricalComfort":
+        """Return a simplified version of this object based on the assigned comfort clases.
+
+        Returns:
+            CategoricalComfort:
+                The simplified categorical comfort.
+        """
+        d = {}
+        for comfort_class, bin_left in list(zip(*[self.comfort_classes, self.bins])):
+            if comfort_class in d:
+                continue
+            d[comfort_class] = bin_left
+        if len(d.keys()) != len(ComfortClass):
+            raise ValueError(
+                "The comfort classes must include all comfort classes "
+                f"{[i.name for i in ComfortClass]}."
+            )
+
+        return CategoricalComfort(
+            bins=list(d.values()) + [self.bins[-1]],
+            bin_names=[i.text for i in ComfortClass],
+            colors=[i.color for i in ComfortClass],
+            name=self.name + " (simplified)",
+            comfort_classes=list(ComfortClass),
+        )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/categories.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/categories.py
index 24632d49..ce08e198 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/categories.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/categories.py
@@ -1,31 +1,21 @@
-from enum import Enum
-
+"""Predetermined categories for categorical data."""
 import numpy as np
 
-from . import Categorical
-
-
-class ComfortClass(Enum):
-    """Thermal comfort categories."""
-
-    TOO_COLD = "Too cold"
-    COMFORTABLE = "Comfortable"
-    TOO_HOT = "Too hot"
-
+from .categorical import Categorical, CategoricalComfort, ComfortClass
 
-UTCI_SIMPLIFIED_CATEGORIES = Categorical(
+UTCI_SIMPLIFIED_CATEGORIES = CategoricalComfort(
     bins=(-np.inf, 9, 26, np.inf),
     bin_names=("Too cold", "Comfortable", "Too hot"),
     colors=("#3C65AF", "#2EB349", "#C31F25"),
     name="UTCI (simplified)",
+    comfort_classes=[
+        ComfortClass.TOO_COLD,
+        ComfortClass.COMFORTABLE,
+        ComfortClass.TOO_HOT,
+    ],
 )
-UTCI_SIMPLIFIED_CATEGORIES.comfort_classes = [
-    ComfortClass.TOO_COLD,
-    ComfortClass.COMFORTABLE,
-    ComfortClass.TOO_HOT,
-]
 
-UTCI_DEFAULT_CATEGORIES = Categorical(
+UTCI_DEFAULT_CATEGORIES = CategoricalComfort(
     bin_names=(
         "Extreme cold stress",
         "Very strong cold stress",
@@ -52,21 +42,21 @@ class ComfortClass(Enum):
         "#580002",
     ),
     name="UTCI",
+    comfort_classes=[
+        ComfortClass.TOO_COLD,
+        ComfortClass.TOO_COLD,
+        ComfortClass.TOO_COLD,
+        ComfortClass.TOO_COLD,
+        ComfortClass.TOO_COLD,
+        ComfortClass.COMFORTABLE,
+        ComfortClass.TOO_HOT,
+        ComfortClass.TOO_HOT,
+        ComfortClass.TOO_HOT,
+        ComfortClass.TOO_HOT,
+    ],
 )
-UTCI_DEFAULT_CATEGORIES.comfort_classes = [
-    ComfortClass.TOO_COLD,
-    ComfortClass.TOO_COLD,
-    ComfortClass.TOO_COLD,
-    ComfortClass.TOO_COLD,
-    ComfortClass.TOO_COLD,
-    ComfortClass.COMFORTABLE,
-    ComfortClass.TOO_HOT,
-    ComfortClass.TOO_HOT,
-    ComfortClass.TOO_HOT,
-    ComfortClass.TOO_HOT,
-]
 
-UTCI_SLIGHTHEATSTRESS_CATEGORIES = Categorical(
+UTCI_SLIGHTHEATSTRESS_CATEGORIES = CategoricalComfort(
     bin_names=(
         "Extreme cold stress",
         "Very strong cold stress",
@@ -94,21 +84,21 @@ class ComfortClass(Enum):
         "#7E1416",
         "#500003",
     ),
-    name="UTCI (inc. slight heat stress)",
+    name="UTCI inc. slight heat stress",
+    comfort_classes=[
+        ComfortClass.TOO_COLD,
+        ComfortClass.TOO_COLD,
+        ComfortClass.TOO_COLD,
+        ComfortClass.TOO_COLD,
+        ComfortClass.TOO_COLD,
+        ComfortClass.COMFORTABLE,
+        ComfortClass.TOO_HOT,
+        ComfortClass.TOO_HOT,
+        ComfortClass.TOO_HOT,
+        ComfortClass.TOO_HOT,
+        ComfortClass.TOO_HOT,
+    ],
 )
-UTCI_SLIGHTHEATSTRESS_CATEGORIES.comfort_classes = [
-    ComfortClass.TOO_COLD,
-    ComfortClass.TOO_COLD,
-    ComfortClass.TOO_COLD,
-    ComfortClass.TOO_COLD,
-    ComfortClass.TOO_COLD,
-    ComfortClass.COMFORTABLE,
-    ComfortClass.TOO_HOT,
-    ComfortClass.TOO_HOT,
-    ComfortClass.TOO_HOT,
-    ComfortClass.TOO_HOT,
-    ComfortClass.TOO_HOT,
-]
 
 BEAUFORT_CATEGORIES = Categorical(
     bin_names=(
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/__init__.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/__init__.py
index 0f04212c..f1055b51 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/__init__.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/__init__.py
@@ -1,76 +1,67 @@
-# pylint: skip-file
+"""Root package for external_comfort."""
+# pylint: disable=E0401
+# import getpass
+# from pathlib import Path
 
-import getpass
+# pylint: enable=E0401
 
-from honeybee.config import folders as hb_folders
-from honeybee_energy.config import folders as hbe_folders
-from honeybee_radiance.config import folders as hbr_folders
+# from honeybee.config import folders as hb_folders
+# from honeybee_energy.config import folders as hbe_folders
+# from honeybee_radiance.config import folders as hbr_folders
 
-USER = getpass.getuser()
-hb_folders.default_simulation_folder = f"C:/Users/{USER}/simulation"
 
-# SET HONEYBEE ENERGY PATHS
-hbe_folders._openstudio_csharp_path = (
-    "C:/Program Files/ladybug_tools/openstudio/CSharp/openstudio"
-)
-hbe_folders._openstudio_path = "C:/Program Files/ladybug_tools/openstudio/bin"
-hbe_folders._openstudio_exe = (
-    "C:/Program Files/ladybug_tools/openstudio/bin/openstudio.exe"
-)
-hbe_folders.energyplus_path = "C:/Program Files/ladybug_tools/openstudio/EnergyPlus"
-hbe_folders._energyplus_exe = (
-    "C:/Program Files/ladybug_tools/openstudio/EnergyPlus/energyplus.exe"
-)
-hbe_folders._openstudio_results_measure_path = (
-    "C:/Program Files/ladybug_tools/resources/measures/openstudio_results"
-)
-hbe_folders._view_data_measure_path = (
-    "C:/Program Files/ladybug_tools/resources/measures/view_data"
-)
-hbe_folders._inject_idf_measure_path = (
-    "C:/Program Files/ladybug_tools/resources/measures/inject_idf"
-)
-hbe_folders.lbt_measures_path = "C:/Program Files/ladybug_tools/resources/measures"
-hbe_folders._honeybee_adapter_path = "C:/Program Files/ladybug_tools/resources/measures/honeybee_openstudio_gem/lib/files/honeybee_adapter.rb"
-hbe_folders.honeybee_openstudio_gem_path = (
-    "C:/Program Files/ladybug_tools/resources/measures/honeybee_openstudio_gem/lib"
-)
-hbe_folders._construction_lib = (
-    f"C:/Users/{USER}/AppData/Roaming/ladybug_tools/standards/constructions"
-)
-hbe_folders._constructionset_lib = (
-    f"C:/Users/{USER}/AppData/Roaming/ladybug_tools/standards/constructionsets"
-)
-hbe_folders._schedule_lib = (
-    "C:/Users/tgerrish/AppData/Roaming/ladybug_tools/standards/schedules"
-)
-hbe_folders._programtype_lib = (
-    f"C:/Users/{USER}/AppData/Roaming/ladybug_tools/standards/programtypes"
-)
-hbe_folders.standards_data_folder = (
-    f"C:/Users/{USER}/AppData/Roaming/ladybug_tools/standards"
-)
-hbe_folders.defaults_file = "C:/Program Files/ladybug_tools/resources/standards/honeybee_standards/energy_default.json"
-hbe_folders.standards_extension_folders = [
-    "C://Program Files//ladybug_tools//resources//standards//honeybee_energy_standards"
-]
-HBE_FOLDERS = hbe_folders
+# USER = getpass.getuser()
+# LB_FOLDER = Path("C:/Program Files/ladybug_tools")
+# ROAMING_FOLDER = Path(f"C:/Users/{USER}/AppData/Roaming/ladybug_tools")
+# hb_folders.default_simulation_folder = Path(f"C:/Users/{USER}/simulation")
 
-# SET HONEYBEE RADIANCE PATHS
-hbr_folders._radbin_path = "C:/Program Files/ladybug_tools/radiance/bin"
-hbr_folders.radiance_path = "C:/Program Files/ladybug_tools/radiance"
-hbr_folders._radlib_path = "C:/Program Files/ladybug_tools/radiance/lib"
-hbr_folders._modifier_lib = (
-    f"C:/Users/{USER}/AppData/Roaming/ladybug_tools/standards/modifiers"
-)
-hbr_folders._modifierset_lib = (
-    f"C:/Users/{USER}/AppData/Roaming/ladybug_tools/standards/modifiersets"
-)
-hbr_folders._standards_data_folder = (
-    f"C:/Users/{USER}/AppData/Roaming/ladybug_tools/standards"
-)
-hbr_folders.defaults_file = "C:/Program Files/ladybug_tools/resources/standards/honeybee_standards/radiance_default.json"
-HBR_FOLDERS = hbr_folders
+# # pytlint: disable=W0212
 
-# SET QUEENBEE PATH
-QUEENBEE_PATH = "C:/Program Files/ladybug_tools/python/Scripts/queenbee.exe"
+# hbe_folders._openstudio_csharp_path = LB_FOLDER / "openstudio/CSharp/openstudio"
+# hbe_folders._openstudio_path = LB_FOLDER / "openstudio/bin"
+# hbe_folders._openstudio_exe = LB_FOLDER / "openstudio/bin/openstudio.exe"
+# hbe_folders.energyplus_path = LB_FOLDER / "openstudio/EnergyPlus"
+# hbe_folders._energyplus_exe = LB_FOLDER / "openstudio/EnergyPlus/energyplus.exe"
+# hbe_folders._openstudio_results_measure_path = (
+#     LB_FOLDER / "resources/measures/openstudio_results"
+# )
+# hbe_folders._view_data_measure_path = LB_FOLDER / "resources/measures/view_data"
+# hbe_folders._inject_idf_measure_path = LB_FOLDER / "resources/measures/inject_idf"
+# hbe_folders.lbt_measures_path = LB_FOLDER / "resources/measures"
+# hbe_folders._honeybee_adapter_path = (
+#     LB_FOLDER
+#     / "resources/measures/honeybee_openstudio_gem/lib/files/honeybee_adapter.rb"
+# )
+# hbe_folders.honeybee_openstudio_gem_path = (
+#     LB_FOLDER / "resources/measures/honeybee_openstudio_gem/lib"
+# )
+# hbe_folders._construction_lib = ROAMING_FOLDER / "standards/constructions"
+# hbe_folders._constructionset_lib = ROAMING_FOLDER / "standards/constructionsets"
+# hbe_folders._schedule_lib = (
+#     "C:/Users/tgerrish/AppData/Roaming/ladybug_tools/standards/schedules"
+# )
+# hbe_folders._programtype_lib = ROAMING_FOLDER / "standards/programtypes"
+# hbe_folders.standards_data_folder = ROAMING_FOLDER / "standards"
+# hbe_folders.defaults_file = (
+#     LB_FOLDER / "resources/standards/honeybee_standards/energy_default.json"
+# )
+# hbe_folders.standards_extension_folders = [
+#     LB_FOLDER / "resources/standards/honeybee_energy_standards"
+# ]
+# HBE_FOLDERS = hbe_folders
+
+# hbr_folders._radbin_path = LB_FOLDER / "radiance/bin"
+# hbr_folders.radiance_path = LB_FOLDER / "radiance"
+# hbr_folders._radlib_path = LB_FOLDER / "radiance/lib"
+# hbr_folders._modifier_lib = ROAMING_FOLDER / "standards/modifiers"
+# hbr_folders._modifierset_lib = ROAMING_FOLDER / "standards/modifiersets"
+# hbr_folders._standards_data_folder = ROAMING_FOLDER / "standards"
+# hbr_folders.defaults_file = (
+#     LB_FOLDER / "resources/standards/honeybee_standards/radiance_default.json"
+# )
+# HBR_FOLDERS = hbr_folders
+
+# # SET QUEENBEE PATH
+# QUEENBEE_PATH = LB_FOLDER / "python/Scripts/queenbee.exe"
+
+# # pytlint: enable=W0212
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_externalcomfortbase.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_externalcomfortbase.py
new file mode 100644
index 00000000..2f25f02f
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_externalcomfortbase.py
@@ -0,0 +1,498 @@
+"""Base class for external comfort objects."""
+# pylint: disable=E0401
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+# pylint: enable=E0401
+from caseconverter import pascalcase
+import matplotlib.pyplot as plt
+import pandas as pd
+from ladybug.epw import AnalysisPeriod, HourlyContinuousCollection
+from ladybug_comfort.collection.pmv import PMV
+from ladybug_comfort.collection.utci import UTCI
+from matplotlib.figure import Figure
+
+from ..bhom.logging import CONSOLE_LOGGER
+from ..bhom.to_bhom import hourlycontinuouscollection_to_bhom
+from ..categorical.categories import UTCI_DEFAULT_CATEGORIES, Categorical
+from ..helpers import convert_keys_to_snake_case
+from ..ladybug_extension.analysisperiod import describe_analysis_period
+from ..ladybug_extension.datacollection import collection_to_series
+from ..plot._heatmap import heatmap
+from ..plot._utci import utci_day_comfort_metrics, utci_heatmap_histogram
+from ..plot.colormaps import (
+    DBT_COLORMAP,
+    MRT_COLORMAP,
+    RH_COLORMAP,
+    UTCI_DISTANCE_TO_COMFORTABLE,
+    WS_COLORMAP,
+)
+from ._simulatebase import SimulationResult
+from .typology import Typology, Typologies
+from .utci import distance_to_comfortable
+
+
+_ATTRIBUTES = [
+    "dry_bulb_temperature",
+    "relative_humidity",
+    "wind_speed",
+    "mean_radiant_temperature",
+    "universal_thermal_climate_index",
+]
+
+
+@dataclass(init=True, repr=True, eq=True)
+class ExternalComfort:
+    """_"""
+
+    simulation_result: SimulationResult
+    typology: Typology
+
+    dry_bulb_temperature: HourlyContinuousCollection = None
+    relative_humidity: HourlyContinuousCollection = None
+    wind_speed: HourlyContinuousCollection = None
+    mean_radiant_temperature: HourlyContinuousCollection = None
+    universal_thermal_climate_index: HourlyContinuousCollection = None
+
+    def __post_init__(self):
+        """_"""
+
+        # validation
+        if not isinstance(self.simulation_result, SimulationResult):
+            raise ValueError(
+                "simulation_result must be an instance of SimulationResult."
+            )
+
+        if isinstance(self.typology, Typologies):
+            self.typology = self.typology.value
+        if not isinstance(self.typology, Typology):
+            raise ValueError("typology must be an instance of Typology.")
+
+        for attr in _ATTRIBUTES:
+            if not isinstance(
+                getattr(self, attr), (HourlyContinuousCollection, type(None))
+            ):
+                raise ValueError(
+                    f"{attr} must be an instance of HourlyContinuousCollection or None."
+                )
+
+        CONSOLE_LOGGER.info(
+            f"Processing {self.simulation_result.model.identifier} - {self.typology.identifier}"
+        )
+
+        # run calculation and populate object with results if not already done
+        _epw = self.simulation_result.epw
+
+        if not getattr(self, "dry_bulb_temperature"):
+            setattr(
+                self, "dry_bulb_temperature", self.typology.dry_bulb_temperature(_epw)
+            )
+
+        if not getattr(self, "relative_humidity"):
+            setattr(self, "relative_humidity", self.typology.relative_humidity(_epw))
+
+        if not getattr(self, "wind_speed"):
+            setattr(self, "wind_speed", self.typology.wind_speed(_epw))
+
+        if not getattr(self, "mean_radiant_temperature"):
+            setattr(
+                self,
+                "mean_radiant_temperature",
+                self.typology.mean_radiant_temperature(self.simulation_result),
+            )
+
+        if not getattr(self, "universal_thermal_climate_index"):
+            setattr(
+                self,
+                "universal_thermal_climate_index",
+                UTCI(
+                    air_temperature=self.dry_bulb_temperature,
+                    rel_humidity=self.relative_humidity,
+                    rad_temperature=self.mean_radiant_temperature,
+                    wind_speed=self.wind_speed,
+                ).universal_thermal_climate_index,
+            )
+
+        # add some accessors for collections as series
+        for attr in _ATTRIBUTES:
+            setattr(self, f"{attr}_series", collection_to_series(getattr(self, attr)))
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({self.simulation_result}, {self.typology})"
+
+    def to_dict(self) -> str:
+        """Convert this object to a dictionary."""
+        attr_dict = {}
+        for attr in _ATTRIBUTES:
+            if getattr(self, attr):
+                attr_dict[attr] = getattr(self, attr).to_dict()
+
+        d = {
+            **{
+                "type": "ExternalComfort",
+                "simulation_result": self.simulation_result.to_dict(),
+                "typology": self.typology.to_dict(),
+            },
+            **attr_dict,
+        }
+        return d
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "ExternalComfort":
+        """Create a dictionary from this object."""
+        if isinstance(d["simulation_result"], dict):
+            d["simulation_result"] = SimulationResult.from_dict(d["simulation_result"])
+
+        if isinstance(d["typology"], dict):
+            d["typology"] = Typology.from_dict(d["typology"])
+
+        for attr in _ATTRIBUTES:
+            if d.get(attr, None):
+                if isinstance(d[attr], dict):
+                    d[attr] = HourlyContinuousCollection.from_dict(d[attr])
+                else:
+                    d[attr] = None
+            else:
+                d[attr] = None
+
+        return cls(
+            simulation_result=d["simulation_result"],
+            typology=d["typology"],
+            dry_bulb_temperature=d["dry_bulb_temperature"],
+            relative_humidity=d["relative_humidity"],
+            wind_speed=d["wind_speed"],
+            mean_radiant_temperature=d["mean_radiant_temperature"],
+            universal_thermal_climate_index=d["universal_thermal_climate_index"],
+        )
+
+    def to_json(self) -> str:
+        """Convert this object to a JSON string."""
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_string: str) -> "SimulationResult":
+        """Create this object from a JSON string."""
+
+        return cls.from_dict(json.loads(json_string))
+
+    def to_file(self, path: Path) -> Path:
+        """Write this object to a JSON file."""
+
+        if Path(path).suffix != ".json":
+            raise ValueError("path must be a JSON file.")
+
+        with open(Path(path), "w") as fp:
+            fp.write(self.to_json())
+
+        return Path(path)
+
+    @classmethod
+    def from_file(cls, path: Path) -> "SimulationResult":
+        """Create this object from a JSON file."""
+
+        with open(Path(path), "r") as fp:
+            return cls.from_json(fp.read())
+
+    def standard_effective_temperature(
+        self,
+        met_rate: float = 1.1,
+        clo_value: float = 0.7,
+        return_comfort_obj: bool = False,
+    ) -> PMV | HourlyContinuousCollection:
+        """Return the predicted mean vote for the given typology
+            following a simulation of the collections necessary to calculate
+            this.
+
+        Args:
+            met_rate (float, optional):
+                Metabolic rate value.
+            clo_value (float, optional):
+                Clothing value.
+            return_comfort_obj (bool, optional):
+                Set to True to return the PMV comfort object instead of the
+                data collection.
+
+        Returns:
+            [PMV, HourlyContinuousCollection]:
+                The resultant PMV for this Typology, in the given
+                SimulationResult location.
+        """
+        CONSOLE_LOGGER.info(
+            f"{self.__class__.__name__} - calculating standard effective temperature"
+        )
+        pmv = PMV(
+            air_temperature=self.dry_bulb_temperature,
+            rel_humidity=self.relative_humidity,
+            rad_temperature=self.mean_radiant_temperature,
+            air_speed=self.wind_speed,
+            met_rate=met_rate,
+            clo_value=clo_value,
+        )
+
+        if return_comfort_obj:
+            return pmv
+
+        return pmv.standard_effective_temperature
+
+    def description(self) -> str:
+        """Return a description of this external comfort object."""
+        return (
+            f"{self.simulation_result.description(include_shade_material=bool(self.typology.shelters))}"
+            f"\n{self.typology.identifier}"
+        )
+
+    def to_dataframe(self) -> pd.DataFrame:
+        """Return a dataframe of all data for this typology."""
+
+        simulation_result_df = self.simulation_result.to_dataframe()
+        obj_df = pd.concat(
+            [
+                pd.concat(
+                    [
+                        collection_to_series(self.dry_bulb_temperature),
+                        collection_to_series(self.relative_humidity),
+                        collection_to_series(self.mean_radiant_temperature),
+                        collection_to_series(self.wind_speed),
+                        pd.Series(
+                            self.typology.evaporative_cooling_effect,
+                            index=simulation_result_df.index,
+                            name="Evaporative Cooling Effect (0-1)",
+                        ),
+                        pd.Series(
+                            self.typology.radiant_temperature_adjustment,
+                            index=simulation_result_df.index,
+                            name="Radiant Temperature Adjustment (C)",
+                        ),
+                        collection_to_series(self.universal_thermal_climate_index),
+                    ],
+                    axis=1,
+                )
+            ],
+            axis=1,
+            keys=[self.__class__.__name__],
+        )
+
+        return pd.concat([simulation_result_df, obj_df], axis=1)
+
+    def plot_utci_day_comfort_metrics(
+        self, ax: plt.Axes = None, month: int = 3, day: int = 21
+    ) -> plt.Axes:
+        """Plot a single day UTCI and composite components
+
+        Args:
+            ax (plt.Axes, optional):
+                A matplotlib Axes object to plot on. Defaults to None.
+            month (int, optional):
+                The month to plot. Defaults to 3.
+            day (int, optional):
+                The day to plot. Defaults to 21.
+
+        Returns:
+            Axes: A figure showing UTCI and component parts for the given day.
+        """
+
+        return utci_day_comfort_metrics(
+            utci=collection_to_series(self.universal_thermal_climate_index),
+            dbt=collection_to_series(self.dry_bulb_temperature),
+            mrt=collection_to_series(self.mean_radiant_temperature),
+            rh=collection_to_series(self.relative_humidity),
+            ws=collection_to_series(self.wind_speed),
+            ax=ax,
+            month=month,
+            day=day,
+            title=self.description(),
+        )
+
+    def plot_utci_heatmap(
+        self,
+        ax: plt.Axes = None,
+        utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
+    ) -> plt.Axes:
+        """Create a heatmap showing the annual hourly UTCI values associated with this Typology.
+
+        Args:
+            ax (plt.Axes, optional): A matplotlib Axes object to plot on. Defaults to None.
+            utci_categories (Categorical, optional): The UTCI categories to use. Defaults to
+                UTCI_DEFAULT_CATEGORIES.
+
+        Returns:
+            plt.Axes: A matplotlib Axes object.
+        """
+
+        return utci_categories.annual_heatmap(
+            series=collection_to_series(self.universal_thermal_climate_index),
+            ax=ax,
+            title=self.description(),
+        )
+
+    def plot_utci_heatmap_histogram(
+        self, utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES, **kwargs
+    ) -> plt.Figure:
+        """Create a heatmap showing the annual hourly UTCI values associated with this Typology.
+
+        Args:
+            utci_categories (Categorical, optional): The UTCI categories to use. Defaults to
+                UTCI_DEFAULT_CATEGORIES.
+            **kwargs:
+                Additional keyword arguments to pass to the heatmap function.
+        Returns:
+            Figure: A matplotlib Figure object.
+        """
+
+        return utci_heatmap_histogram(
+            utci_collection=self.universal_thermal_climate_index,
+            utci_categories=utci_categories,
+            title=self.description(),
+            **kwargs,
+        )
+
+    def plot_utci_histogram(
+        self,
+        ax: plt.Axes = None,
+        utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
+        analysis_period: AnalysisPeriod = AnalysisPeriod(),
+        title: str = None,
+        **kwargs,
+    ) -> plt.Axes:
+        """Create a histogram showing the annual hourly UTCI values associated with this Typology.
+
+        Args:
+            ax (plt.Axes, optional):
+                A matplotlib Axes object to plot on. Defaults to None.
+            utci_categories (Categorical, optional):
+                The UTCI categories to use. Defaults to UTCI_DEFAULT_CATEGORIES.
+            analysis_period (AnalysisPeriod, optional):
+                The analysis period to filter the results by. Defaults to AnalysisPeriod().
+            title (str, optional):
+                The title to use for the plot. Defaults to None which generates a title from the
+                Typology and AnalysisPeriod.
+            **kwargs:
+                Additional keyword arguments to pass to the histogram function.
+        Returns:
+            plt.Axes:
+                A matplotlib Axes object.
+        """
+
+        if title is None:
+            title = (
+                f"{self.description()} ({describe_analysis_period(analysis_period)})"
+            )
+
+        return utci_categories.annual_monthly_histogram(
+            series=collection_to_series(
+                self.universal_thermal_climate_index.filter_by_analysis_period(
+                    analysis_period
+                )
+            ),
+            ax=ax,
+            title=title,
+            **kwargs,
+        )
+
+    def plot_utci_distance_to_comfortable(
+        self,
+        ax: plt.Axes = None,
+        comfort_thresholds: tuple[float] = (9, 26),
+        distance_to_comfort_band_centroid: bool = True,
+    ) -> Figure:
+        """Create a heatmap showing the "distance" in C from the "no thermal stress" UTCI comfort
+            band.
+
+        Args:
+            ax (plt.Axes, optional): A matplotlib Axes object to plot on. Defaults to None.
+            comfort_thresholds (List[float], optional): The comfortable band of UTCI temperatures.
+                Defaults to [9, 26].
+            distance_to_comfort_band_centroid (bool, optional): Set to True to calculate the
+                distance to the centroid of the comfort band. Defaults to True.
+
+        Returns:
+            Axes: A matplotlib Axes object.
+        """
+
+        new_collection = distance_to_comfortable(
+            utci_value=self.universal_thermal_climate_index,
+            comfort_thresholds=comfort_thresholds,
+            distance_to_comfort_band_centroid=distance_to_comfort_band_centroid,
+        )
+
+        return heatmap(
+            collection_to_series(new_collection),
+            ax=ax,
+            cmap=UTCI_DISTANCE_TO_COMFORTABLE,
+            title=f"{self.description()}\nDistance to comfortable",
+            vmin=-10,
+            vmax=10,
+        )
+
+    def plot_dbt_heatmap(self, **kwargs) -> plt.Axes:
+        """Create a heatmap showing the annual hourly DBT values associated with this Typology.
+
+        Args:
+            **kwargs:
+                Additional keyword arguments to pass to the heatmap function.
+
+        Returns:
+            Figure: A matplotlib Figure object.
+        """
+
+        return heatmap(
+            series=collection_to_series(self.dry_bulb_temperature),
+            cmap=DBT_COLORMAP,
+            title=self.description(),
+            **kwargs,
+        )
+
+    def plot_rh_heatmap(self, **kwargs) -> plt.Axes:
+        """Create a heatmap showing the annual hourly RH values associated with this Typology.
+
+        Args:
+            **kwargs:
+                Additional keyword arguments to pass to the heatmap function.
+
+        Returns:
+            Figure: A matplotlib Figure object.
+        """
+
+        return heatmap(
+            series=collection_to_series(self.relative_humidity),
+            cmap=RH_COLORMAP,
+            title=self.description(),
+            **kwargs,
+        )
+
+    def plot_ws_heatmap(self, **kwargs) -> plt.Axes:
+        """Create a heatmap showing the annual hourly WS values associated with this Typology.
+
+        Args:
+            **kwargs:
+                Additional keyword arguments to pass to the heatmap function.
+
+        Returns:
+            Figure: A matplotlib Figure object.
+        """
+
+        return heatmap(
+            series=collection_to_series(self.wind_speed),
+            cmap=WS_COLORMAP,
+            title=self.description(),
+            **kwargs,
+        )
+
+    def plot_mrt_heatmap(self, **kwargs) -> plt.Axes:
+        """Create a heatmap showing the annual hourly MRT values associated with this Typology.
+
+        Args:
+            **kwargs:
+                Additional keyword arguments to pass to the heatmap function.
+
+        Returns:
+            Figure: A matplotlib Figure object.
+        """
+
+        return heatmap(
+            series=collection_to_series(self.mean_radiant_temperature),
+            cmap=MRT_COLORMAP,
+            title=self.description(),
+            **kwargs,
+        )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_shelterbase.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_shelterbase.py
new file mode 100644
index 00000000..c046a449
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_shelterbase.py
@@ -0,0 +1,957 @@
+"""Base shelter object for use in external comfort calculations."""
+# pylint: disable=E0401
+import json
+from pathlib import Path
+from typing import Any
+from dataclasses import dataclass
+
+# pylint: enable=E0401
+
+import honeybee.dictutil as hb_dict_util
+import honeybee_energy.dictutil as energy_dict_util
+import honeybee_radiance.dictutil as radiance_dict_util
+import matplotlib.pyplot as plt
+import numpy as np
+from honeybee.model import Face, Model, Shade
+from honeybee_energy.schedule.fixedinterval import ScheduleFixedInterval
+from ladybug.epw import EPW
+from ladybug.viewsphere import ViewSphere
+from ladybug_geometry.geometry3d import (
+    Face3D,
+    LineSegment3D,
+    Plane,
+    Point3D,
+    Ray3D,
+    Vector3D,
+)
+from matplotlib.figure import Figure
+from mpl_toolkits import mplot3d
+
+
+from ..bhom.analytics import bhom_analytics
+from ..bhom.to_bhom import point3d_to_bhom
+from ..ladybug_extension.epw import sun_position_list
+from ..helpers import convert_keys_to_snake_case
+
+SENSOR_LOCATION = Point3D(0, 0, 1.2)
+
+
+@dataclass(init=True, eq=True)
+class Shelter:
+    """_"""
+
+    vertices: tuple[Point3D]
+    wind_porosity: tuple[float] = (0,) * 8760
+    radiation_porosity: tuple[float] = (0,) * 8760
+
+    def __post_init__(self):
+        """_"""
+
+        # validation
+        if len(self.wind_porosity) != 8760:
+            raise ValueError("wind_porosity must be 8760 items long.")
+        if any(not isinstance(i, (float, int)) for i in self.wind_porosity):
+            raise ValueError("wind_porosity must be a list of floats.")
+        if any(i < 0 for i in self.wind_porosity):
+            raise ValueError("wind_porosity values must be >= 0.")
+        if any(i > 1 for i in self.wind_porosity):
+            raise ValueError("wind_porosity values must be <= 1.")
+
+        if len(self.radiation_porosity) != 8760:
+            raise ValueError("radiation_porosity must be 8760 items long.")
+        if any(not isinstance(i, (float, int)) for i in self.radiation_porosity):
+            raise ValueError("radiation_porosity must be a list of floats.")
+        if any(i < 0 for i in self.radiation_porosity):
+            raise ValueError("radiation_porosity values must be >= 0.")
+        if any(i > 1 for i in self.radiation_porosity):
+            raise ValueError("radiation_porosity values must be <= 1.")
+
+        if sum(self.wind_porosity) == 8760 and sum(self.radiation_porosity) == 8760:
+            raise ValueError(
+                "This shelter would have no effect as it does not impact wind or radiation exposure."
+            )
+
+        if len(self.wind_porosity) != len(self.radiation_porosity):
+            raise ValueError(
+                "The wind and radiation porosity lists must be the same length."
+            )
+
+        if len(self.vertices) < 3:
+            raise ValueError("A shelter must have at least 3 vertices.")
+
+        if not all(isinstance(item, Point3D) for item in self.vertices):
+            raise ValueError("All vertices must be Point3D objects.")
+
+        _plane = Plane.from_three_points(*self.vertices[:3])
+        for vertex in self.vertices[3:]:
+            if not np.isclose(a=_plane.distance_to_point(point=vertex), b=0):
+                raise ValueError(
+                    "All vertices must be coplanar. Please check your input."
+                )
+
+    def __repr__(self) -> str:
+        return (
+            f"{self.__class__.__name__}("
+            f"avg_wind_porosity={self.average_wind_porosity:0.2f}, "
+            f"avg_radiation_porosity={self.average_radiation_porosity:0.2f}"
+            ")"
+        )
+
+    def to_dict(self) -> str:
+        """Convert this object to a dictionary."""
+        point_dicts = []
+        for point in self.vertices:
+            point_dict = point.to_dict()
+            point_dict["type"] = "Point3D"
+            point_dicts.append(point_dict)
+
+        d = {
+            "type": "Shelter",
+            "vertices": point_dicts,
+            "wind_porosity": self.wind_porosity,
+            "radiation_porosity": self.radiation_porosity,
+        }
+        return d
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "Shelter":
+        """Create this object from a dictionary."""
+        new_vertices = []
+        for vertex in d["vertices"]:
+            if isinstance(vertex, dict):
+                new_vertices.append(Point3D.from_dict(vertex))
+        d["vertices"] = new_vertices
+
+        return cls(
+            vertices=d["vertices"],
+            wind_porosity=d["wind_porosity"],
+            radiation_porosity=d["radiation_porosity"],
+        )
+
+    def to_json(self) -> str:
+        """Convert this object to a JSON string."""
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_string: str) -> "Shelter":
+        """Create this object from a JSON string."""
+
+        return cls.from_dict(json.loads(json_string))
+
+    def to_file(self, path: Path) -> Path:
+        """Convert this object to a JSON file."""
+
+        if Path(path).suffix != ".json":
+            raise ValueError("path must be a JSON file.")
+
+        with open(Path(path), "w") as fp:
+            fp.write(self.to_json())
+
+        return Path(path)
+
+    @classmethod
+    def from_file(cls, path: Path) -> "Shelter":
+        """Create this object from a JSON file."""
+        with open(Path(path), "r") as fp:
+            return cls.from_json(fp.read())
+
+    @property
+    def min_wind_porosity(self) -> float:
+        return min(self.wind_porosity)
+
+    @property
+    def average_wind_porosity(self) -> float:
+        return np.mean(self.wind_porosity)
+
+    @property
+    def max_wind_porosity(self) -> float:
+        return max(self.wind_porosity)
+
+    @property
+    def min_radiation_porosity(self) -> float:
+        return min(self.radiation_porosity)
+
+    @property
+    def average_radiation_porosity(self) -> float:
+        return np.mean(self.radiation_porosity)
+
+    @property
+    def max_radiation_porosity(self) -> float:
+        return max(self.radiation_porosity)
+
+    @classmethod
+    def from_overhead_linear(
+        cls,
+        width: float = 3,
+        height_above_ground: float = 3.5,
+        length: float = 2000,
+        wind_porosity: list[float] = (0,) * 8760,
+        radiation_porosity: list[float] = (0,) * 8760,
+        angle: float = 0,
+    ) -> "Shelter":
+        """Create a linear shelter object oriented north-south, which is
+            effectively infinite in its length.
+
+        Args:
+            width (float, optional):
+                The width of the shelter in m. Defaults to 3m.
+            height_above_ground (float, optional):
+                The height of the shelter in m. Defaults to 3.5m.
+            wind_porosity (float, optional):
+                The transmissivity of the shelter to wind. Defaults to 0.
+            radiation_porosity (float, optional):
+                The transmissivity of the shelter to radiation from sun.
+                Defaults to 0.
+            angle (float, optional):
+                The angle of the shelter in degrees clockwise from north at 0.
+                A value of 0 is north-south. A value of 45 is
+                northeast-southwest. A value of 90 is east-west.
+            length (float, optional):
+                The length of the shelter in m. Defaults to 2000m (centered
+                about origin, so 1000m north and 1000m south).
+
+        Returns:
+            Shelter: A Shelter object.
+        """
+
+        if height_above_ground <= SENSOR_LOCATION.z:
+            raise ValueError(
+                f"height_above_ground must be greater than {SENSOR_LOCATION.z}"
+            )
+
+        origin = Point3D()
+        angle = np.deg2rad(-angle)
+        return cls(
+            vertices=(
+                Point3D(width / 2, -length / 2, height_above_ground).rotate_xy(
+                    angle, origin
+                ),
+                Point3D(width / 2, length / 2, height_above_ground).rotate_xy(
+                    angle, origin
+                ),
+                Point3D(-width / 2, length / 2, height_above_ground).rotate_xy(
+                    angle, origin
+                ),
+                Point3D(-width / 2, -length / 2, height_above_ground).rotate_xy(
+                    angle, origin
+                ),
+            ),
+            wind_porosity=wind_porosity,
+            radiation_porosity=radiation_porosity,
+        )
+
+    @classmethod
+    def from_overhead_circle(
+        cls,
+        radius: float = 1.5,
+        height_above_ground: float = 3.5,
+        wind_porosity: list[float] = (0,) * 8760,
+        radiation_porosity: list[float] = (0,) * 8760,
+    ) -> "Shelter":
+        """Create a circular overhead shelter object.
+
+        Args:
+            radius (float, optional):
+                The radius of the shelter in m. Defaults to 1.5m.
+            height_above_ground (float, optional):
+                The height of the shelter in m. Defaults to 3.5m.
+            wind_porosity (float, optional):
+                The transmissivity of the shelter to wind. Defaults to 0.
+            radiation_porosity (float, optional):
+                The transmissivity of the shelter to radiation from sun.
+                Defaults to 0.
+
+        Returns:
+            Shelter: A Shelter object.
+        """
+
+        if radius <= 0:
+            raise ValueError("radius must be greater than 0")
+
+        if height_above_ground <= SENSOR_LOCATION.z:
+            raise ValueError(
+                f"height_above_ground must be greater than {SENSOR_LOCATION.z}"
+            )
+
+        return cls(
+            vertices=Face3D.from_regular_polygon(
+                side_count=36,
+                radius=radius,
+                base_plane=Plane(o=Point3D(z=height_above_ground)),
+            ).vertices,
+            wind_porosity=wind_porosity,
+            radiation_porosity=radiation_porosity,
+        )
+
+    @classmethod
+    def from_adjacent_wall(
+        cls,
+        distance_from_wall: float = 1,
+        wall_height: float = 2,
+        wall_length: float = 2,
+        wind_porosity: list[float] = (0,) * 8760,
+        radiation_porosity: list[float] = (0,) * 8760,
+        angle: float = 0,
+    ) -> "Shelter":
+        """Create a shelter object representative of an adjacent wall.
+
+        Args:
+            distance_from_wall (float, optional):
+                The distance from the wall in m. Defaults to 1.5m.
+            wall_height (float, optional):
+                The height of the wall in m. Defaults to 2m.
+            wall_length (float, optional):
+                The length of the wall in m. Defaults to 3m.
+            wind_porosity (float, optional):
+                The transmissivity of the shelter to wind. Defaults to 0.
+            radiation_porosity (float, optional):
+                The transmissivity of the shelter to radiation from sun.
+                Defaults to 0.
+            angle (float, optional):
+                The angle of the shelter in degrees clockwise from north at 0.
+                A value of 0 is directly north. A value of 45 is
+                northeast and a value of 90 is to the east.
+
+        Returns:
+            Shelter: A Shelter object.
+        """
+
+        if wall_height <= 0:
+            raise ValueError("wall_height must be greater than 0")
+
+        origin = Point3D()
+        angle = np.deg2rad(-angle)
+        base_line = LineSegment3D(
+            p=origin.move(Vector3D(x=-wall_length / 2, y=distance_from_wall)),
+            v=Vector3D(x=wall_length),
+        )
+
+        return cls(
+            vertices=Face3D.from_extrusion(
+                extrusion_vector=Vector3D(0, 0, wall_height), line_segment=base_line
+            )
+            .rotate_xy(origin=origin, angle=angle)
+            .vertices,
+            wind_porosity=wind_porosity,
+            radiation_porosity=radiation_porosity,
+        )
+
+    @classmethod
+    def from_lb_face3d(
+        cls,
+        face: Face3D,
+        wind_porosity: list[float] = (0,) * 8760,
+        radiation_porosity: list[float] = (0,) * 8760,
+    ) -> "Shelter":
+        """Create a shelter object from a Ladybug Face3D object."""
+        return cls(
+            vertices=face.vertices,
+            wind_porosity=wind_porosity,
+            radiation_porosity=radiation_porosity,
+        )
+
+    @classmethod
+    def from_hb_face(cls, face: Face) -> "Shelter":
+        """Create a shelter object from a Honeybee Face object."""
+        vertices = face.vertices
+        wind_porosity = 0
+        radiation_porosity = (
+            (0,) * 8760
+            if face.properties.radiance.modifier.is_opaque
+            else (face.properties.radiance.modifier.average_transmittance,) * 8760
+        )
+        return cls(
+            vertices=vertices,
+            wind_porosity=wind_porosity,
+            radiation_porosity=radiation_porosity,
+        )
+
+    @classmethod
+    def from_hb_shade(cls, shade: Shade) -> "Shelter":
+        """Create a shelter object from a Honeybee Shade object.
+
+        Args:
+            shade (Shade):
+                A Honeybee Shade object.
+
+        Returns:
+            Shelter: A Shelter object.
+        """
+        vertices = shade.vertices
+        # get shade transmittance schedule, if it is present
+        try:
+            radiation_porosity = (
+                shade.properties.energy.transmittance_schedule.data_collection.values
+            )
+            wind_porosity = (
+                shade.properties.energy.transmittance_schedule.data_collection.values
+            )
+        except AttributeError:
+            radiation_porosity = (0,) * 8760
+            wind_porosity = (0,) * 8760
+        return cls(
+            vertices=vertices,
+            wind_porosity=wind_porosity,
+            radiation_porosity=radiation_porosity,
+        )
+
+    @classmethod
+    def from_hbjson(cls, hbjson_file: Path) -> list["Shelter"]:
+        """Generate Shelter objects from a Honeybee JSON file.
+
+        Args:
+            hbjson_file (Path):
+                A Path to a Honeybee JSON file.
+
+        Returns:
+            list[Shelter]: A list of Shelter objects.
+        """
+        hbjson_file = Path(hbjson_file)
+
+        with open(hbjson_file) as json_file:
+            data = json.load(json_file)
+        try:
+            hb_objs = hb_dict_util.dict_to_object(data, False)
+            if hb_objs is None:
+                hb_objs = energy_dict_util.dict_to_object(data, False)
+                if hb_objs is None:
+                    hb_objs = radiance_dict_util.dict_to_object(data, False)
+        except (KeyError, ValueError):
+            hb_objs = []
+            for hb_dict in data.values():
+                hb_obj = hb_dict_util.dict_to_object(hb_dict, False)
+                if hb_obj is None:
+                    hb_obj = energy_dict_util.dict_to_object(hb_dict, False)
+                    if hb_obj is None:
+                        hb_obj = radiance_dict_util.dict_to_object(hb_dict, False)
+                hb_objs.append(hb_obj)
+
+        if isinstance(hb_objs, Model):
+            hb_objs = hb_objs.shades + hb_objs.faces
+
+        shelters = []
+        for obj in hb_objs:
+            if isinstance(obj, Shade):
+                shelters.append(cls.from_hb_shade(obj))
+            if isinstance(obj, Face):
+                shelters.append(cls.from_hb_face(obj))
+        return shelters
+
+    def to_hbjson(self, hbjson_path: Path) -> Path:
+        """Convert this object to a Honeybee JSON file.
+
+        Args:
+            hbjson_path (Path):
+                A Path to a Honeybee JSON file.
+
+        Returns:
+            Path: A Path to the Honeybee JSON file.
+        """
+        # TODO - maintain the origin porosity as a properties.energy.transmissivity_schedule
+        with open(hbjson_path, "w") as fp:
+            json.dump(self.face3d.to_dict(), fp)
+
+        return hbjson_path
+
+    @property
+    def face3d(self) -> Face3D:
+        """Create the face of the shelter object."""
+        return Face3D(self.vertices)
+
+    @property
+    def hb_shade(self) -> Shade:
+        """Create a Honeybee Shade object from this shelter object."""
+        shd = Shade.from_vertices(identifier="shade", vertices=self.vertices)
+        shd.properties.energy.transmittance_schedule = (  # pylint: disable=no-member
+            ScheduleFixedInterval(
+                "porosity",
+                np.mean([self.radiation_porosity, self.wind_porosity], axis=0),
+            )
+        )
+        return shd
+
+    @bhom_analytics()
+    def rotate(self, angle: float, center: Point3D = Point3D()) -> "Shelter":
+        """Rotate the shelter about 0, 0, 0 by the given angle in degrees clockwise from north at 0.
+
+        Args:
+            angle (float):
+                An angle in degrees.
+            center (Point3D, optional):
+                A Point3D representing the center of rotation. Defaults to 0, 0, 0.
+
+        Returns:
+            Shelter:
+                The rotated shelter object.
+        """
+        angle = np.deg2rad(-angle)
+        return Shelter(
+            vertices=self.face3d.rotate_xy(angle, center).vertices,
+            wind_porosity=self.wind_porosity,
+            radiation_porosity=self.radiation_porosity,
+        )
+
+    @bhom_analytics()
+    def move(self, vector: Vector3D) -> "Shelter":
+        """Move the shelter by the given vector.
+
+        Args:
+            vector (Vector3D):
+                A Vector3D representing the movement.
+
+        Returns:
+            Shelter:
+                The moved shelter object.
+        """
+        return Shelter(
+            vertices=self.face3d.move(vector).vertices,
+            wind_porosity=self.wind_porosity,
+            radiation_porosity=self.radiation_porosity,
+        )
+
+    def set_porosity(self, porosity: float) -> "Shelter":
+        """Return this shelter with an adjusted porosity value applied to both wind and radiation components."""
+
+        if isinstance(porosity, (int, float)):
+            porosity = (porosity,) * 8760
+
+        return Shelter(
+            vertices=self.vertices,
+            radiation_porosity=porosity,
+            wind_porosity=porosity,
+        )
+
+    def annual_sky_exposure(self, include_radiation_porosity: bool = True) -> float:
+        """Determine the proportion of sky the analytical point is exposed to.
+            Also account for radiation_porosity in that exposure.
+
+        Args:
+            include_radiation_porosity (bool, optional):
+                If True, then increase exposure according to shelter porosity. Defaults to True.
+
+        Returns:
+            float:
+                A value between 0 and 1 denoting the proportion of sky exposure.
+        """
+
+        _radiation_porosity = np.array(self.radiation_porosity)
+
+        view_sphere = ViewSphere()
+        rays = [
+            Ray3D(SENSOR_LOCATION, vector)
+            for vector in view_sphere.reinhart_dome_vectors
+        ]
+        n_intersections = sum(bool(self.face3d.intersect_line_ray(ray)) for ray in rays)
+        if include_radiation_porosity:
+            return 1 - ((n_intersections / len(rays)) * (1 - _radiation_porosity))
+        return 1 - (n_intersections / len(rays))
+
+    def annual_sun_exposure(
+        self, epw: EPW, include_radiation_porosity: bool = True
+    ) -> list[float]:
+        """Calculate annual hourly sun exposure. Overnight hours where sun is
+        below horizon default to np.nan sun visibility.
+
+        Args:
+            epw (EPW):
+                A Ladybug EPW object.
+            include_radiation_porosity (bool, optional):
+                If True, then increase exposure according to shelter porosity.
+                Defaults to True.
+
+        Returns:
+            List[float]:
+                A list of annual hourly values denoting sun exposure values.
+        """
+
+        _radiation_porosity = np.array(self.radiation_porosity)
+
+        if all(_radiation_porosity == 1):
+            return epw.wind_speed.values
+
+        _sun_exposure = []
+        for radiation_porosity, sun in list(
+            zip(*[_radiation_porosity, sun_position_list(epw)])
+        ):
+            if sun.altitude < 0:
+                _sun_exposure.append(np.nan)
+                continue
+
+            if radiation_porosity == 1:
+                _sun_exposure.append(1)
+                continue
+
+            ray = Ray3D(
+                SENSOR_LOCATION, (sun.position_3d() - SENSOR_LOCATION).normalize()
+            )
+
+            if self.face3d.intersect_line_ray(ray) is None:
+                _sun_exposure.append(1)
+            else:
+                _sun_exposure.append(
+                    radiation_porosity if include_radiation_porosity else 0
+                )
+        return _sun_exposure
+
+    def wind_exposure(
+        self,
+        wind_direction: float,
+        obstruction_band_width: float = 10,
+        n_samples_xy: int = 4,
+        n_samples_rotational: int = 5,
+        porosity: float = 0,
+        edge_acceleration_factor: float = 1.2,
+    ) -> list[float]:
+        """Determine the multiplier for wind speed from a given direction based on
+            shelter obstruction and edge acceleration effects.
+
+        * When fully obstructed, the result would be 1 * porosity.
+        * Where partially obstructed, edge effects are expected based on the
+            level of porosity of the shelter, and the proportion of obstructed
+            /unobstructed area.
+        * Where not obstructed, the results would be 1.
+
+        Args:
+            wind_direction (float):
+                A single wind direction between 0-360.
+            obstruction_band_width (float):
+                The azimuthal range over which obstruction is checked.
+                Defaults to 10 degrees.
+            n_samples_xy (int):
+                The number of samples to take in the xy plane.
+            n_samples_rotational (int):
+                The number of samples to take in the rotational plane.
+            porosity (bool, optional):
+                A porosity value to apply to this calculation. This does not
+                override the shelter porosity, but is required to enable the
+                calculation of the proportional edge acceleration. Defaults
+                to 0.
+            edge_acceleration_factor (float, optional):
+                The proportional increase in wind speed due to edge acceleration
+                around a shelter edge. Defaults to 1.2.
+
+        Returns:
+            list[float]:
+                A list of multipliers for each hour of the year which describes
+                how much to multiply wind by for the given direction.
+        """
+
+        wind_direction_rad = np.deg2rad(wind_direction)
+        wind_direction_vector = Vector3D(
+            np.sin(wind_direction_rad), np.cos(wind_direction_rad)
+        )  # TODO - check that this is going the right direction
+        wind_direction_plane = Plane(n=wind_direction_vector, o=SENSOR_LOCATION)
+
+        # check that the shelter is not in the opposite direction to the wind
+        if not any(wind_direction_plane.is_point_above(i) for i in self.vertices):
+            # the shelter is in the opposite direction to the wind, no multiplier needed
+            return 1
+
+        # check if shelter is too high to impact wind
+        if all(i.z > SENSOR_LOCATION.z + 5 for i in self.vertices):
+            # the shelter is too high to impact wind, no multiplier needed
+            return 1
+
+        if all(i.z < 0 for i in self.vertices):
+            # the shelter is too low to impact wind, no multiplier needed
+            return 1
+
+        if len(set(i.z for i in self.vertices)) == 1:
+            # the shelter is flat, no multiplier needed
+            # TODO - if shelter angled, approximate funneling towards/away from ground effect
+            return 1
+
+        wind_ray = Ray3D(p=SENSOR_LOCATION, v=wind_direction_vector)
+        wind_rays = []
+        for az_angle in np.deg2rad(
+            np.linspace(
+                -obstruction_band_width / 2, obstruction_band_width / 2, n_samples_xy
+            )
+        ):
+            wr = wind_ray.rotate_xy(origin=SENSOR_LOCATION, angle=az_angle)
+            for rotation in np.deg2rad(np.linspace(0, 180, n_samples_rotational)):
+                wind_rays.append(
+                    wr.rotate(
+                        origin=SENSOR_LOCATION,
+                        angle=rotation,
+                        axis=wind_direction_vector,
+                    )
+                )
+
+        # remove duplicate-ish rays
+        d = {}
+        for wr in wind_rays:
+            vv = tuple(round(i, 3) for i in wr.v.to_array())
+            if vv in d:
+                continue
+            d[vv] = wr
+
+        # get the number of intersections with the shelter object
+        intersections = [bool(self.face3d.intersect_line_ray(i)) for i in d.values()]
+
+        if sum(intersections) == 0:
+            return 1
+
+        # calculate the resultant multiplier
+        return (
+            (porosity * sum(intersections))
+            + (edge_acceleration_factor * sum(not i for i in intersections))
+        ) / len(intersections)
+
+    def annual_wind_speed(
+        self,
+        epw: EPW,
+        include_wind_porosity: bool = True,
+    ) -> list[float]:
+        """Calculate annual hourly effective wind speed. Wind from the given
+        EPW file will be translated to 1.2m above ground.
+
+        Args:
+            epw (EPW):
+                A Ladybug EPW object.
+            include_wind_porosity (bool, optional):
+                If True, then increase exposure according to shelter porosity.
+
+        Returns:
+            List[float]:
+                A list of annual hourly values denoting sun exposure values.
+        """
+
+        _wind_porosity = np.array(self.wind_porosity)
+
+        if all(_wind_porosity == 1):
+            return epw.wind_speed.values
+
+        _wind_speed = []
+        for ws, wd, porosity in list(
+            zip(
+                *[
+                    epw.wind_speed,
+                    epw.wind_direction,
+                    _wind_porosity,
+                ]
+            )
+        ):
+            if ws == 0:
+                _wind_speed.append(0)
+                continue
+            _wind_speed.append(
+                ws
+                * self.wind_exposure(
+                    wind_direction=wd,
+                    porosity=porosity if include_wind_porosity else 0,
+                )
+            )
+
+        return _wind_speed
+
+    @bhom_analytics()
+    def visualise(
+        self,
+        ax: plt.Axes = None,
+        tri_kwargs: dict[str, Any] = None,
+        lim_kwargs: dict[str, tuple[float]] = None,
+    ) -> Figure:
+        """Visualise this shelter to check validity and that it exists where you think it should!
+
+        Args:
+            ax (plt.Axes, optional):
+                A matplotlib axes object. Defaults to None.
+            tri_kwargs:
+                Additional keyword arguments to pass to the Poly3DCollection (shelter) render object.
+            lim_kwargs:
+                Additional keyword arguments to pass to the x/y/z lims of the axes.
+
+        """
+
+        if ax is None:
+            fig = plt.figure()
+            ax = mplot3d.Axes3D(fig)
+            fig.add_axes(ax)
+        if not isinstance(ax, mplot3d.Axes3D):
+            raise ValueError("ax must be a 3D matplotlib axes object")
+
+        if tri_kwargs is None:
+            tri_kwargs = {}
+        if lim_kwargs is None:
+            lim_kwargs = {}
+
+        # TODO - make this use the Ladybug-Matplotlib renderer when it is ready
+
+        ax.scatter(*SENSOR_LOCATION.to_array(), c="red")
+
+        # add shelter as a polygon
+        vtx = np.array([i.to_array() for i in self.face3d.vertices])
+        tri = mplot3d.art3d.Poly3DCollection([vtx])
+        tri.set_color(
+            tri_kwargs.get(
+                "color",
+                tri_kwargs.get(
+                    "c", tri_kwargs.get("fc", tri_kwargs.get("facecolor", "grey"))
+                ),
+            )
+        )
+        tri.set_alpha(
+            tri_kwargs.get(
+                "alpha",
+                tri_kwargs.get("a", 0.5),
+            )
+        )
+        tri.set_edgecolor(
+            tri_kwargs.get(
+                "edgecolor",
+                tri_kwargs.get("ec", "k"),
+            )
+        )
+        ax.add_collection3d(tri)
+
+        # format axes
+        ax.set_xlabel("x")
+        ax.set_ylabel("y")
+        ax.set_zlabel("z")
+
+        # set lims
+        ax.set_xlim(lim_kwargs.get("xlim", (-10, 10)))
+        ax.set_ylim(lim_kwargs.get("ylim", (-10, 10)))
+
+        # pylint: disable=no-member
+        ax.set_zlim(lim_kwargs.get("zlim", (0, 20)))
+        # pylint: enable=no-member
+
+        ax.set_aspect("equal")
+
+        return fig
+
+
+@bhom_analytics()
+def annual_sky_exposure(
+    shelters: list[Shelter], include_radiation_porosity: bool = True
+) -> list[float]:
+    """Determine the proportion of sky the analytical point is exposed to
+    under a combination of shelters. Also account for radiation_porosity in
+    that exposure.
+
+    Args:
+        shelters (List[Shelter]):
+            A list of shelter objects.
+        include_radiation_porosity (bool, optional):
+            If True, then increase exposure according to shelter porosity.
+            Defaults to True.
+
+    Returns:
+        float:
+            A value between 0 and 1 denoting the proportion of sky exposure.
+    """
+    if not bool(shelters):
+        return [1] * 8760
+
+    view_sphere = ViewSphere()
+    rays = [
+        Ray3D(SENSOR_LOCATION, vector) for vector in view_sphere.reinhart_dome_vectors
+    ]
+
+    results = []
+    for ray in rays:
+        result = np.ones_like(shelters[0].radiation_porosity)
+        for shelter in shelters:
+            if bool(shelter.face3d.intersect_line_ray(ray)):
+                result *= (
+                    shelter.radiation_porosity if include_radiation_porosity else 1
+                )
+        results.append(result)
+    results = np.clip(results, 0, None)
+    return results.sum(axis=0) / (len(rays) * len(shelters))
+
+
+@bhom_analytics()
+def annual_sun_exposure(
+    shelters: list[Shelter], epw: EPW, include_radiation_porosity: bool = True
+) -> list[float]:
+    """Calculate annual hourly sun exposure under a set of shelters. Where sun
+    is below horizon default to 0 sun visibility.
+
+    Args:
+        shelters (List[Shelter]):
+            A list of shelter objects.
+        epw (EPW):
+            A Ladybug EPW object.
+        include_radiation_porosity (bool, optional):
+            If True, then increase exposure according to shelter porosity.
+            Defaults to True.
+
+    Returns:
+        List[float]:
+            A list of annual hourly values denoting sun exposure values.
+    """
+    if not bool(shelters):
+        return [np.nan if i == 0 else 1 for i in epw.global_horizontal_radiation]
+    result = np.ones_like(shelters[0].radiation_porosity)
+    for shelter in shelters:
+        result = result * shelter.annual_sun_exposure(
+            epw=epw, include_radiation_porosity=include_radiation_porosity
+        )
+    return result
+
+
+@bhom_analytics()
+def annual_wind_speed(
+    shelters: list[Shelter],
+    epw: EPW,
+) -> list[float]:
+    """Determine the effective hourly annual wind speed based on a combination
+        of multiple shelters in a given EPW file location.
+
+    Note:
+        This method does not modify wind speed from 10m (assumed for height of
+        wind in EPW files). This is so that the resultant values are valid for
+        use in the UTCI calculation process.
+
+    Args:
+        shelters (List[Shelter]):
+            A list of shelter objects.
+        epw (EPW):
+            A Ladybug EPW object.
+
+    Returns:
+        List[float]:
+            A resultant list of EPW aligned wind speeds subject to obstruction
+            from the shelter.
+    """
+    if not bool(shelters):
+        return epw.wind_speed.values
+
+    results = []
+    for shelter in shelters:
+        results.append(shelter.annual_wind_speed(epw=epw))
+    return np.array(results).min(axis=0)
+
+
+@bhom_analytics()
+def write_shelters_to_hbjson(shelters: list[Shelter], hbjson_path: Path) -> Path:
+    """Create a Honeybee JSON file from a list of Shelter objects.
+
+    Args:
+        shelters (List[Shelter]):
+            A list of Shelter objects.
+        hbjson_path (Path):
+            A Path to a Honeybee JSON file.
+
+    Returns:
+        Path: A Path to the Honeybee JSON file.
+    """
+
+    name = hbjson_path.stem
+    directory = hbjson_path.parent
+    faces = [
+        Face.from_vertices(
+            identifier=f"Shelter_{n:02d}", vertices=shelter.face3d.vertices
+        )
+        for n, shelter in enumerate(shelters)
+    ]
+
+    # TODO - include temporal shade schedule here
+
+    Model(identifier="Shelters", orphaned_faces=faces).to_hbjson(
+        name=name, folder=directory
+    )
+    return hbjson_path
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_simulatebase.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_simulatebase.py
new file mode 100644
index 00000000..b032ee80
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_simulatebase.py
@@ -0,0 +1,610 @@
+"""Module for the external comfort package, handling simulation of shaded/
+unshaded surface temperatures in an abstract "openfield" condition."""
+# pylint: disable=E0401
+import json
+from pathlib import Path
+from dataclasses import dataclass
+from typing import Any
+
+# pylint: enable=E0401
+from caseconverter import pascalcase
+import numpy as np
+import pandas as pd
+from honeybee.config import folders as hb_folders
+from honeybee.model import Model
+from honeybee_energy.dictutil import dict_to_material
+from honeybee_energy.material.opaque import EnergyMaterial, EnergyMaterialVegetation
+from honeybee_energy.run import run_idf, run_osw, to_openstudio_osw
+from honeybee_energy.simulation.parameter import (
+    ShadowCalculation,
+    SimulationControl,
+    SimulationOutput,
+    SimulationParameter,
+)
+from ladybug.epw import EPW, HourlyContinuousCollection
+from ladybug.futil import nukedir
+from ladybug_comfort.collection.solarcal import OutdoorSolarCal, SolarCalParameter
+
+from ..bhom.logging import CONSOLE_LOGGER
+from ..bhom.to_bhom import (
+    hourlycontinuouscollection_to_bhom,
+    material_to_bhom,
+)
+from ..honeybee_extension.results import load_sql
+from ..ladybug_extension.datacollection import (
+    collection_from_series,
+    collection_to_series,
+)
+from ..ladybug_extension.epw import epw_to_dataframe
+from ..ladybug_extension.epw import equality as epw_equality
+from ..ladybug_extension.groundtemperature import energyplus_strings
+from .model import create_model, get_ground_reflectance, model_equality
+from ..helpers import convert_keys_to_snake_case, sanitise_string
+from .material import Materials
+
+
+def simulation_id(
+    epw_file: Path,
+    ground_material: EnergyMaterial | EnergyMaterialVegetation,
+    shade_material: EnergyMaterial | EnergyMaterialVegetation,
+) -> str:
+    """Create an ID for a simulation.
+
+    Args:
+        epw_file (Path): The path to the EPW file.
+        ground_material (EnergyMaterial | EnergyMaterialVegetation): The ground material.
+        shade_material (EnergyMaterial | EnergyMaterialVegetation): The shade material.
+
+    Returns:
+        str: The simulation ID.
+    """
+
+    epw_id = sanitise_string(epw_file.stem)
+    ground_material_id = sanitise_string(ground_material.identifier)
+    shade_material_id = sanitise_string(shade_material.identifier)
+    return f"{epw_id}__{ground_material_id}__{shade_material_id}"
+
+
+def simulation_directory(model: Model) -> Path:
+    """Get the working directory (where simulation results will be stored) for the given model, and
+        create it if it doesn't already exist.
+
+    Args:
+        model (Model): A honeybee Model.
+
+    Returns:
+        Path: The simulation directory associated with the given model.
+    """
+
+    working_dir: Path = Path(hb_folders.default_simulation_folder) / model.identifier
+    working_dir.mkdir(parents=True, exist_ok=True)
+
+    return working_dir
+
+
+def simulate_surface_temperatures(
+    model: Model, epw_file: Path, remove_dir: bool = False
+) -> dict[str, HourlyContinuousCollection]:
+    """Simulate surface temperatures for a Honeybee Model and return the
+        resulting SQL results file path.
+
+    Args:
+        model (Model): A honeybee Model.
+        epw_file (Path): The path to an EPW file.
+        remove_dir (bool, optional): Set to True to remove the simulation
+            directory
+
+    Returns:
+        dict[str, HourlyContinuousCollection]: Surface temperature results.
+    """
+
+    if not isinstance(model, Model):
+        raise ValueError("model must be a Honeybee Model.")
+
+    epw_file = Path(epw_file)
+    if not epw_file.exists():
+        raise ValueError("epw_file must be a valid file path.")
+    epw = EPW(epw_file)
+
+    sim_dir = simulation_directory(model)
+
+    # does the epw file already exist in the sim dir
+    epws_match = False
+    existing_epws = list(sim_dir.glob("*.epw"))
+    if len(existing_epws) >= 1:
+        for existing_epw in existing_epws:
+            if epw_equality(epw, EPW(existing_epw), include_header=True):
+                # print(
+                #     f"{epw} is the same as {EPW(existing_epw)} ({existing_epw.relative_to(sim_dir)})"
+                # )
+                epws_match = True
+            else:
+                existing_epw.unlink()
+    saved_epw = (sim_dir / epw_file.name).as_posix()
+    epw.save(saved_epw)
+
+    # do the models match
+    models_match = False
+    existing_models = list(sim_dir.glob("*.hbjson"))
+    if len(existing_models) >= 1:
+        for existing_model in existing_models:
+            if model_equality(model, Model.from_hbjson(existing_model)):
+                models_match = True
+            else:
+                existing_model.unlink()
+
+    # does the sql_path exist
+    sql_path = sim_dir / "run" / "eplusout.sql"
+    sql_exists = sql_path.exists()
+
+    # check for existing results and reload if they exist
+    if not all(
+        [
+            sql_exists,
+            models_match,
+            epws_match,
+        ]
+    ):
+        CONSOLE_LOGGER.info(f"Simulating {model.identifier}")
+        model_json = sim_dir / f"{model.identifier}.hbjson"
+        with open(model_json, "w", encoding="utf-8") as fp:
+            json.dump(model.to_dict(triangulate_sub_faces=True), fp)
+
+        sim_par = SimulationParameter(
+            output=SimulationOutput(
+                outputs=["Surface Outside Face Temperature"],
+                include_sqlite=True,
+                summary_reports=None,
+                include_html=False,
+            ),
+            simulation_control=SimulationControl(
+                do_zone_sizing=False,
+                do_system_sizing=False,
+                do_plant_sizing=False,
+                run_for_sizing_periods=False,
+                run_for_run_periods=True,
+            ),
+            shadow_calculation=ShadowCalculation(
+                solar_distribution="FullExteriorWithReflections",
+                calculation_method="PolygonClipping",
+                calculation_update_method="Periodic",
+                maximum_figures=200,
+            ),
+            terrain_type="Country",
+            timestep=10,
+        )
+        sim_par_json = sim_dir / "simulation_parameter.json"
+        with open(sim_par_json, "w", encoding="utf-8") as fp:
+            json.dump(sim_par.to_dict(), fp)
+
+        osw = to_openstudio_osw(
+            sim_dir.as_posix(),
+            model_json.as_posix(),
+            sim_par_json.as_posix(),
+            additional_measures=None,
+            epw_file=epw.file_path,
+        )
+
+        _, idf = run_osw(osw, silent=True)
+
+        with open(idf, "r", encoding="utf-8") as fp:
+            idf_string = fp.read()
+        idf_string += f"\n\n{energyplus_strings(epw)}"
+
+        with open(idf, "w", encoding="utf-8") as fp:
+            idf_string = fp.write(idf_string)
+
+        run_idf(idf, epw.file_path, silent=False)
+
+    else:
+        CONSOLE_LOGGER.info(f"Reloading {model.identifier}")
+
+    df = load_sql(sql_path)
+
+    if remove_dir:
+        nukedir(sim_dir, rmdir=True)
+
+    return {
+        "shaded_down_temperature": collection_from_series(
+            df.filter(regex="GROUND_ZONE_UP_SHADED")
+            .droplevel([0, 1, 2], axis=1)
+            .squeeze()
+            .rename("Ground Temperature (C)")
+        ),
+        "unshaded_down_temperature": collection_from_series(
+            df.filter(regex="GROUND_ZONE_UP_UNSHADED")
+            .droplevel([0, 1, 2], axis=1)
+            .squeeze()
+            .rename("Ground Temperature (C)")
+        ),
+        "shaded_up_temperature": collection_from_series(
+            df.filter(regex="SHADE_ZONE_DOWN")
+            .droplevel([0, 1, 2], axis=1)
+            .squeeze()
+            .rename("Sky Temperature (C)")
+        ),
+        "unshaded_up_temperature": epw.sky_temperature,
+    }
+
+
+def radiant_temperature(
+    collections: list[HourlyContinuousCollection], view_factors: list[float] = None
+) -> HourlyContinuousCollection:
+    """Calculate the MRT from a list of surface temperature collections, and view
+        factors to each of those surfaces.
+
+    Args:
+        collections (List[HourlyContinuousCollection]):
+            A list of hourly continuous collections.
+        view_factors (List[float]):
+            A list of view factors to each of the collections.
+            If None, then all input collections are weighted equally.
+
+    Returns:
+        HourlyContinuousCollection:
+            An HourlyContinuousCollection of the effective radiant temperature.
+    """
+
+    if view_factors is None:
+        view_factors = [1 / len(collections)] * len(collections)
+    if len(collections) != len(view_factors):
+        raise ValueError("The number of collections and view factors must be the same.")
+    if sum(view_factors) != 1:
+        raise ValueError("The sum of view factors must be 1.")
+
+    mrt_series = (
+        np.power(
+            (
+                np.power(
+                    pd.concat([collection_to_series(i) for i in collections], axis=1)
+                    + 273.15,
+                    4,
+                )
+                * view_factors
+            ).sum(axis=1),
+            0.25,
+        )
+        - 273.15
+    )
+    mrt_series.name = "Radiant Temperature (C)"
+    return collection_from_series(mrt_series)
+
+
+_ATTRIBUTES = [
+    "shaded_down_temperature",
+    "shaded_up_temperature",
+    "unshaded_down_temperature",
+    "unshaded_up_temperature",
+    "shaded_radiant_temperature",
+    "shaded_longwave_mean_radiant_temperature_delta",
+    "shaded_shortwave_mean_radiant_temperature_delta",
+    "shaded_mean_radiant_temperature",
+    "unshaded_radiant_temperature",
+    "unshaded_longwave_mean_radiant_temperature_delta",
+    "unshaded_shortwave_mean_radiant_temperature_delta",
+    "unshaded_mean_radiant_temperature",
+]
+
+
+@dataclass(init=True, repr=True, eq=True)
+class SimulationResult:
+    """_"""
+
+    epw_file: Path
+    ground_material: EnergyMaterial | EnergyMaterialVegetation
+    shade_material: EnergyMaterial | EnergyMaterialVegetation
+    identifier: str = None
+
+    shaded_down_temperature: HourlyContinuousCollection = None
+    shaded_up_temperature: HourlyContinuousCollection = None
+
+    unshaded_down_temperature: HourlyContinuousCollection = None
+    unshaded_up_temperature: HourlyContinuousCollection = None
+
+    shaded_radiant_temperature: HourlyContinuousCollection = None
+    shaded_longwave_mean_radiant_temperature_delta: HourlyContinuousCollection = None
+    shaded_shortwave_mean_radiant_temperature_delta: HourlyContinuousCollection = None
+    shaded_mean_radiant_temperature: HourlyContinuousCollection = None
+
+    unshaded_radiant_temperature: HourlyContinuousCollection = None
+    unshaded_longwave_mean_radiant_temperature_delta: HourlyContinuousCollection = None
+    unshaded_shortwave_mean_radiant_temperature_delta: HourlyContinuousCollection = None
+    unshaded_mean_radiant_temperature: HourlyContinuousCollection = None
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({self.identifier})"
+
+    def __post_init__(self):
+        """_"""
+
+        # validation
+        if not isinstance(self.epw_file, (Path, str)):
+            raise ValueError("epw_file must be a Path or str.")
+        self.epw_file = Path(self.epw_file).resolve()
+        if not self.epw_file.exists():
+            raise ValueError("epw_file does not exist.")
+
+        if isinstance(self.ground_material, Materials):
+            self.ground_material = self.ground_material.value
+        if isinstance(self.shade_material, Materials):
+            self.shade_material = self.shade_material.value
+
+        if not isinstance(
+            self.ground_material, (EnergyMaterial, EnergyMaterialVegetation)
+        ):
+            raise ValueError(
+                "ground_material must be an EnergyMaterial or EnergyMaterialVegetation."
+            )
+        if not isinstance(
+            self.shade_material, (EnergyMaterial, EnergyMaterialVegetation)
+        ):
+            raise ValueError(
+                "shade_material must be an EnergyMaterial or EnergyMaterialVegetation."
+            )
+
+        if self.identifier is None:
+            self.identifier = simulation_id(
+                self.epw_file, self.ground_material, self.shade_material
+            )
+
+        for attr in _ATTRIBUTES:
+            if not isinstance(
+                getattr(self, attr), (HourlyContinuousCollection, type(None))
+            ):
+                raise ValueError(
+                    f"{attr} must be either an HourlyContinuousCollection, or None."
+                )
+
+        # run simulation and populate object with results if not already done
+        _epw = EPW(self.epw_file)
+        _model = create_model(
+            identifier=self.identifier,
+            ground_material=self.ground_material,
+            shade_material=self.shade_material,
+        )
+
+        if not all(
+            [
+                self.shaded_down_temperature,
+                self.unshaded_down_temperature,
+                self.shaded_up_temperature,
+                self.unshaded_up_temperature,
+            ]
+        ):
+            results = simulate_surface_temperatures(
+                model=_model,
+                epw_file=self.epw_file,
+                remove_dir=not bool(self.identifier),
+            )
+            for k, v in results.items():
+                if isinstance(getattr(self, k), HourlyContinuousCollection):
+                    continue
+                setattr(self, k, v)
+
+        # calculate other variables
+        self.shaded_radiant_temperature = radiant_temperature(
+            [
+                self.shaded_down_temperature,
+                self.shaded_up_temperature,
+            ],
+        )
+        self.unshaded_radiant_temperature = radiant_temperature(
+            [
+                self.unshaded_down_temperature,
+                self.unshaded_up_temperature,
+            ],
+        )
+
+        # calculate MRT
+        params = SolarCalParameter()
+        shaded_cal = OutdoorSolarCal(
+            location=_epw.location,
+            direct_normal_solar=_epw.direct_normal_radiation,
+            diffuse_horizontal_solar=_epw.diffuse_horizontal_radiation,
+            horizontal_infrared=_epw.horizontal_infrared_radiation_intensity,
+            surface_temperatures=self.shaded_radiant_temperature,
+            floor_reflectance=get_ground_reflectance(_model),
+            sky_exposure=0,
+            fraction_body_exposed=0,
+            solarcal_body_parameter=params,
+        )
+        unshaded_cal = OutdoorSolarCal(
+            location=_epw.location,
+            direct_normal_solar=_epw.direct_normal_radiation,
+            diffuse_horizontal_solar=_epw.diffuse_horizontal_radiation,
+            horizontal_infrared=_epw.horizontal_infrared_radiation_intensity,
+            surface_temperatures=self.unshaded_down_temperature,
+            floor_reflectance=get_ground_reflectance(_model),
+            sky_exposure=1,
+            fraction_body_exposed=1,
+            solarcal_body_parameter=params,
+        )
+        for shadedness, cal in list(
+            zip(*[["shaded", "unshaded"], [shaded_cal, unshaded_cal]])
+        ):
+            for var in [
+                "mean_radiant_temperature",
+                "shortwave_mrt_delta",
+                "longwave_mrt_delta",
+            ]:
+                setattr(
+                    self,
+                    f"{shadedness}_{var.replace('mrt', 'mean_radiant_temperature')}",
+                    getattr(cal, var),
+                )
+
+        # add some accessors for collections as series
+        for attr in _ATTRIBUTES:
+            setattr(self, f"{attr}_series", collection_to_series(getattr(self, attr)))
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert this object to a dictionary."""
+        ground_material_dict = self.ground_material.to_dict()
+        shade_material_dict = self.shade_material.to_dict()
+
+        attr_dict = {}
+        for attr in _ATTRIBUTES:
+            if getattr(self, attr):
+                attr_dict[attr] = getattr(self, attr).to_dict()
+
+        d = {
+            **{
+                "type": "SimulationResult",
+                "epw_file": self.epw_file.as_posix(),
+                "ground_material": ground_material_dict,
+                "shade_material": shade_material_dict,
+                "identifier": self.identifier,
+            },
+            **attr_dict,
+        }
+
+        return d
+
+    @classmethod
+    def from_dict(cls, d: dict[str, Any]) -> "SimulationResult":
+        """Create this object from a dictionary."""
+        if isinstance(d["ground_material"], dict):
+            d["ground_material"] = dict_to_material(d["ground_material"])
+
+        if isinstance(d["shade_material"], dict):
+            d["shade_material"] = dict_to_material(d["shade_material"])
+
+        for attr in _ATTRIBUTES:
+            if d.get(attr, None):
+                if isinstance(d[attr], dict):
+                    d[attr] = HourlyContinuousCollection.from_dict(d[attr])
+            else:
+                d[attr] = None
+
+        return cls(
+            epw_file=d["epw_file"],
+            ground_material=d["ground_material"],
+            shade_material=d["shade_material"],
+            identifier=d["identifier"],
+            shaded_down_temperature=d["shaded_down_temperature"],
+            shaded_up_temperature=d["shaded_up_temperature"],
+            unshaded_down_temperature=d["unshaded_down_temperature"],
+            unshaded_up_temperature=d["unshaded_up_temperature"],
+            shaded_radiant_temperature=d["shaded_radiant_temperature"],
+            shaded_longwave_mean_radiant_temperature_delta=d[
+                "shaded_longwave_mean_radiant_temperature_delta"
+            ],
+            shaded_shortwave_mean_radiant_temperature_delta=d[
+                "shaded_shortwave_mean_radiant_temperature_delta"
+            ],
+            shaded_mean_radiant_temperature=d["shaded_mean_radiant_temperature"],
+            unshaded_radiant_temperature=d["unshaded_radiant_temperature"],
+            unshaded_longwave_mean_radiant_temperature_delta=d[
+                "unshaded_longwave_mean_radiant_temperature_delta"
+            ],
+            unshaded_shortwave_mean_radiant_temperature_delta=d[
+                "unshaded_shortwave_mean_radiant_temperature_delta"
+            ],
+            unshaded_mean_radiant_temperature=d["unshaded_mean_radiant_temperature"],
+        )
+
+    def to_json(self) -> str:
+        """Create a JSON string from this object."""
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_string: str) -> "SimulationResult":
+        """Create this object from a JSON string."""
+
+        return cls.from_dict(json.loads(json_string))
+
+    def to_file(self, path: Path) -> Path:
+        """Write this object to a JSON file."""
+
+        if Path(path).suffix != ".json":
+            raise ValueError("path must be a JSON file.")
+
+        with open(Path(path), "w") as fp:
+            fp.write(self.to_json())
+
+        return Path(path)
+
+    @classmethod
+    def from_file(cls, path: Path) -> "SimulationResult":
+        """Create this object from a JSON file."""
+
+        with open(Path(path), "r") as fp:
+            return cls.from_json(fp.read())
+
+    @property
+    def epw(self) -> EPW:
+        """Return the EPW object associated with this simulation result."""
+        return EPW(self.epw_file)
+
+    @property
+    def simulation_directory(self) -> Path:
+        """Return the simulation directory for this simulation result."""
+        return simulation_directory(self.model)
+
+    @property
+    def model(self) -> Model:
+        """Return the model object for this simulation result."""
+        return create_model(
+            identifier=self.identifier,
+            ground_material=self.ground_material,
+            shade_material=self.shade_material,
+        )
+
+    def to_dataframe(self) -> pd.DataFrame:
+        """Create a Pandas DataFrame from this object.
+
+        Returns:
+            pd.DataFrame: Represent this object as a Pandas DataFrame.
+        """
+
+        obj_series = []
+        for var in dir(self):
+            for shadedness in ["shaded", "unshaded"]:
+                if not var.startswith(shadedness):
+                    continue
+                _temp = getattr(self, var)
+                if isinstance(_temp, HourlyContinuousCollection):
+                    _temp = collection_to_series(_temp)
+                    _temp.rename(
+                        (shadedness.title(), _temp.name),
+                        inplace=True,
+                    )
+                    obj_series.append(_temp)
+
+        obj_df = pd.concat(obj_series, axis=1)
+
+        return pd.concat(
+            [
+                pd.concat(
+                    [epw_to_dataframe(self.epw, include_additional=True)],
+                    axis=1,
+                    keys=["EPW"],
+                ),
+                obj_df,
+            ],
+            axis=1,
+        )
+
+    def description(self, include_shade_material: bool = True) -> str:
+        """Create the description for this object.
+
+        Args:
+            include_shade_material (bool, optional):
+                Set to False to exclude the shade material from the description.
+                Defaults to True.
+
+        Returns:
+            str:
+                A description of this object.
+        """
+        if include_shade_material:
+            return (
+                f"{self.epw_file.name} - "
+                f"{self.ground_material.identifier} (ground material) - "
+                f"{self.shade_material.identifier} (shade material)"
+            )
+
+        return (
+            f"{self.epw_file.name} - "
+            f"{self.ground_material.identifier} (ground material)"
+        )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_typologybase.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_typologybase.py
new file mode 100644
index 00000000..b16c0d9b
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/_typologybase.py
@@ -0,0 +1,309 @@
+"""Base class for typology objects."""
+# pylint: disable=E0401
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+# pylint: enable=E0401
+
+import numpy as np
+import pandas as pd
+from ladybug.epw import EPW, HourlyContinuousCollection
+
+from ..bhom.analytics import bhom_analytics
+from ..helpers import (
+    convert_keys_to_snake_case,
+    decay_rate_smoother,
+    evaporative_cooling_effect,
+)
+from ..ladybug_extension.datacollection import (
+    collection_from_series,
+    collection_to_series,
+)
+from ._shelterbase import (
+    Shelter,
+    annual_sky_exposure,
+    annual_sun_exposure,
+    annual_wind_speed,
+)
+from .simulate import SimulationResult
+
+
+@dataclass(init=True, repr=True, eq=True)
+class Typology:
+    """_"""
+
+    identifier: str
+    shelters: tuple[Shelter] = ()
+    evaporative_cooling_effect: tuple[float] = (0,) * 8760
+    target_wind_speed: tuple[float] = (None,) * 8760
+    radiant_temperature_adjustment: tuple[float] = (0,) * 8760
+
+    def __post_init__(self):
+        """_"""
+
+        # validation
+        if len(self.shelters) > 0:
+            if any(not isinstance(shelter, Shelter) for shelter in self.shelters):
+                raise ValueError("All shelters must be of type 'Shelter'.")
+
+        if len(self.evaporative_cooling_effect) != 8760:
+            raise ValueError("evaporative_cooling_effect must be 8760 items long.")
+        if any(
+            not isinstance(i, (float, int)) for i in self.evaporative_cooling_effect
+        ):
+            raise ValueError("evaporative_cooling_effect must be a list of floats.")
+        if any(i < 0 for i in self.evaporative_cooling_effect):
+            raise ValueError("evaporative_cooling_effect must be >= 0.")
+        if any(i > 1 for i in self.evaporative_cooling_effect):
+            raise ValueError("evaporative_cooling_effect must be <= 1.")
+
+        if len(self.target_wind_speed) != 8760:
+            raise ValueError("target_wind_speed must be 8760 items long.")
+        for tws in self.target_wind_speed:
+            if not isinstance(tws, (float, int, type(None))):
+                raise ValueError(
+                    "target_wind_speed must be a list of numbers, which can include None values."
+                )
+            if tws is None:
+                continue
+            if tws < 0:
+                raise ValueError(
+                    "target_wind_speed must be >= 0, or None (representing no change to in-situ wind speed)."
+                )
+
+        if len(self.radiant_temperature_adjustment) != 8760:
+            raise ValueError("radiant_temperature_adjustment must be 8760 items long.")
+        if any(
+            not isinstance(i, (float, int)) for i in self.radiant_temperature_adjustment
+        ):
+            raise ValueError("radiant_temperature_adjustment must be a list of floats.")
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({self.identifier})"
+
+    def to_dict(self) -> str:
+        """Convert this object to a dictionary."""
+        shelter_dicts = []
+        for shelter in self.shelters:
+            shelter_dicts.append(shelter.to_dict())
+
+        d = {
+            "type": "Typology",
+            "identifier": self.identifier,
+            "shelters": shelter_dicts,
+            "evaporative_cooling_effect": self.evaporative_cooling_effect,
+            "target_wind_speed": self.target_wind_speed,
+            "radiant_temperature_adjustment": self.radiant_temperature_adjustment,
+        }
+        return d
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "Shelter":
+        """Create this object from a dictionary."""
+        new_shelters = []
+        for shelter in d["shelters"]:
+            if isinstance(shelter, dict):
+                new_shelters.append(Shelter.from_dict(shelter))
+        d["shelters"] = new_shelters
+
+        return cls(
+            identifier=d["identifier"],
+            shelters=d["shelters"],
+            evaporative_cooling_effect=d["evaporative_cooling_effect"],
+            target_wind_speed=d["target_wind_speed"],
+            radiant_temperature_adjustment=d["radiant_temperature_adjustment"],
+        )
+
+    def to_json(self) -> str:
+        """Convert this object to a JSON string."""
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_string: str) -> "Shelter":
+        """Create this object from a JSON string."""
+        return cls.from_dict(json.loads(json_string))
+
+    def to_file(self, path: Path) -> Path:
+        """Convert this object to a JSON file."""
+        if Path(path).suffix != ".json":
+            raise ValueError("path must be a JSON file.")
+
+        with open(Path(path), "w") as fp:
+            fp.write(self.to_json())
+
+        return Path(path)
+
+    @classmethod
+    def from_file(cls, path: Path) -> "Typology":
+        """Create this object from a JSON file."""
+        with open(Path(path), "r") as fp:
+            return cls.from_json(fp.read())
+
+    @property
+    def average_evaporative_cooling_effect(self) -> float:
+        """_"""
+        return np.mean(self.evaporative_cooling_effect)
+
+    @property
+    def average_radiant_temperature_adjustment(self) -> float:
+        """_"""
+        return np.mean(self.radiant_temperature_adjustment)
+
+    @property
+    def average_target_wind_speed(self) -> float:
+        """_"""
+        return np.mean([i for i in self.target_wind_speed if i is not None])
+
+    @bhom_analytics()
+    def sky_exposure(self) -> list[float]:
+        """Direct access to "sky_exposure" method for this typology object."""
+        return annual_sky_exposure(self.shelters, include_radiation_porosity=True)
+
+    @bhom_analytics()
+    def sun_exposure(self, epw: EPW) -> list[float]:
+        """Direct access to "sun_exposure" method for this typology object."""
+        return annual_sun_exposure(self.shelters, epw, include_radiation_porosity=True)
+
+    @bhom_analytics()
+    def wind_speed(self, epw: EPW) -> list[float]:
+        """Direct access to "wind_speed" method for this typology object."""
+        shelter_wind_speed = annual_wind_speed(self.shelters, epw)
+
+        ws = []
+        for sh_ws, tgt_ws in list(zip(shelter_wind_speed, self.target_wind_speed)):
+            if tgt_ws is None or np.isnan(tgt_ws):
+                ws.append(sh_ws)
+            else:
+                ws.append(tgt_ws)
+        return epw.wind_speed.get_aligned_collection(ws)
+
+    @bhom_analytics()
+    def dry_bulb_temperature(self, epw: EPW) -> HourlyContinuousCollection:
+        """Get the effective DBT for the given EPW file for this Typology.
+
+        Args:
+            epw (EPW): A ladybug EPW object.
+
+        Returns:
+            HourlyContinuousCollection: The effective DBT following application of any evaporative
+                cooling effects.
+        """
+        # if there is no evaporative cooling effect, return the original DBT
+        if sum(self.evaporative_cooling_effect) == 0:
+            return epw.dry_bulb_temperature
+
+        # if there is evaporative cooling effect, return the adjusted DBT
+        dbt_evap = []
+        for dbt, rh, ece, atm in list(
+            zip(
+                *[
+                    epw.dry_bulb_temperature,
+                    epw.relative_humidity,
+                    self.evaporative_cooling_effect,
+                    epw.atmospheric_station_pressure,
+                ]
+            )
+        ):
+            _dbt, _ = evaporative_cooling_effect(
+                dry_bulb_temperature=dbt,
+                relative_humidity=rh,
+                evaporative_cooling_effectiveness=ece,
+                atmospheric_pressure=atm,
+            )
+            dbt_evap.append(_dbt)
+
+        return epw.dry_bulb_temperature.get_aligned_collection(dbt_evap)
+
+    @bhom_analytics()
+    def relative_humidity(self, epw: EPW) -> HourlyContinuousCollection:
+        """Get the effective RH for the given EPW file for this Typology.
+
+        Args:
+            epw (EPW): A ladybug EPW object.
+
+        Returns:
+            HourlyContinuousCollection: The effective RH following application of any evaporative
+                cooling effects.
+        """
+
+        # if there is no evaporative cooling effect, return the original RH
+        if sum(self.evaporative_cooling_effect) == 0:
+            return epw.relative_humidity
+
+        # if there is evaporative cooling effect, return the adjusted RH
+        rh_evap = []
+        for dbt, rh, ece, atm in list(
+            zip(
+                *[
+                    epw.dry_bulb_temperature,
+                    epw.relative_humidity,
+                    self.evaporative_cooling_effect,
+                    epw.atmospheric_station_pressure,
+                ]
+            )
+        ):
+            _, _rh = evaporative_cooling_effect(
+                dry_bulb_temperature=dbt,
+                relative_humidity=rh,
+                evaporative_cooling_effectiveness=ece,
+                atmospheric_pressure=atm,
+            )
+            rh_evap.append(_rh)
+
+        return epw.relative_humidity.get_aligned_collection(rh_evap)
+
+    @bhom_analytics()
+    def mean_radiant_temperature(
+        self,
+        simulation_result: SimulationResult,
+    ) -> HourlyContinuousCollection:
+        """Return the effective mean radiant temperature for the given typology following a
+            simulation of the collections necessary to calculate this.
+
+        Args:
+            simulation_result (SimulationResult): An object containing all collections describing
+                shaded and unshaded mean-radiant-temperature.
+
+        Returns:
+            HourlyContinuousCollection: An calculated mean radiant temperature based on the shelter
+                configuration for the given typology.
+        """
+
+        shaded_mrt = collection_to_series(
+            simulation_result.shaded_mean_radiant_temperature
+        )
+        unshaded_mrt = collection_to_series(
+            simulation_result.unshaded_mean_radiant_temperature
+        )
+
+        _sun_exposure = self.sun_exposure(simulation_result.epw)
+        _sky_exposure = self.sky_exposure()
+        mrts = []
+        for sun_exp, sky_exp, shaded, unshaded in list(
+            zip(*[_sun_exposure, _sky_exposure, shaded_mrt.values, unshaded_mrt.values])
+        ):
+            if sun_exp:
+                mrts.append(np.interp(sun_exp, [0, 1], [shaded, unshaded]))
+            else:
+                mrts.append(np.interp(sky_exp, [0, 1], [shaded, unshaded]))
+
+        # Fill any gaps where sun-visible/sun-occluded values are missing
+        mrt_series = pd.Series(
+            mrts, index=shaded_mrt.index, name=shaded_mrt.name
+        ).interpolate()
+
+        # apply an exponentially weighted moving average to account for
+        # transition between shaded/unshaded periods on surrounding surface
+        # temperatures
+        mrt_series = decay_rate_smoother(
+            mrt_series, difference_threshold=-10, transition_window=4, ewm_span=1.25
+        )
+
+        # fill missing data at beginning with interpolation from end of year
+        mrt_series.iloc[0] = mrt_series.iloc[24]
+        mrt_series.interpolate(inplace=True)
+
+        # apply radiant temperature adjustment if given
+        mrt_series += self.radiant_temperature_adjustment
+        return collection_from_series(mrt_series)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/external_comfort.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/external_comfort.py
deleted file mode 100644
index a3e3daad..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/external_comfort.py
+++ /dev/null
@@ -1,748 +0,0 @@
-from __future__ import annotations
-
-import json
-from dataclasses import dataclass, field
-from types import FunctionType
-from typing import Any, Dict, Tuple, Union
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from ladybug.epw import HourlyContinuousCollection
-from ladybug_geometry.geometry3d.pointvector import Point3D
-from matplotlib.figure import Figure
-
-from ..bhomutil.analytics import CONSOLE_LOGGER
-from ..bhomutil.bhom_object import BHoMObject
-from ..bhomutil.encoder import (
-    BHoMEncoder,
-    fix_bhom_jsondict,
-    inf_dtype_to_inf_str,
-    inf_str_to_inf_dtype,
-    pascalcase,
-)
-from ..categorical.categories import UTCI_DEFAULT_CATEGORIES, Categorical
-from ..helpers import wind_speed_at_height
-from ..ladybug_extension.analysis_period import AnalysisPeriod, describe_analysis_period
-from ..ladybug_extension.datacollection import collection_to_series
-from ..ladybug_extension.epw import epw_to_dataframe
-from ..ladybug_extension.location import location_to_string
-from ..plot import heatmap
-from ..plot._utci import (
-    utci_day_comfort_metrics,
-    utci_distance_to_comfortable,
-    utci_heatmap,
-    utci_heatmap_histogram,
-    utci_histogram,
-)
-from ..plot.colormaps import DBT_COLORMAP, MRT_COLORMAP, RH_COLORMAP, WS_COLORMAP
-from .shelter import Shelter
-from .simulate import SimulationResult
-from .typology import Typology
-from .utci.calculate import utci
-
-
-@dataclass(init=True, repr=True, eq=True)
-class ExternalComfort(BHoMObject):
-    """An object containing all inputs and results of an external MRT
-        simulation and resultant thermal comfort metrics.
-
-    Args:
-        simulation_result (SimulationResult):
-            A set of pre-run simulation results.
-        typology (Typology):
-            A typology object.
-
-    Returns:
-        ExternalComfort: An object containing all inputs and results of an external MRT simulation
-        and resultant thermal comfort metrics.
-    """
-
-    SimulationResult: SimulationResult = field(init=True, compare=True, repr=True)
-    Typology: Typology = field(init=True, compare=True, repr=True)
-
-    DryBulbTemperature: HourlyContinuousCollection = field(
-        init=True, compare=True, repr=False, default=None
-    )
-    RelativeHumidity: HourlyContinuousCollection = field(
-        init=True, compare=True, repr=False, default=None
-    )
-    WindSpeed: HourlyContinuousCollection = field(
-        init=True, compare=True, repr=False, default=None
-    )
-    MeanRadiantTemperature: HourlyContinuousCollection = field(
-        init=True, compare=True, repr=False, default=None
-    )
-    UniversalThermalClimateIndex: HourlyContinuousCollection = field(
-        init=True, compare=True, repr=False, default=None
-    )
-
-    _t: str = field(
-        init=False,
-        compare=True,
-        repr=False,
-        default="BH.oM.LadybugTools.ExternalComfort",
-    )
-
-    def __post_init__(self):
-        if not self.SimulationResult.is_run():
-            self.SimulationResult = self.SimulationResult.run()
-
-        # calculate metrics
-        epw = self.SimulationResult.epw
-
-        self.DryBulbTemperature = (
-            self.DryBulbTemperature
-            if isinstance(
-                getattr(self, "DryBulbTemperature"), HourlyContinuousCollection
-            )
-            else self.Typology.dry_bulb_temperature(epw)
-        )
-        self.RelativeHumidity = (
-            self.RelativeHumidity
-            if isinstance(getattr(self, "RelativeHumidity"), HourlyContinuousCollection)
-            else self.Typology.relative_humidity(epw)
-        )
-        self.WindSpeed = (
-            self.WindSpeed
-            if isinstance(getattr(self, "WindSpeed"), HourlyContinuousCollection)
-            else self.Typology.wind_speed(epw)
-        )
-        self.MeanRadiantTemperature = (
-            self.MeanRadiantTemperature
-            if isinstance(
-                getattr(self, "MeanRadiantTemperature"), HourlyContinuousCollection
-            )
-            else self.Typology.mean_radiant_temperature(self.SimulationResult)
-        )
-        if isinstance(
-            getattr(self, "UniversalThermalClimateIndex"), HourlyContinuousCollection
-        ):
-            pass
-        elif all(
-            [
-                isinstance(
-                    getattr(self, "DryBulbTemperature"), HourlyContinuousCollection
-                ),
-                isinstance(
-                    getattr(self, "RelativeHumidity"), HourlyContinuousCollection
-                ),
-                isinstance(getattr(self, "WindSpeed"), HourlyContinuousCollection),
-                isinstance(
-                    getattr(self, "MeanRadiantTemperature"),
-                    HourlyContinuousCollection,
-                ),
-            ]
-        ):
-            CONSOLE_LOGGER.info(f"[{self.Typology.Name}] - Calculating UTCI")
-            self.UniversalThermalClimateIndex = utci(
-                air_temperature=self.DryBulbTemperature,
-                relative_humidity=self.RelativeHumidity,
-                mean_radiant_temperature=self.MeanRadiantTemperature,
-                wind_speed=self.WindSpeed,
-            )
-        else:
-            self.UniversalThermalClimateIndex = (
-                self.Typology.universal_thermal_climate_index(self.SimulationResult)
-            )
-
-        # populate metadata in metrics with current ExternalComfort config
-        if self.Typology.sky_exposure() != 1:
-            typology_description = f"{self.Typology.Name} ({self.SimulationResult.GroundMaterial.to_lbt().identifier} ground and {self.SimulationResult.ShadeMaterial.to_lbt().identifier} shade)"
-        else:
-            typology_description = f"{self.Typology.Name} ({self.SimulationResult.GroundMaterial.to_lbt().identifier} ground)"
-        for attr in [
-            "DryBulbTemperature",
-            "RelativeHumidity",
-            "WindSpeed",
-            "MeanRadiantTemperature",
-            "UniversalThermalClimateIndex",
-        ]:
-            obj = getattr(self, attr)
-            if isinstance(obj, HourlyContinuousCollection):
-                old_metadata = obj.header.metadata
-                new_metadata = {
-                    **old_metadata,
-                    **{"typology": typology_description},
-                }
-                setattr(obj.header, "metadata", new_metadata)
-
-        # wrap methods within this class
-        super().__post_init__()
-
-    @classmethod
-    def from_dict(cls, dictionary: Dict[str, Any]) -> SimulationResult:
-        """Create this object from a dictionary."""
-
-        # handle object conversions
-        if not isinstance(dictionary["SimulationResult"], SimulationResult):
-            dictionary["SimulationResult"] = SimulationResult.from_dict(
-                dictionary["SimulationResult"]
-            )
-
-        if not isinstance(dictionary["Typology"], Typology):
-            dictionary["Typology"] = Typology.from_dict(dictionary["Typology"])
-
-        for calculated_result in [
-            "DryBulbTemperature",
-            "RelativeHumidity",
-            "WindSpeed",
-            "MeanRadiantTemperature",
-            "UniversalThermalClimateIndex",
-        ]:
-            if dictionary[calculated_result] is None:
-                continue
-            if not isinstance(
-                dictionary[calculated_result], HourlyContinuousCollection
-            ):
-                dictionary[calculated_result] = HourlyContinuousCollection.from_dict(
-                    inf_str_to_inf_dtype(dictionary[calculated_result])
-                )
-
-        return cls(
-            SimulationResult=dictionary["SimulationResult"],
-            Typology=dictionary["Typology"],
-            DryBulbTemperature=dictionary["DryBulbTemperature"],
-            RelativeHumidity=dictionary["RelativeHumidity"],
-            WindSpeed=dictionary["WindSpeed"],
-            MeanRadiantTemperature=dictionary["MeanRadiantTemperature"],
-            UniversalThermalClimateIndex=dictionary["UniversalThermalClimateIndex"],
-        )
-
-    @classmethod
-    def from_json(cls, json_string: str) -> SimulationResult:
-        """Create this object from a JSON string."""
-
-        dictionary = inf_str_to_inf_dtype(
-            json.loads(json_string, object_hook=fix_bhom_jsondict)
-        )
-        return cls.from_dict(dictionary)
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Return this object as it's dictionary equivalent."""
-        dictionary = {}
-        for k, v in self.__dict__.items():
-            if isinstance(getattr(self, k), FunctionType):
-                continue
-            dictionary[k] = v
-        dictionary["_t"] = self._t
-        return dictionary
-
-    def to_json(self) -> str:
-        """Return this object as it's JSON string equivalent."""
-        return json.dumps(inf_dtype_to_inf_str(self.to_dict()), cls=BHoMEncoder)
-
-    @property
-    def simulation_result(self) -> SimulationResult:
-        """Handy accessor using proper Python naming convention."""
-        return self.SimulationResult
-
-    @property
-    def typology(self) -> Typology:
-        """Handy accessor using proper Python naming convention."""
-        return self.Typology
-
-    @property
-    def dry_bulb_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming convention."""
-        return self.DryBulbTemperature
-
-    @property
-    def relative_humidity(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming convention."""
-        return self.RelativeHumidity
-
-    @property
-    def wind_speed(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming convention."""
-        return self.WindSpeed
-
-    @property
-    def mean_radiant_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming convention."""
-        return self.MeanRadiantTemperature
-
-    @property
-    def universal_thermal_climate_index(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming convention."""
-        return self.UniversalThermalClimateIndex
-
-    def to_dataframe(
-        self,
-        include_epw: bool = False,
-        include_simulation_results: bool = False,
-        include_epw_additional: bool = False,
-    ) -> pd.DataFrame:
-        """Create a Pandas DataFrame from this object.
-
-        Args:
-            include_epw (bool, optional): Set to True to include the dataframe for the EPW file
-                also.
-            include_simulation_results (bool, optional): Set to True to include the dataframe for
-                the simulation results also.
-            include_epw_additional (bool, optional): Set to True to also include calculated
-                values such as sun position along with EPW. Only includes if include_epw is
-                True also.
-
-        Returns:
-            pd.DataFrame: A Pandas DataFrame with this objects properties.
-        """
-        dfs = []
-
-        if include_epw:
-            dfs.append(
-                epw_to_dataframe(self.SimulationResult.epw, include_epw_additional)
-            )
-
-        if include_simulation_results:
-            dfs.append(self.SimulationResult.to_dataframe())
-
-        variables = [
-            "UniversalThermalClimateIndex",
-            "DryBulbTemperature",
-            "RelativeHumidity",
-            "MeanRadiantTemperature",
-            "WindSpeed",
-        ]
-        for var in variables:
-            s = collection_to_series(getattr(self, var))
-            s.rename(
-                (
-                    f"{self.SimulationResult.Identifier} - {self.Typology.Name}",
-                    pascalcase(var),
-                    s.name,
-                ),
-                inplace=True,
-            )
-            dfs.append(s)
-
-        df = pd.concat(dfs, axis=1)
-
-        return df
-
-    def add_insitu_comfort_measures(
-        self,
-        overhead_shelter: bool = False,
-        wind_speed_multiplier: float = 1,
-        evaporative_cooling_effectiveness: Union[float, Tuple[float]] = 0,
-        radiant_temperature_adjustment: Union[float, Tuple[float]] = 0,
-        adjust_shelter_wind_porosity: float = None,
-        adjust_shelter_radiation_porosity: float = None,
-        additional_air_movement: float = 0,
-    ) -> ExternalComfort:
-        """Apply varying levels of additional measures to the insitu comfort model, taking into account any existing measures that are in place already.
-
-        Args:
-            overhead_shelter (bool, optional):
-                Add an overhead shelter to this object. Defaults to False.
-            evaporative_cooling_effectiveness (float, optional):
-                Set the effectivess of the evaporative cooling. Defaults to 0.7.
-            wind_speed_multiplier (float, optional):
-                Increase wind speed. Defaults to 1.
-            adjust_shelter_wind_porosity (float, optional):
-                Modity existing shelter porosity to help increase air movement from wind. Defaults to None which changes nothing.
-            adjust_shelter_radiation_porosity (float, optional):
-                Modity existing shelter porosity to help increase air movement from wind. Defaults to None which changes nothing.
-            radiant_temperature_adjustment (float, optional):
-                The amount of radiant cooling to apply to the MRT. Defaults to 0.
-            additional_air_movement (float, optional):
-                The amount of additional ground level air movement to apply. Defaults to 0.
-
-        Returns:
-            ExternalComfort:
-                A modified object!
-        """
-
-        # Validation stage and data preparation
-        if isinstance(wind_speed_multiplier, (int, float)):
-            _wind_speed_multiplier = (
-                np.ones_like(self.Typology.WindSpeedMultiplier) * wind_speed_multiplier
-            )
-        elif len(wind_speed_multiplier) != len(self.Typology.WindSpeedMultiplier):
-            raise ValueError(
-                "wind_speed_multiplier must be a float or an iterable with the same length as the number times in the original EC object."
-            )
-        else:
-            _wind_speed_multiplier = wind_speed_multiplier
-
-        if isinstance(evaporative_cooling_effectiveness, (int, float)):
-            _evaporative_cooling_effectiveness = (
-                np.ones_like(self.Typology.EvaporativeCoolingEffect)
-                * evaporative_cooling_effectiveness
-            )
-        elif len(evaporative_cooling_effectiveness) != len(
-            self.Typology.EvaporativeCoolingEffect
-        ):
-            raise ValueError(
-                "evaporative_cooling_effectiveness must be a float or an iterable with the same length as the number times in the original EC object."
-            )
-        else:
-            _evaporative_cooling_effectiveness = evaporative_cooling_effectiveness
-
-        if isinstance(radiant_temperature_adjustment, (int, float)):
-            _radiant_temperature_adjustment = (
-                np.ones_like(self.Typology.RadiantTemperatureAdjustment)
-                * radiant_temperature_adjustment
-            )
-        elif len(radiant_temperature_adjustment) != len(
-            self.Typology.RadiantTemperatureAdjustment
-        ):
-            raise ValueError(
-                "radiant_temperature_adjustment must be a float or an iterable with the same length as the number times in the original EC object."
-            )
-        else:
-            _radiant_temperature_adjustment = radiant_temperature_adjustment
-
-        if isinstance(additional_air_movement, (int, float)):
-            _additional_air_movement = (
-                np.zeros_like(self.Typology.WindSpeedMultiplier)
-                + additional_air_movement
-            )
-        elif len(additional_air_movement) != len(self.Typology.WindSpeedMultiplier):
-            raise ValueError(
-                "additional_air_movement must be a float or an iterable with the same length as the number times in the original EC object."
-            )
-        else:
-            _additional_air_movement = additional_air_movement
-        _additional_air_movement = wind_speed_at_height(
-            _additional_air_movement, 1.1, 10
-        )
-
-        # check if any changes are needed, and return original object if not
-        if not overhead_shelter:
-            if sum(_wind_speed_multiplier) == len(self.Typology.WindSpeedMultiplier):
-                if sum(_evaporative_cooling_effectiveness) == 0:
-                    if sum(_radiant_temperature_adjustment) == 0:
-                        if adjust_shelter_wind_porosity is None:
-                            if adjust_shelter_radiation_porosity is None:
-                                if sum(_additional_air_movement) == 0:
-                                    return self
-
-        # create new typology
-        new_typology_name = f"{self.Typology.Name}"
-
-        # OVERHEAD SHELTER
-        additional_shelters = []
-        if overhead_shelter:
-            new_typology_name += " + overhead shelter"
-            # TODO - check that overhead is not already sheltered and raise error if it is
-            overhead_shelter_obj = Shelter(
-                Vertices=[
-                    Point3D(-3, -3, 3),
-                    Point3D(-3, 3, 3),
-                    Point3D(3, 3, 3),
-                    Point3D(3, -3, 3),
-                ],
-                WindPorosity=0,
-                RadiationPorosity=0,
-            )
-            additional_shelters = [overhead_shelter_obj]
-
-        # MODIFY WIND SPEED
-        if sum(_wind_speed_multiplier) != len(self.Typology.WindSpeedMultiplier):
-            if len(set(_wind_speed_multiplier)) == 1:
-                new_typology_name += f" + {min(_wind_speed_multiplier):0.0%} wind"
-            else:
-                new_typology_name += f" + varying ({min(_wind_speed_multiplier):0.0%}-{max(_wind_speed_multiplier):0.0%}) wind"
-
-        # ADD ADDITIONAL AIR MOVEMENT
-        if sum(_additional_air_movement) != 0:
-            if len(set(_additional_air_movement)) == 1:
-                new_typology_name += (
-                    f" + {additional_air_movement:0.1f}m/s additional air movement"
-                )
-            else:
-                original = wind_speed_at_height(_additional_air_movement, 10, 1.1)
-                new_typology_name += f" + varying ({min(original):0.1f}-{max(original):0.1f}m/s) additional air movement"
-
-        # MODIFY EVAPORATIVE COOLING
-        if sum(_evaporative_cooling_effectiveness) != 0:
-            if len(set(_evaporative_cooling_effectiveness)) == 1:
-                new_typology_name += f" + {min(_evaporative_cooling_effectiveness):0.0%} effective evaporative cooling"
-            else:
-                new_typology_name += f" + varying ({min(_evaporative_cooling_effectiveness):0.0%}-{max(_evaporative_cooling_effectiveness):0.0%}) evaporative cooling effectiveness"
-
-        # MODIFY RADIANT COOLING
-        if sum(_radiant_temperature_adjustment) != 0:
-            if len(set(_radiant_temperature_adjustment)) == 1:
-                new_typology_name += (
-                    f" + {min(_radiant_temperature_adjustment):0.1f}°C radiant cooling"
-                )
-            else:
-                new_typology_name += f" + varying ({min(_radiant_temperature_adjustment):0.1f}-{max(_radiant_temperature_adjustment):0.1f}°C) radiant cooling"
-
-        # MODIFY EXISTING SHELTERS
-        if adjust_shelter_wind_porosity is not None:
-            for shelter in self.Typology.Shelters:
-                shelter.WindPorosity = (
-                    shelter.WindPorosity * adjust_shelter_wind_porosity
-                )
-
-        if adjust_shelter_radiation_porosity is not None:
-            for shelter in self.Typology.Shelters:
-                shelter.RadiationPorosity = (
-                    shelter.RadiationPorosity * adjust_shelter_radiation_porosity
-                )
-
-        # create new typology
-        new_typology = Typology(
-            Name=new_typology_name,
-            Shelters=self.Typology.Shelters + additional_shelters,
-            WindSpeedMultiplier=_wind_speed_multiplier,
-            EvaporativeCoolingEffect=_evaporative_cooling_effectiveness,
-            RadiantTemperatureAdjustment=_radiant_temperature_adjustment,
-        )
-
-        # Run calculation
-        ec = ExternalComfort(
-            SimulationResult=self.SimulationResult,
-            Typology=new_typology,
-        )
-
-        # ADD ADDITIONAL AIR MOVEMENT
-        # print(
-        #     ec.WindSpeed.filter_by_analysis_period(AnalysisPeriod(end_month=2)).average
-        # )
-        ec.WindSpeed._values = (
-            ec.WindSpeed._values + _additional_air_movement
-        ).tolist()
-        # print(
-        #     ec.WindSpeed.filter_by_analysis_period(AnalysisPeriod(end_month=2)).average
-        # )
-        # print(np.unique(_additional_air_movement, return_counts=True))
-        ec.UniversalThermalClimateIndex = utci(
-            ec.dry_bulb_temperature,
-            ec.relative_humidity,
-            ec.mean_radiant_temperature,
-            ec.wind_speed,
-        )
-
-        return ec
-
-    def plot_title_string(self, analysis_period: AnalysisPeriod = None) -> str:
-        """Return the description of this result suitable for use in plotting titles.
-
-        Args:
-            analysis_period (AnalysisPeriod, optional): The analysis period to filter the results by. Defaults to None.
-
-        Returns:
-            str: A string describing this result.
-
-        """
-        typ_str = self.Typology.Name
-
-        if self.Typology.sky_exposure() == 1:
-            ret_str = f"{location_to_string(self.SimulationResult.epw.location)}\n{self.SimulationResult.GroundMaterial.to_lbt().display_name} ground, No shade\n{typ_str}"
-        else:
-            ret_str = f"{location_to_string(self.SimulationResult.epw.location)}\n{self.SimulationResult.GroundMaterial.to_lbt().display_name} ground, {self.SimulationResult.ShadeMaterial.to_lbt().display_name} shade\n{typ_str}"
-
-        if analysis_period is None:
-            return ret_str
-        return f"{ret_str}\n{describe_analysis_period(analysis_period)}"
-
-    def plot_utci_day_comfort_metrics(
-        self, ax: plt.Axes = None, month: int = 3, day: int = 21
-    ) -> plt.Axes:
-        """Plot a single day UTCI and composite components
-
-        Args:
-            ax (plt.Axes, optional): A matplotlib Axes object to plot on. Defaults to None.
-            month (int, optional): The month to plot. Defaults to 3.
-            day (int, optional): The day to plot. Defaults to 21.
-
-        Returns:
-            Axes: A figure showing UTCI and component parts for the given day.
-        """
-
-        return utci_day_comfort_metrics(
-            utci=collection_to_series(self.UniversalThermalClimateIndex),
-            dbt=collection_to_series(self.DryBulbTemperature),
-            mrt=collection_to_series(self.MeanRadiantTemperature),
-            rh=collection_to_series(self.RelativeHumidity),
-            ws=collection_to_series(self.WindSpeed),
-            ax=ax,
-            month=month,
-            day=day,
-            title=self.plot_title_string(),
-        )
-
-    def plot_utci_heatmap(
-        self,
-        ax: plt.Axes = None,
-        utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
-    ) -> plt.Axes:
-        """Create a heatmap showing the annual hourly UTCI values associated with this Typology.
-
-        Args:
-            ax (plt.Axes, optional): A matplotlib Axes object to plot on. Defaults to None.
-            utci_categories (Categorical, optional): The UTCI categories to use. Defaults to
-                UTCI_DEFAULT_CATEGORIES.
-
-        Returns:
-            plt.Axes: A matplotlib Axes object.
-        """
-
-        return utci_heatmap(
-            utci_collection=self.UniversalThermalClimateIndex,
-            ax=ax,
-            utci_categories=utci_categories,
-            title=self.plot_title_string(),
-        )
-
-    def plot_utci_heatmap_histogram(
-        self, utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES, **kwargs
-    ) -> plt.Figure:
-        """Create a heatmap showing the annual hourly UTCI values associated with this Typology.
-
-        Args:
-            utci_categories (Categorical, optional): The UTCI categories to use. Defaults to
-                UTCI_DEFAULT_CATEGORIES.
-            **kwargs:
-                Additional keyword arguments to pass to the heatmap function.
-        Returns:
-            Figure: A matplotlib Figure object.
-        """
-
-        return utci_heatmap_histogram(
-            utci_collection=self.UniversalThermalClimateIndex,
-            utci_categories=utci_categories,
-            title=self.plot_title_string(),
-            **kwargs,
-        )
-
-    def plot_utci_histogram(
-        self,
-        ax: plt.Axes = None,
-        utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
-        analysis_period: AnalysisPeriod = AnalysisPeriod(),
-        **kwargs,
-    ) -> plt.Axes:
-        """Create a histogram showing the annual hourly UTCI values associated with this Typology.
-
-        Args:
-            ax (plt.Axes, optional):
-                A matplotlib Axes object to plot on. Defaults to None.
-            utci_categories (Categorical, optional):
-                The UTCI categories to use. Defaults to UTCI_DEFAULT_CATEGORIES.
-            analysis_period (AnalysisPeriod, optional):
-                The analysis period to filter the results by. Defaults to AnalysisPeriod().
-            **kwargs:
-                Additional keyword arguments to pass to the histogram function.
-        Returns:
-            plt.Axes:
-                A matplotlib Axes object.
-        """
-
-        return utci_histogram(
-            utci_collection=self.universal_thermal_climate_index.filter_by_analysis_period(
-                analysis_period
-            ),
-            ax=ax,
-            utci_categories=utci_categories,
-            title=self.plot_title_string(analysis_period=analysis_period),
-            **kwargs,
-        )
-
-    def plot_utci_distance_to_comfortable(
-        self,
-        ax: plt.Axes = None,
-        comfort_thresholds: Tuple[float] = (9, 26),
-        vmin: float = -10,
-        vmax: float = 10,
-    ) -> Figure:
-        """Create a heatmap showing the "distance" in C from the "no thermal stress" UTCI comfort
-            band.
-
-        Args:
-            ax (plt.Axes, optional): A matplotlib Axes object to plot on. Defaults to None.
-            comfort_thresholds (List[float], optional): The comfortable band of UTCI temperatures.
-                Defaults to [9, 26].
-            vmin (float, optional): The distance from the lower edge of the comfort threshold
-                to include in the "too cold" part of the heatmap. Defaults to -10.
-            vmax (float, optional): The distance from the upper edge of the comfort threshold
-                to include in the "too hot" part of the heatmap. Defaults to 10.
-
-        Returns:
-            Figure: A matplotlib Figure object.
-        """
-
-        return utci_distance_to_comfortable(
-            utci_collection=self.UniversalThermalClimateIndex,
-            ax=ax,
-            title=self.plot_title_string(),
-            comfort_thresholds=comfort_thresholds,
-            vmin=vmin,
-            vmax=vmax,
-        )
-
-    def plot_dbt_heatmap(self, **kwargs) -> plt.Axes:
-        """Create a heatmap showing the annual hourly DBT values associated with this Typology.
-
-        Args:
-            **kwargs:
-                Additional keyword arguments to pass to the heatmap function.
-
-        Returns:
-            Figure: A matplotlib Figure object.
-        """
-
-        return heatmap(
-            series=collection_to_series(self.DryBulbTemperature),
-            cmap=DBT_COLORMAP,
-            title=self.plot_title_string,
-            **kwargs,
-        )
-
-    def plot_rh_heatmap(self, **kwargs) -> plt.Axes:
-        """Create a heatmap showing the annual hourly RH values associated with this Typology.
-
-        Args:
-            **kwargs:
-                Additional keyword arguments to pass to the heatmap function.
-
-        Returns:
-            Figure: A matplotlib Figure object.
-        """
-
-        return heatmap(
-            series=collection_to_series(self.RelativeHumidity),
-            cmap=RH_COLORMAP,
-            title=self.plot_title_string,
-            **kwargs,
-        )
-
-    def plot_ws_heatmap(self, **kwargs) -> plt.Axes:
-        """Create a heatmap showing the annual hourly WS values associated with this Typology.
-
-        Args:
-            **kwargs:
-                Additional keyword arguments to pass to the heatmap function.
-
-        Returns:
-            Figure: A matplotlib Figure object.
-        """
-
-        return heatmap(
-            series=collection_to_series(self.WindSpeed),
-            cmap=WS_COLORMAP,
-            title=self.plot_title_string,
-            **kwargs,
-        )
-
-    def plot_mrt_heatmap(self, **kwargs) -> plt.Axes:
-        """Create a heatmap showing the annual hourly MRT values associated with this Typology.
-
-        Args:
-            **kwargs:
-                Additional keyword arguments to pass to the heatmap function.
-
-        Returns:
-            Figure: A matplotlib Figure object.
-        """
-
-        return heatmap(
-            series=collection_to_series(self.MeanRadiantTemperature),
-            cmap=MRT_COLORMAP,
-            title=self.plot_title_string,
-            **kwargs,
-        )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/externalcomfort.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/externalcomfort.py
new file mode 100644
index 00000000..00e6827c
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/externalcomfort.py
@@ -0,0 +1,112 @@
+"""Derived methods for the ExternalComfort class."""
+
+# pylint: disable=E0401
+import copy
+import warnings
+
+# pylint: enable=E0401
+
+import numpy as np
+
+from ..bhom.analytics import bhom_analytics
+from ._externalcomfortbase import ExternalComfort
+from ._shelterbase import Shelter
+from ._typologybase import Typology
+
+
+@bhom_analytics()
+def modify_external_comfort(
+    external_comfort: ExternalComfort,
+    additional_shelters: tuple[Shelter] = (),
+    target_wind_speed: tuple[float] = (np.nan * np.empty(8760)).tolist(),
+    evaporative_cooling_effect: tuple[float] = (np.nan * np.empty(8760)).tolist(),
+    radiant_temperature_adjustment: tuple[float] = (np.nan * np.empty(8760)).tolist(),
+    existing_shelters_wind_porosity: tuple[float] = (np.nan * np.empty(8760)).tolist(),
+    existing_shelters_radiation_porosity: tuple[float] = (
+        np.nan * np.empty(8760)
+    ).tolist(),
+) -> ExternalComfort:
+    """Apply varying levels of additional measures to the insitu comfort model,
+    taking into account any existing measures that are in place already.
+
+    Args:
+        external_comfort (ExternalComfort):
+            An ExternalComfort object to modify.
+        additional_shelters (tuple[Shelter], optional):
+            Add more shelters to the existing External Comfort case.
+        target_wind_speed (tuple[float], optional):
+            Override the target wind speed of the current typology.
+        evaporative_cooling_effect (tuple[float], optional):
+            Override the effectivess of the current evaporative cooling.
+        radiant_temperature_adjustment (tuple[float], optional):
+            The amount of radiant cooling to apply to the MRT.
+        existing_shelters_wind_porosity (tuple[float], optional):
+            Override the wind porosity of the existing shelters.
+        existing_shelters_radiation_porosity (tuple[float], optional):
+            Override the radiation porosity of the existing shelters.
+
+    Returns:
+        ExternalComfort:
+            A modified object!
+    """
+
+    # check if any changes are needed, and return original object if not
+    if all(
+        [
+            not additional_shelters,
+            all(np.isnan(target_wind_speed)),
+            all(np.isnan(evaporative_cooling_effect)),
+            all(np.isnan(radiant_temperature_adjustment)),
+            all(np.isnan(existing_shelters_wind_porosity)),
+            all(np.isnan(existing_shelters_radiation_porosity)),
+        ]
+    ):
+        warnings.warn("No changes made to the input ExternalComfort object.")
+        return external_comfort
+
+    # modify existing shelters if necessary
+    modified_shelters = []
+    for shelter in external_comfort.typology.shelters:
+        _shelter = copy.copy(shelter)
+        _shelter.wind_porosity = np.where(
+            ~np.isnan(existing_shelters_wind_porosity),
+            existing_shelters_wind_porosity,
+            _shelter.wind_porosity,
+        )
+        _shelter.radiation_porosity = np.where(
+            ~np.isnan(existing_shelters_radiation_porosity),
+            existing_shelters_radiation_porosity,
+            _shelter.radiation_porosity,
+        )
+        modified_shelters.append(_shelter)
+    modified_shelters.extend(additional_shelters)
+
+    # construct name
+    modified_name = (
+        f"{external_comfort.typology.identifier} + modified"  # TODO - add more info
+    )
+
+    # construct new typology
+    modified_typology = Typology(
+        identifier=modified_name,
+        shelters=modified_shelters,
+        target_wind_speed=np.where(
+            ~np.isnan(target_wind_speed),
+            target_wind_speed,
+            external_comfort.typology.target_wind_speed,
+        ).tolist(),
+        evaporative_cooling_effect=np.where(
+            ~np.isnan(evaporative_cooling_effect),
+            evaporative_cooling_effect,
+            external_comfort.typology.evaporative_cooling_effect,
+        ).tolist(),
+        radiant_temperature_adjustment=np.where(
+            ~np.isnan(radiant_temperature_adjustment),
+            radiant_temperature_adjustment,
+            external_comfort.typology.radiant_temperature_adjustment,
+        ).tolist(),
+    )
+    return ExternalComfort(
+        simulation_result=external_comfort.simulation_result,
+        typology=modified_typology,
+    )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/ground_temperature.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/ground_temperature.py
deleted file mode 100644
index 40b0e9c9..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/ground_temperature.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import inspect
-from warnings import warn
-
-import numpy as np
-import pandas as pd
-from honeybee_energy.lib.scheduletypelimits import schedule_type_limit_by_identifier
-from honeybee_energy.schedule.fixedinterval import ScheduleFixedInterval
-from ladybug.datacollection import HourlyContinuousCollection, MonthlyCollection
-from ladybug.epw import EPW
-
-from ..ladybug_extension.datacollection import (
-    collection_from_series,
-    collection_to_series,
-    to_hourly,
-)
-
-
-def energyplus_strings(epw: EPW) -> str:
-    """Generate strings to add into EnergyPlus simulation for more accurate
-        ground surface temperature results.
-
-    Args:
-        epw (EPW):
-            A ladybug EPW object.
-
-    Returns:
-        List[str]:
-            Strings to append to the EnergyPlus IDF simulation input file.
-    """
-
-    monthly_ground_temperatures = monthly_ground_temperature(epw, 0.5)
-
-    ground_temperature_str = inspect.cleandoc(
-        f"""
-            Site:GroundTemperature:BuildingSurface,
-                {monthly_ground_temperatures[0]}, !- January Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[1]}, !- February Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[2]}, !- March Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[3]}, !- April Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[4]}, !- May Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[5]}, !- June Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[6]}, !- July Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[7]}, !- August Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[8]}, !- September Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[9]}, !- October Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[10]}, !- November Surface Ground Temperature {{C}}
-                {monthly_ground_temperatures[11]}; !- December Surface Ground Temperature {{C}}
-            """
-    )
-
-    return ground_temperature_str
-
-
-def eplus_otherside_coefficient(epw: EPW) -> str:
-    """Generate strings to add into EnergyPlus simulation for annual-hourly ground temperature
-        values applied to sub-ground surfaces.
-
-    Args:
-        epw (EPW):
-            A ladybug EPW object.
-
-    Returns:
-        List[str]:
-            Strings to append to the EnergyPlus IDF simulation input file.
-    """
-    gnd_temp_collection = hourly_ground_temperature(epw, 0.5)
-
-    gnd_schedule = ScheduleFixedInterval(
-        "GroundTemperatureSchedule",
-        gnd_temp_collection.values,
-        schedule_type_limit_by_identifier("Temperature"),
-    )
-
-    ground_temperature_str = inspect.cleandoc(
-        """
-            SurfaceProperty:OtherSideCoefficients,
-                GroundTemperature,          !- Name
-                0,                          !- Combined Convective/Radiative Film Coefficient {{W/m2-K}}
-                0.000000,                   !- Constant Temperature {{C}}"
-                1.000000,                   !- Constant Temperature Coefficient"
-                0.000000,                   !- External Dry-Bulb Temperature Coefficient"
-                0.000000,                   !- Ground Temperature Coefficient"
-                0.000000,                   !- Wind Speed Coefficient"
-                0.000000,                   !- Zone Air Temperature Coefficient"
-                GroundTemperatureSchedule;  !- Constant Temperature Schedule Name"
-        """
-    )
-
-    ground_temperature_str += f"\n\n{gnd_schedule.to_idf_compact()}"
-
-    return ground_temperature_str
-
-
-def ground_temperature_at_depth(
-    epw: EPW, depth: float, soil_diffusivity: float = 0.31e-6
-) -> HourlyContinuousCollection:
-    """Construct annual hourly ground temperature at given depth.
-    This method uses the IEC 60287-3-1 method for approximating soil temperature.
-
-    Args:
-        epw (EPW):
-            A ladybug EPW object.
-        depth (float):
-            The depth at which the ground temperature will be calculated.
-        soil_diffusivity (float, optional):
-            Soil diffusivity value. Common values available from the table below.
-            Defaults to 0.31e-6.
-
-    Example:
-        Below is a table from https://dx.doi.org/10.3390%2Fs16030306 detailing suitable diffusivity
-        values for different soil types:
-        +---------------------------+-----------------+-------------+--------------------+
-        | RockType                  |     Thermal     | Volumetric  |      Thermal       |
-        |                           |   Conductivity  |    Heat     |     Diffusivity    |
-        |                           |      (W/mK)     |  Capacity   |    (*10^6 m2/s)    |
-        |                           | Min | Typ | Max |  (MJ/m3K)   | Min  | Typ  | Max  |
-        +---------------------------+-----+-----+-----|-------------+---+----------------+
-        | Basalt                    | 1.3 | 1.7 | 2.3 |     2.6     | 0.5  | 0.65 | 0.88 |
-        | Greenstone                | 2.0 | 2.6 | 2.9 |     2.9     | 0.69 | 0.90 | 1.00 |
-        | Gabbro                    | 1.7 | 1.9 | 2.5 |     2.6     | 0.65 | 0.73 | 0.96 |
-        | Granite                   | 2.1 | 3.4 | 4.1 |     3.0     | 0.70 | 1.13 | 1.37 |
-        | Peridotite                | 3.8 | 4,0 | 5.3 |     2.7     | 1.41 | 1.48 | 1.96 |
-        | Gneiss                    | 1.9 | 2.9 | 4.0 |     2.4     | 0.79 | 1.21 | 1.67 |
-        | Marble                    | 1.3 | 2.1 | 3.1 |     2.0     | 0.65 | 1.05 | 1.55 |
-        | Mica schist               | 1.5 | 2,0 | 3.1 |     2.2     | 0.68 | 0.91 | 1.41 |
-        | Shale sedimentary         | 1.5 | 2.1 | 2.1 |     2.5     | 0.60 | 0.84 | 0.84 |
-        | Limestone                 | 2.5 | 2.8 | 4.0 |     2.4     | 1.04 | 1.17 | 1.67 |
-        | Loam                      | 1.5 | 2.1 | 3.5 |     2.3     | 0.65 | 0.91 | 1.52 |
-        | Quartzite                 | 3.6 | 6,0 | 6.6 |     2.2     | 1.64 | 2.73 | 3.00 |
-        | Salt                      | 5.3 | 5.4 | 6.4 |     1.2     | 4.42 | 4.50 | 5.33 |
-        | Sandstone                 | 1.3 | 2.3 | 5.1 |     2.8     | 0.46 | 0.82 | 1.82 |
-        | Siltstones and argillites | 1.1 | 2.2 | 3.5 |     2.4     | 0.46 | 0.92 | 1.46 |
-        | Dry gravel                | 0.4 | 0.4 | 0.5 |     1.6     | 0.25 | 0.25 | 0.31 |
-        | Water saturated gravel    | 1.8 | 1.8 | 1.8 |     2.4     | 0.75 | 0.75 | 0.75 |
-        | Dry sand                  | 0.3 | 0.4 | 0.55|     1.6     | 0.19 | 0.25 | 0.34 |
-        | Water saturated sand      | 1.7 | 2.4 | 5.0 |     2.9     | 0.59 | 0.83 | 1.72 |
-        | Dry clay/silt             | 0.4 | 0.5 | 1.0 |     1.6     | 0.25 | 0.31 | 0.62 |
-        | Water saturated clay/silt | 0.9 | 1.7 | 2.3 |     3.4     | 0.26 | 0.5  | 0.68 |
-        | Peat                      | 0.2 | 0.4 | 0.7 |     3.8     | 0.05 | 0.10 | 0.18 |
-        +---------------------------+-----+-----+-----+-------------+------+------+------+
-
-    Returns:
-        HourlyContinuousCollection:
-            A data collection containing ground temperature values.
-    """
-
-    try:
-        return to_hourly(epw.monthly_ground_temperature[depth])
-    except (KeyError, ValueError):
-        dbt = collection_to_series(epw.dry_bulb_temperature)
-
-        dbt_range = dbt.max() - dbt.min()
-        dbt_mean = dbt.mean()
-        day_count = len(dbt.index.dayofyear.unique())
-
-        coldest_day = dbt.resample("D").min().values.argmin()
-        days_since_coldest_day = []
-        for i in range(day_count):
-            if i <= coldest_day:
-                days_since_coldest_day.append(day_count - (coldest_day - i))
-            else:
-                days_since_coldest_day.append(i - coldest_day)
-
-        annual_profile = 2 * np.pi / day_count
-        annual_profile_factored = np.sqrt(2 * soil_diffusivity * 86400 / annual_profile)
-        gnd_temp_daily = (
-            pd.Series(
-                [
-                    dbt_mean
-                    - (dbt_range / 2)
-                    * np.exp(-depth / annual_profile_factored)
-                    * np.cos((annual_profile * i) - (depth / annual_profile_factored))
-                    for i in days_since_coldest_day
-                ],
-                index=dbt.resample("D").min().index,
-            )
-            .resample("60T")
-            .mean()
-            .interpolate()
-            .values
-        )
-        gnd_temp_hourly = list(gnd_temp_daily) + list(gnd_temp_daily[0:23])
-        gnd_temp_hourly = pd.Series(
-            gnd_temp_hourly, index=dbt.index, name="Ground Temperature (C)"
-        )
-
-        return collection_from_series(gnd_temp_hourly)
-
-
-def hourly_ground_temperature(
-    epw: EPW, depth: float = 0.5, soil_diffusivity: float = 0.31e-6
-) -> HourlyContinuousCollection:
-    """Return the hourly ground temperature values from the EPW file (upsampled from any
-        available monthly values), or approximate these if not available.
-
-    Args:
-        epw (EPW):
-            The EPW file to extract ground temperature data from.
-        depth (float, optional):
-            The depth of the soil in meters. Defaults to 0.5 meters.
-        soil_diffusivity (float, optional):
-            The soil diffusivity in m2/s. Defaults to 0.31e-6 m2/s.
-
-    Returns:
-        MonthlyCollection:
-            A data collection containing ground temperature values at the
-            depth specified.
-    """
-
-    try:
-        return to_hourly(epw.monthly_ground_temperature[depth])
-    except KeyError:
-        return ground_temperature_at_depth(epw, depth, soil_diffusivity)
-
-
-def monthly_ground_temperature(
-    epw: EPW, depth: float = 0.5, soil_diffusivity: float = 0.31e-6
-) -> MonthlyCollection:
-    """Return the monthly ground temperature values from the EPW file, or
-    approximate these if not available.
-
-    Args:
-        epw (EPW):
-            The EPW file to extract ground temperature data from.
-        depth (float, optional):
-            The depth of the soil in meters. Defaults to 0.5 meters.
-        soil_diffusivity (float, optional):
-            The soil diffusivity in m2/s. Defaults to 0.31e-6 m2/s.
-
-    Returns:
-        MonthlyCollection:
-            A data collection containing ground temperature values at the
-            depth specified.
-    """
-
-    try:
-        return epw.monthly_ground_temperature[depth]
-    except KeyError:
-        warn(
-            f"The input EPW doesn't contain any monthly ground temperatures at {depth}m. An "
-            "approximation method will be used to determine ground temperature at that depth "
-            "based on ambient dry-bulb."
-        )
-        return ground_temperature_at_depth(
-            epw, depth, soil_diffusivity
-        ).average_monthly()
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/material.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/material.py
index 92cb2e3e..b075df4b 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/material.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/material.py
@@ -1,2623 +1,185 @@
-from __future__ import annotations
+"""Methods for creating and manipulating materials.
+"""
 
-import json
-from dataclasses import dataclass, field
+# pylint: disable=E0401
+from difflib import get_close_matches
+from pathlib import Path
 from enum import Enum
-from types import FunctionType
-from typing import Any, Dict, Union
 
-from honeybee_energy.material.opaque import EnergyMaterial, EnergyMaterialVegetation
-
-from ..bhomutil.bhom_object import BHoMObject
-
-# NOTE: This is the version of the Honeybee Material Standards that this module referneces. If these standards are updated, this version number should be updated as well.
-HONEYBEE_MATERIAL_STANDARDS_VERSION = "2.2.6"
-
-# TODO - inheritance here is a nightmare when trying to account for automatic BHoM logging too.
-
-
-@dataclass(init=True, repr=True, eq=True)
-class OpaqueMaterial(BHoMObject):
-    """An object representing a material."""
-
-    Identifier: str = field(repr=True, compare=True)
-    Source: str = field(repr=False, compare=True)
-    Thickness: float = field(repr=False, compare=True)
-    Conductivity: float = field(repr=False, compare=True)
-    Density: float = field(repr=False, compare=True)
-    SpecificHeat: float = field(repr=False, compare=True)
-    Roughness: str = field(repr=False, compare=True, default="MediumRough")
-    ThermalAbsorptance: float = field(repr=False, compare=True, default=0.9)
-    SolarAbsorptance: float = field(repr=False, compare=True, default=0.7)
-    VisibleAbsorptance: float = field(repr=False, compare=True, default=0.7)
-
-    _t: str = field(
-        init=False,
-        repr=False,
-        compare=True,
-        default="BH.oM.LadybugTools.OpaqueMaterial",
-    )
-
-    def __post_init__(self):
-        if "," in self.Identifier:
-            raise ValueError("commas cannot be used in Identifier values.")
-        if len(self.Identifier) > 100:
-            raise ValueError("Identifiers must be lass than 100 characters")
-
-    @classmethod
-    def from_dict(cls, dictionary: Dict[str, Any]) -> OpaqueMaterial:
-        """Create this object from a dictionary."""
-
-        return cls(
-            Identifier=dictionary["Identifier"],
-            Thickness=dictionary["Thickness"],
-            Conductivity=dictionary["Conductivity"],
-            Density=dictionary["Density"],
-            SpecificHeat=dictionary["SpecificHeat"],
-            Roughness=dictionary["Roughness"],
-            ThermalAbsorptance=dictionary["ThermalAbsorptance"],
-            SolarAbsorptance=dictionary["SolarAbsorptance"],
-            VisibleAbsorptance=dictionary["VisibleAbsorptance"],
-            Source=dictionary["Source"],
-        )
-
-    @classmethod
-    def from_json(cls, json_string: str) -> OpaqueMaterial:
-        """Create this object from a JSON string."""
-
-        dictionary = json.loads(json_string)
-
-        return cls.from_dict(dictionary)
-
-    @classmethod
-    def from_lbt(cls, lbt_material: EnergyMaterial) -> OpaqueMaterial:
-        """Create this object from its LBT equivalent."""
-        return cls(
-            Identifier=lbt_material.identifier,
-            Thickness=lbt_material.thickness,
-            Conductivity=lbt_material.conductivity,
-            Density=lbt_material.density,
-            SpecificHeat=lbt_material.specific_heat,
-            Roughness=lbt_material.roughness,
-            ThermalAbsorptance=lbt_material.thermal_absorptance,
-            SolarAbsorptance=lbt_material.solar_absorptance,
-            VisibleAbsorptance=lbt_material.visible_absorptance,
-            Source="LadybugTools",
-        )
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Return this object as it's dictionary equivalent."""
-        dictionary = {}
-        for k, v in self.__dict__.items():
-            if isinstance(getattr(self, k), FunctionType):
-                continue
-            dictionary[k] = v
-        dictionary["_t"] = self._t
-        return dictionary
-
-    def to_json(self) -> str:
-        """Return this object as it's JSON string equivalent."""
-        return json.dumps(self.to_dict())
-
-    def to_lbt(self) -> EnergyMaterial:
-        """Return this object as its LBT equivalent."""
-        return EnergyMaterial(
-            identifier=self.Identifier,
-            thickness=self.Thickness,
-            conductivity=self.Conductivity,
-            density=self.Density,
-            specific_heat=self.SpecificHeat,
-            roughness=self.Roughness,
-            thermal_absorptance=self.ThermalAbsorptance,
-            solar_absorptance=self.SolarAbsorptance,
-            visible_absorptance=self.VisibleAbsorptance,
-        )
-
-    @property
-    def identifier(self) -> str:
-        """Handy accessor using proper Python naming style."""
-        return self.Identifier
-
-    @property
-    def thickness(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.Thickness
-
-    @property
-    def conductivity(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.Conductivity
-
-    @property
-    def density(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.Density
-
-    @property
-    def specific_heat(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.SpecificHeat
-
-    @property
-    def roughness(self) -> str:
-        """Handy accessor using proper Python naming style."""
-        return self.Roughness
-
-    @property
-    def thermal_absorptance(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.ThermalAbsorptance
-
-    @property
-    def solar_absorptance(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.SolarAbsorptance
-
-    @property
-    def visible_absorptance(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.VisibleAbsorptance
-
-    @property
-    def source(self) -> str:
-        """Handy accessor using proper Python naming style."""
-        return self.Source
-
-
-@dataclass(init=True, repr=True, eq=True)
-class OpaqueVegetationMaterial(BHoMObject):
-    """An object representing a vegetation material."""
-
-    Identifier: float = field(repr=True, compare=True)
-    Source: str = field(repr=False, compare=True)
-    Thickness: float = field(repr=False, compare=True, default=0.1)
-    Conductivity: float = field(repr=False, compare=True, default=0.35)
-    Density: float = field(repr=False, compare=True, default=1100)
-    SpecificHeat: float = field(repr=False, compare=True, default=1200)
-    Roughness: float = field(repr=False, compare=True, default="MediumRough")
-    SoilThermalAbsorptance: float = field(repr=False, compare=True, default=0.9)
-    SoilSolarAbsorptance: float = field(repr=False, compare=True, default=0.7)
-    SoilVisibleAbsorptance: float = field(repr=False, compare=True, default=0.7)
-    PlantHeight: float = field(repr=False, compare=True, default=0.2)
-    LeafAreaIndex: float = field(repr=False, compare=True, default=1)
-    LeafReflectivity: float = field(repr=False, compare=True, default=0.22)
-    LeafEmissivity: float = field(repr=False, compare=True, default=0.95)
-    MinStomatalResist: float = field(repr=False, compare=True, default=180)
-
-    _t: str = field(
-        init=False,
-        repr=False,
-        compare=True,
-        default="BH.oM.LadybugTools.OpaqueVegetationMaterial",
-    )
-
-    def __post_init__(self):
-        if "," in self.Identifier:
-            raise ValueError("commas cannot be used in Identifier values.")
-        if len(self.Identifier) > 100:
-            raise ValueError("Identifiers must be lass than 100 characters")
-
-    @classmethod
-    def from_dict(cls, dictionary: Dict[str, Any]) -> OpaqueVegetationMaterial:
-        """Create this object from a dictionary."""
-
-        return cls(
-            Identifier=dictionary["Identifier"],
-            Thickness=dictionary["Thickness"],
-            Conductivity=dictionary["Conductivity"],
-            Density=dictionary["Density"],
-            SpecificHeat=dictionary["SpecificHeat"],
-            Roughness=dictionary["Roughness"],
-            SoilThermalAbsorptance=dictionary["SoilThermalAbsorptance"],
-            SoilSolarAbsorptance=dictionary["SoilSolarAbsorptance"],
-            SoilVisibleAbsorptance=dictionary["SoilVisibleAbsorptance"],
-            PlantHeight=dictionary["PlantHeight"],
-            LeafAreaIndex=dictionary["LeafAreaIndex"],
-            LeafReflectivity=dictionary["LeafReflectivity"],
-            LeafEmissivity=dictionary["LeafEmissivity"],
-            MinStomatalResist=dictionary["MinStomatalResist"],
-            Source=dictionary["Source"],
-        )
-
-    @classmethod
-    def from_json(cls, json_string: str) -> OpaqueVegetationMaterial:
-        """Create this object from a JSON string."""
-
-        dictionary = json.loads(json_string)
-
-        return cls.from_dict(dictionary)
-
-    @classmethod
-    def from_lbt(cls, material: EnergyMaterialVegetation):
-        """Create this object from its LBT equivalent."""
-        return cls(
-            Identifier=material.identifier,
-            Thickness=material.thickness,
-            Conductivity=material.conductivity,
-            Density=material.density,
-            SpecificHeat=material.specific_heat,
-            Roughness=material.roughness,
-            SoilThermalAbsorptance=material.soil_thermal_absorptance,
-            SoilSolarAbsorptance=material.soil_solar_absorptance,
-            SoilVisibleAbsorptance=material.soil_visible_absorptance,
-            PlantHeight=material.plant_height,
-            LeafAreaIndex=material.leaf_area_index,
-            LeafReflectivity=material.leaf_reflectivity,
-            LeafEmissivity=material.leaf_emissivity,
-            MinStomatalResist=material.min_stomatal_resist,
-            Source="LadybugTools",
-        )
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Return this object as it's dictionary equivalent."""
-        dictionary = {}
-        for k, v in self.__dict__.items():
-            if isinstance(getattr(self, k), FunctionType):
-                continue
-            dictionary[k] = v
-        dictionary["_t"] = self._t
-        return dictionary
-
-    def to_json(self) -> str:
-        """Return this object as it's JSON string equivalent."""
-        return json.dumps(self.to_dict())
-
-    def to_lbt(self) -> EnergyMaterialVegetation:
-        """Return this object as its LBT equivalent."""
-        return EnergyMaterialVegetation(
-            identifier=self.Identifier,
-            thickness=self.Thickness,
-            conductivity=self.Conductivity,
-            density=self.Density,
-            specific_heat=self.SpecificHeat,
-            roughness=self.Roughness,
-            soil_thermal_absorptance=self.SoilThermalAbsorptance,
-            soil_solar_absorptance=self.SoilSolarAbsorptance,
-            soil_visible_absorptance=self.SoilVisibleAbsorptance,
-            plant_height=self.PlantHeight,
-            leaf_area_index=self.LeafAreaIndex,
-            leaf_reflectivity=self.LeafReflectivity,
-            leaf_emissivity=self.LeafEmissivity,
-            min_stomatal_resist=self.MinStomatalResist,
-        )
-
-    @property
-    def identifier(self) -> str:
-        """Handy accessor using proper Python naming style."""
-        return self.Identifier
-
-    @property
-    def thickness(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.Thickness
-
-    @property
-    def conductivity(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.Conductivity
-
-    @property
-    def density(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.Density
-
-    @property
-    def specific_heat(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.SpecificHeat
-
-    @property
-    def roughness(self) -> str:
-        """Handy accessor using proper Python naming style."""
-        return self.Roughness
-
-    @property
-    def soil_thermal_absorptance(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.SoilThermalAbsorptance
-
-    @property
-    def soil_solar_absorptance(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.SoilSolarAbsorptance
-
-    @property
-    def soil_visible_absorptance(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.SoilVisibleAbsorptance
-
-    @property
-    def plant_height(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.PlantHeight
-
-    @property
-    def leaf_area_index(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.LeafAreaIndex
-
-    @property
-    def leaf_reflectivity(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.LeafReflectivity
-
-    @property
-    def leaf_emissivity(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.LeafEmissivity
-
-    @property
-    def min_stomatal_resist(self) -> float:
-        """Handy accessor using proper Python naming style."""
-        return self.MinStomatalResist
+# pylint: enable=E0401
+
+import pandas as pd
+from honeybee_energy.lib.materials import (
+    OPAQUE_MATERIALS,
+    opaque_material_by_identifier,
+)
+from honeybee_energy.material.opaque import (
+    EnergyMaterial,
+    EnergyMaterialVegetation,
+    _EnergyMaterialOpaqueBase,
+)
+from ..helpers import sanitise_string
+
+
+def _ice_tool_materials(
+    path: Path = Path(__file__).parent.parent.parent
+    / "data"
+    / r"ICE_database_sources.xlsx",
+) -> list[_EnergyMaterialOpaqueBase]:
+    """Load predefined materials from the ICE tool database.
+
+    Args:
+        path (Path):
+            Path to the ICE tool database.
+    Returns:
+        List of EnergyMaterial objects.
+    """
 
-    @property
-    def source(self) -> str:
-        """Handy accessor using proper Python naming style."""
-        return self.Source
+    df = pd.read_excel(path, "Basic")
+
+    _materials = []
+    for _, row in df.iterrows():
+        try:
+            mat = EnergyMaterial(
+                identifier=f"{row['id']} - {row['specific name']} ({row['color']} {row['age/rugosite']})".replace(
+                    ",", " "
+                ),
+                thickness=0.01,
+                conductivity=row["Thermal condutivity (W/m.K)"],
+                density=row["density (kg/m³)"],
+                specific_heat=row["Specific heat capacity  Cp (J/kg.K)"],
+                roughness="MediumRough",
+                thermal_absorptance=row["emissivitty"],
+                solar_absorptance=1 - row["albedo"],
+                visible_absorptance=1 - row["albedo"],
+            )
+            _materials.append(mat)
+        except AssertionError as _:
+            pass
+    return _materials
+
+
+def _lbt_materials() -> list[_EnergyMaterialOpaqueBase]:
+    """Load predefined LBT materials into a list."""
+    _materials = [
+        opaque_material_by_identifier(material_name)
+        for material_name in OPAQUE_MATERIALS
+    ]
+    return [
+        i
+        for i in _materials
+        if isinstance(i, (EnergyMaterial, EnergyMaterialVegetation))
+    ]
+
+
+def _custom_materials() -> list[_EnergyMaterialOpaqueBase]:
+    """Create a list of custom materials."""
+    return [
+        EnergyMaterial(
+            identifier="Fabric",
+            roughness="Smooth",
+            thickness=0.002,
+            conductivity=0.06,
+            density=500.0,
+            specific_heat=1800.0,
+            thermal_absorptance=0.89,
+            solar_absorptance=0.5,
+            visible_absorptance=0.5,
+        ),
+        EnergyMaterialVegetation(
+            identifier="Shrubs",
+            roughness="Rough",
+            thickness=0.1,
+            conductivity=0.35,
+            density=1260,
+            specific_heat=1100,
+            soil_thermal_absorptance=0.9,
+            soil_solar_absorptance=0.7,
+            soil_visible_absorptance=0.7,
+            plant_height=0.2,
+            leaf_area_index=2.08,
+            leaf_reflectivity=0.21,
+            leaf_emissivity=0.95,
+            min_stomatal_resist=180,
+        ),
+        EnergyMaterial(
+            identifier="Travertine",
+            roughness="MediumRough",
+            thickness=0.2,
+            conductivity=3.2,
+            density=2700.0,
+            specific_heat=790.0,
+            thermal_absorptance=0.96,
+            solar_absorptance=0.55,
+            visible_absorptance=0.55,
+        ),
+    ]
+
+
+def _material_equality(
+    material1: _EnergyMaterialOpaqueBase, material2: _EnergyMaterialOpaqueBase
+) -> bool:
+    """Determine if two materials are equal.
+
+    Args:
+        material1 (_EnergyMaterialOpaqueBase): The first material.
+        material2 (_EnergyMaterialOpaqueBase): The second material.
+
+    Returns:
+        bool: True if the materials are equal, False otherwise.
+    """
 
+    return str(material1) == str(material2)
 
-def material_from_dict(
-    dictionary: Dict[str, Any]
-) -> Union[OpaqueMaterial, OpaqueVegetationMaterial]:
-    """Attempt to convert a dictionary into a material object."""
-    # pylint disable=broad-except
-    try:
-        return OpaqueMaterial.from_dict(dictionary)
-    except Exception:
-        return OpaqueVegetationMaterial.from_dict(dictionary)
-    # pylint enable=broad-except
 
+def materials() -> list[_EnergyMaterialOpaqueBase]:
+    """Return a list all the materials in the library.
 
-# pylint: disable=C0103
-class Materials(Enum):
+    Notes:
+        This function is used to create the Materials enum which is a combination of all
+        LBT provided generic materials, custom materials, and materials from the ICE tool.
     """
-    Enum of predefined materials for use in External Comfort simulation
-    workflow.
 
-    Material properties defined here are from a range of sources.
+    all_materials = []
+    all_materials.extend(_lbt_materials())
+    all_materials.extend(_custom_materials())
+    all_materials.extend(_ice_tool_materials())
 
-    If they cannot be found in the locations given below, then the material
-    must have been added based on precedent, project requirement and research
-    into representative properties of the named material:
+    # remove dodgy materials
+    filtered_materials = []
+    for mat in all_materials:
+        if any(i in mat.identifier.lower() for i in [" air ", " gap ", " void "]):
+            continue
+        filtered_materials.append(mat)
+    return filtered_materials
 
-    - ASHRAE_2005_HOF_Materials.idf (ASHRAE Handbook of Fundamentals, 2005,
-        Chapter 30, Table 19 and Table 22)
-    - https://github.com/ladybug-tools/honeybee-energy-standards
-    """
 
-    # CUSTOM MATERIALS
-    FABRIC: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Fabric",
-        Roughness="Smooth",
-        Thickness=0.002,
-        Conductivity=0.06,
-        Density=500.0,
-        SpecificHeat=1800.0,
-        ThermalAbsorptance=0.89,
-        SolarAbsorptance=0.5,
-        VisibleAbsorptance=0.5,
-        Source="Buro Happold",
-    )
-    SHRUBS: OpaqueVegetationMaterial = OpaqueVegetationMaterial(
-        Identifier="Shrubs",
-        Roughness="Rough",
-        Thickness=0.1,
-        Conductivity=0.35,
-        Density=1260,
-        SpecificHeat=1100,
-        SoilThermalAbsorptance=0.9,
-        SoilSolarAbsorptance=0.7,
-        SoilVisibleAbsorptance=0.7,
-        PlantHeight=0.2,
-        LeafAreaIndex=2.08,
-        LeafReflectivity=0.21,
-        LeafEmissivity=0.95,
-        MinStomatalResist=180,
-        Source="Buro Happold",
-    )
-    TRAVERTINE: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Travertine",
-        Roughness="MediumRough",
-        Thickness=0.2,
-        Conductivity=3.2,
-        Density=2700.0,
-        SpecificHeat=790.0,
-        ThermalAbsorptance=0.96,
-        SolarAbsorptance=0.55,
-        VisibleAbsorptance=0.55,
-        Source="Buro Happold",
-    )
+def get_material(material_identifier: str) -> _EnergyMaterialOpaqueBase:
+    """Get a material from its name.
 
-    # MATERIALS FROM ICE TOOL
-    SD01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Sand (Quartzite_Beige/brown/black_0.26 albedo)",
-        Thickness=0.01,
-        Conductivity=5.0,
-        Density=2600,
-        SpecificHeat=690,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Rough",
-        ThermalAbsorptance=0.92,
-        SolarAbsorptance=0.74,
-        VisibleAbsorptance=0.74,
-    )
-    SD02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Sand (Quartzite_Beige/brown/black_0.32 albedo)",
-        Thickness=0.01,
-        Conductivity=5.0,
-        Density=2600,
-        SpecificHeat=690,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Rough",
-        ThermalAbsorptance=0.96,
-        SolarAbsorptance=0.6799999999999999,
-        VisibleAbsorptance=0.6799999999999999,
-    )
-    SD03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Sand (Quartzite_Brown_0.25 albedo)",
-        Thickness=0.01,
-        Conductivity=5.0,
-        Density=2600,
-        SpecificHeat=690,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Rough",
-        ThermalAbsorptance=0.97,
-        SolarAbsorptance=0.75,
-        VisibleAbsorptance=0.75,
-    )
-    ST01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Sandstone_Beige_0.4 albedo)",
-        Thickness=0.01,
-        Conductivity=3.0,
-        Density=2000,
-        SpecificHeat=745,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Smooth",
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.6,
-        VisibleAbsorptance=0.6,
-    )
-    ST02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Carboniferous Coral Limestone_Grey_0.2 albedo)",
-        Thickness=0.01,
-        Conductivity=2.0,
-        Density=3250,
-        SpecificHeat=1180,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Smooth",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.8,
-        VisibleAbsorptance=0.8,
-    )
-    ST03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Sandstone_Yellow_0.26 albedo)",
-        Thickness=0.01,
-        Conductivity=3.0,
-        Density=2000,
-        SpecificHeat=745,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Smooth",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.74,
-        VisibleAbsorptance=0.74,
-    )
-    ST04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Limestone_Beige_0.68 albedo)",
-        Thickness=0.01,
-        Conductivity=2.0,
-        Density=3250,
-        SpecificHeat=1180,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Smooth",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.31999999999999995,
-        VisibleAbsorptance=0.31999999999999995,
-    )
-    ST05: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Sandstone_Light grey_0.46 albedo)",
-        Thickness=0.01,
-        Conductivity=3.0,
-        Density=2000,
-        SpecificHeat=745,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Smooth",
-        ThermalAbsorptance=0.92,
-        SolarAbsorptance=0.54,
-        VisibleAbsorptance=0.54,
-    )
-    ST06: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Granite_White/black_0.48 albedo)",
-        Thickness=0.01,
-        Conductivity=2.7,
-        Density=2600,
-        SpecificHeat=280,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.92,
-        SolarAbsorptance=0.52,
-        VisibleAbsorptance=0.52,
-    )
-    ST07: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Granite With Cement_White/red_0.34 albedo)",
-        Thickness=0.01,
-        Conductivity=2.7,
-        Density=2600,
-        SpecificHeat=280,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.6599999999999999,
-        VisibleAbsorptance=0.6599999999999999,
-    )
-    ST08: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Granite With Cement_White/black_0.41 albedo)",
-        Thickness=0.01,
-        Conductivity=2.7,
-        Density=2600,
-        SpecificHeat=280,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.89,
-        SolarAbsorptance=0.5900000000000001,
-        VisibleAbsorptance=0.5900000000000001,
-    )
-    ST09: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Granite_White/red/black_0.54 albedo)",
-        Thickness=0.01,
-        Conductivity=2.7,
-        Density=2600,
-        SpecificHeat=280,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.45999999999999996,
-        VisibleAbsorptance=0.45999999999999996,
-    )
-    ST10: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stone (Granite_Red/black_0.22 albedo)",
-        Thickness=0.01,
-        Conductivity=2.7,
-        Density=2600,
-        SpecificHeat=280,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Smooth",
-        ThermalAbsorptance=0.89,
-        SolarAbsorptance=0.78,
-        VisibleAbsorptance=0.78,
-    )
-    AS01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Asphalt (Asphalt With Stone Aggregate_Black/grey_0.21 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.96,
-        SolarAbsorptance=0.79,
-        VisibleAbsorptance=0.79,
-    )
-    AS02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Asphalt (Asphalt With Stone Aggregate_Black/grey_0.18 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.8200000000000001,
-        VisibleAbsorptance=0.8200000000000001,
-    )
-    AS03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Asphalt (Asphalt With Stone Aggregate_Black/grey_0.21 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.79,
-        VisibleAbsorptance=0.79,
-    )
-    AS04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Asphalt (Asphalt With Stone Aggregate_Black/grey_0.18 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.8200000000000001,
-        VisibleAbsorptance=0.8200000000000001,
-    )
-    AS05: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Asphalt (Asphalt With Stone Aggregate_Black/grey_0.19 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.81,
-        VisibleAbsorptance=0.81,
-    )
-    AS06: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Asphalt (Asphalt With Stone Aggregate_Black/grey_0.12 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.91,
-        SolarAbsorptance=0.88,
-        VisibleAbsorptance=0.88,
-    )
-    TR01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Tarmac (Tarmac Roofing Paper_Grey_0.07 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.9299999999999999,
-        VisibleAbsorptance=0.9299999999999999,
-    )
-    TR02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Tarmac (Tarmac_Black_0.13 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.87,
-        VisibleAbsorptance=0.87,
-    )
-    TR03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Tarmac (Tarmac_Black_0.08 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.92,
-        VisibleAbsorptance=0.92,
-    )
-    TR04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Tarmac (Tarmac_Black_0.1 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.96,
-        SolarAbsorptance=0.9,
-        VisibleAbsorptance=0.9,
-    )
-    CM01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement_Grey/ochre_0.29 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.71,
-        VisibleAbsorptance=0.71,
-    )
-    CC01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Concrete (Light Concrete_Grey/white_0.21 albedo)",
-        Thickness=0.01,
-        Conductivity=0.21,
-        Density=2800,
-        SpecificHeat=657,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.92,
-        SolarAbsorptance=0.79,
-        VisibleAbsorptance=0.79,
-    )
-    CM02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement_Grey_0.23 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.91,
-        SolarAbsorptance=0.77,
-        VisibleAbsorptance=0.77,
-    )
-    CC02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Concrete (Concrete_Grey_0.37 albedo)",
-        Thickness=0.01,
-        Conductivity=0.21,
-        Density=2800,
-        SpecificHeat=657,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.63,
-        VisibleAbsorptance=0.63,
-    )
-    CM03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement_Grey_0.41 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.5900000000000001,
-        VisibleAbsorptance=0.5900000000000001,
-    )
-    CC03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Concrete (Concrete_White_0.42 albedo)",
-        Thickness=0.01,
-        Conductivity=0.21,
-        Density=2800,
-        SpecificHeat=657,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.5800000000000001,
-        VisibleAbsorptance=0.5800000000000001,
-    )
-    CC04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Concrete (Concrete_Grey_0.25 albedo)",
-        Thickness=0.01,
-        Conductivity=0.21,
-        Density=2800,
-        SpecificHeat=657,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Rough",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.75,
-        VisibleAbsorptance=0.75,
-    )
-    CM04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement Brick_Yellow_0.3 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-    )
-    CM05: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement Brick_With Sand_Black/light grey_0.11 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.89,
-        VisibleAbsorptance=0.89,
-    )
-    CM06: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement Brick_Black_0.09 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.91,
-        VisibleAbsorptance=0.91,
-    )
-    CL01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Clay (Brick Clay_With Cement_Red_0.31 albedo)",
-        Thickness=0.01,
-        Conductivity=1.0,
-        Density=1300,
-        SpecificHeat=1381,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.91,
-        SolarAbsorptance=0.69,
-        VisibleAbsorptance=0.69,
-    )
-    CM07: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement Brick_Red_0.17 albedo)",
-        Thickness=0.01,
-        Conductivity=0.25,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.83,
-        VisibleAbsorptance=0.83,
-    )
-    CM08: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement Brick_With Sand_Black_0.2 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.89,
-        SolarAbsorptance=0.8,
-        VisibleAbsorptance=0.8,
-    )
-    CM09: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement Brick_Light red_0.22 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.78,
-        VisibleAbsorptance=0.78,
-    )
-    CL02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Clay (Brick Clay_Light red_0.43 albedo)",
-        Thickness=0.01,
-        Conductivity=1.0,
-        Density=1300,
-        SpecificHeat=1381,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.5700000000000001,
-        VisibleAbsorptance=0.5700000000000001,
-    )
-    CM10: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement_Red_0.27 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.73,
-        VisibleAbsorptance=0.73,
-    )
-    CL03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Clay (Brick Clay_Painted_Red with beige and grey_0.53 albedo)",
-        Thickness=0.01,
-        Conductivity=1.0,
-        Density=1300,
-        SpecificHeat=1381,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.47,
-        VisibleAbsorptance=0.47,
-    )
-    CL04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Clay (Brick Clay_With Cement_Red/grey_0.35 albedo)",
-        Thickness=0.01,
-        Conductivity=1.0,
-        Density=1300,
-        SpecificHeat=1381,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.65,
-        VisibleAbsorptance=0.65,
-    )
-    CL05: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Clay (Brick Clay_Painted_Red with white_0.56 albedo)",
-        Thickness=0.01,
-        Conductivity=1.0,
-        Density=1300,
-        SpecificHeat=1381,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Rough",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.43999999999999995,
-        VisibleAbsorptance=0.43999999999999995,
-    )
-    CL06: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Clay (Brick Clay_Red_0.32 albedo)",
-        Thickness=0.01,
-        Conductivity=1.0,
-        Density=1300,
-        SpecificHeat=1381,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.6799999999999999,
-        VisibleAbsorptance=0.6799999999999999,
-    )
-    CL07: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Clay (Brick Clay_Yellow/grey_0.43 albedo)",
-        Thickness=0.01,
-        Conductivity=1.0,
-        Density=2600,
-        SpecificHeat=1381,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.96,
-        SolarAbsorptance=0.5700000000000001,
-        VisibleAbsorptance=0.5700000000000001,
-    )
-    SL01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Slate (Slate_Black_0.09 albedo)",
-        Thickness=0.01,
-        Conductivity=2.0,
-        Density=2771,
-        SpecificHeat=760,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.91,
-        VisibleAbsorptance=0.91,
-    )
-    SL02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Slate (Slate_Black_0.14 albedo)",
-        Thickness=0.01,
-        Conductivity=2.0,
-        Density=2771,
-        SpecificHeat=760,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Smooth",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.86,
-        VisibleAbsorptance=0.86,
-    )
-    FB01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Fibre Cement (Fibre Cement_Black_0.05 albedo)",
-        Thickness=0.01,
-        Conductivity=0.25,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.95,
-        VisibleAbsorptance=0.95,
-    )
-    FB02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Fibre Cement (Fibre Cement_Black_0.06 albedo)",
-        Thickness=0.01,
-        Conductivity=0.25,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.94,
-        VisibleAbsorptance=0.94,
-    )
-    CR01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ceramic (Ceramic_Red_0.31 albedo)",
-        Thickness=0.01,
-        Conductivity=5.0,
-        Density=3800,
-        SpecificHeat=790,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.69,
-        VisibleAbsorptance=0.69,
-    )
-    CR02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ceramic (Ceramic_Brown_0.2 albedo)",
-        Thickness=0.01,
-        Conductivity=5.0,
-        Density=3800,
-        SpecificHeat=790,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.8,
-        VisibleAbsorptance=0.8,
-    )
-    CR03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement_Rustic red_0.32 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.91,
-        SolarAbsorptance=0.6799999999999999,
-        VisibleAbsorptance=0.6799999999999999,
-    )
-    CR04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ceramic (Ceramic_Burnt red_0.24 albedo)",
-        Thickness=0.01,
-        Conductivity=1.99,
-        Density=3800,
-        SpecificHeat=908,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.92,
-        SolarAbsorptance=0.76,
-        VisibleAbsorptance=0.76,
-    )
-    CM11: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement_Rustic red with dark shading_0.17 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Smooth",
-        ThermalAbsorptance=0.96,
-        SolarAbsorptance=0.83,
-        VisibleAbsorptance=0.83,
-    )
-    CM12: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement_Slate grey_0.12 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.88,
-        VisibleAbsorptance=0.88,
-    )
-    CR05: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ceramic (Ceramic_Black_0.16 albedo)",
-        Thickness=0.01,
-        Conductivity=5.0,
-        Density=3800,
-        SpecificHeat=790,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.92,
-        SolarAbsorptance=0.84,
-        VisibleAbsorptance=0.84,
-    )
-    CM13: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement_Rustic red_0.26 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.74,
-        VisibleAbsorptance=0.74,
-    )
-    CM14: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Cement (Cement_Autumn red_0.19 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.92,
-        SolarAbsorptance=0.81,
-        VisibleAbsorptance=0.81,
-    )
-    CR06: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ceramic (Ceramic_Red_0.19 albedo)",
-        Thickness=0.01,
-        Conductivity=5.0,
-        Density=3800,
-        SpecificHeat=790,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.81,
-        VisibleAbsorptance=0.81,
-    )
-    CR07: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ceramic (Ceramic_Red_0.13 albedo)",
-        Thickness=0.01,
-        Conductivity=5.0,
-        Density=3800,
-        SpecificHeat=790,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.87,
-        VisibleAbsorptance=0.87,
-    )
-    CR08: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ceramic (Ceramic_Red_0.12 albedo)",
-        Thickness=0.01,
-        Conductivity=5.0,
-        Density=3800,
-        SpecificHeat=790,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.88,
-        VisibleAbsorptance=0.88,
-    )
-    MT01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal (Aluminium Plus Zinc_Grey_dull_0.36 albedo)",
-        Thickness=0.01,
-        Conductivity=45.0,
-        Density=7000,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.58,
-        SolarAbsorptance=0.64,
-        VisibleAbsorptance=0.64,
-    )
-    MT02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal (Aluminium_Stucco_Grey_shiny_0.25 albedo)",
-        Thickness=0.01,
-        Conductivity=45.0,
-        Density=7000,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.16,
-        SolarAbsorptance=0.75,
-        VisibleAbsorptance=0.75,
-    )
-    MT03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal (Metal With Paint_Green_0.11 albedo)",
-        Thickness=0.01,
-        Conductivity=45.0,
-        Density=7000,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.89,
-        VisibleAbsorptance=0.89,
-    )
-    MT04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal (Metal With Paint_Copper patina_0.45 albedo)",
-        Thickness=0.01,
-        Conductivity=0.8,
-        Density=2100,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.55,
-        VisibleAbsorptance=0.55,
-    )
-    MT05: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal (Metal With Paint_Slate grey_0.12 albedo)",
-        Thickness=0.01,
-        Conductivity=1.0,
-        Density=2200,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.88,
-        VisibleAbsorptance=0.88,
-    )
-    MT06: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal (Aluminium_Grey_0.26 albedo)",
-        Thickness=0.01,
-        Conductivity=45.0,
-        Density=2300,
-        SpecificHeat=1030,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.81,
-        SolarAbsorptance=0.74,
-        VisibleAbsorptance=0.74,
-    )
-    MT07: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal (Lead_Grey_0.21 albedo)",
-        Thickness=0.01,
-        Conductivity=45.0,
-        Density=2300,
-        SpecificHeat=1400,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.86,
-        SolarAbsorptance=0.79,
-        VisibleAbsorptance=0.79,
-    )
-    MT08: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal (Iron_Black_0.05 albedo)",
-        Thickness=0.01,
-        Conductivity=45.0,
-        Density=2300,
-        SpecificHeat=2580,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumSmooth",
-        ThermalAbsorptance=0.97,
-        SolarAbsorptance=0.95,
-        VisibleAbsorptance=0.95,
-    )
-    PV01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Pvc (Pvc Roofing Material_Lead grey_0.08 albedo)",
-        Thickness=0.01,
-        Conductivity=0.19,
-        Density=2300,
-        SpecificHeat=1600,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.92,
-        VisibleAbsorptance=0.92,
-    )
-    PV02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Pvc (Pvc Roofing Material_Light grey_0.43 albedo)",
-        Thickness=0.01,
-        Conductivity=0.19,
-        Density=2300,
-        SpecificHeat=1600,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.5700000000000001,
-        VisibleAbsorptance=0.5700000000000001,
-    )
-    PV03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Pvc (Pvc Roofing Material_Copper brown_0.29 albedo)",
-        Thickness=0.01,
-        Conductivity=0.19,
-        Density=2300,
-        SpecificHeat=1600,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="Rough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.71,
-        VisibleAbsorptance=0.71,
-    )
-    PV04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Pvc (Pvc Roofing Material_Azure blue_0.14 albedo)",
-        Thickness=0.01,
-        Conductivity=0.19,
-        Density=2300,
-        SpecificHeat=1600,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.86,
-        VisibleAbsorptance=0.86,
-    )
-    PV05: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Pvc (Pvc Roofing Material_Copper brown_0.17 albedo)",
-        Thickness=0.01,
-        Conductivity=0.19,
-        Density=2300,
-        SpecificHeat=1600,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.83,
-        VisibleAbsorptance=0.83,
-    )
-    PV06: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Pvc (Pvc Roofing Material_Copper patina_0.28 albedo)",
-        Thickness=0.01,
-        Conductivity=0.19,
-        Density=2300,
-        SpecificHeat=1600,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.94,
-        SolarAbsorptance=0.72,
-        VisibleAbsorptance=0.72,
-    )
-    VG01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Grass (Grass Green Watered_Green_0.27 albedo)",
-        Thickness=0.01,
-        Conductivity=0.28,
-        Density=1600,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.98,
-        SolarAbsorptance=0.73,
-        VisibleAbsorptance=0.73,
-    )
-    VG02: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Grass (Grass Dry _Yellow_0.17 albedo)",
-        Thickness=0.01,
-        Conductivity=0.28,
-        Density=1600,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.98,
-        SolarAbsorptance=0.83,
-        VisibleAbsorptance=0.83,
-    )
-    VG03: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Dense Forest (Dense Forest_Green_0.27 albedo)",
-        Thickness=0.01,
-        Conductivity=0.28,
-        Density=1600,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated 25/06/2021)",
-        Roughness="VeryRough",
-        ThermalAbsorptance=0.98,
-        SolarAbsorptance=0.73,
-        VisibleAbsorptance=0.73,
-    )
-    VG04: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Wood (Tree_Brown_0.4 albedo)",
-        Thickness=0.01,
-        Conductivity=0.11,
-        Density=545,
-        SpecificHeat=200,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated nan)",
-        Roughness="Smooth",
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.6,
-        VisibleAbsorptance=0.6,
-    )
-    VS01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Soil (Wood_Brown_0.35 albedo)",
-        Thickness=0.01,
-        Conductivity=0.28,
-        Density=1600,
-        SpecificHeat=800,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated nan)",
-        Roughness="VeryRough",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.65,
-        VisibleAbsorptance=0.65,
-    )
-    WT01: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Water (Water Small_-_0.07 albedo)",
-        Thickness=0.01,
-        Conductivity=0.68,
-        Density=997,
-        SpecificHeat=4184,
-        Source="https://github.com/Art-Ev/ICEtool_sources (last updated nan)",
-        Roughness="MediumRough",
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.9299999999999999,
-        VisibleAbsorptance=0.9299999999999999,
-    )
+    Args:
+        material_identifier (str): The identifier of the material.
 
-    # MATERIALS FROM LBT
-    LBT_12InGypsumBoard: OpaqueMaterial = OpaqueMaterial(
-        Identifier="1/2 in. Gypsum Board",
-        Roughness="Smooth",
-        Thickness=0.0127,
-        Conductivity=0.15989299909405463,
-        Density=800.0018291911765,
-        SpecificHeat=1089.2971854559414,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.5,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_12InGypsum: OpaqueMaterial = OpaqueMaterial(
-        Identifier="1/2IN Gypsum",
-        Roughness="Smooth",
-        Thickness=0.0127,
-        Conductivity=0.15989299909405608,
-        Density=784.9017946651944,
-        SpecificHeat=829.4648292921422,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.4,
-        VisibleAbsorptance=0.4,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_100MmNormalweightConcreteFloor: OpaqueMaterial = OpaqueMaterial(
-        Identifier="100mm Normalweight concrete floor",
-        Roughness="MediumRough",
-        Thickness=0.1016,
-        Conductivity=2.3089016425031033,
-        Density=2322.006775596,
-        SpecificHeat=832.0199665271966,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_12InNormalweightConcreteFloor: OpaqueMaterial = OpaqueMaterial(
-        Identifier="12 in. Normalweight Concrete Floor",
-        Roughness="MediumRough",
-        Thickness=0.30479999999999996,
-        Conductivity=2.308455174420426,
-        Density=2322.005309227381,
-        SpecificHeat=831.4635397241673,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_1InStucco: OpaqueMaterial = OpaqueMaterial(
-        Identifier="1IN Stucco",
-        Roughness="Smooth",
-        Thickness=0.025299999999999993,
-        Conductivity=0.6913373548329222,
-        Density=1858.004248296516,
-        SpecificHeat=836.4603158042426,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.92,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_25MmStucco: OpaqueMaterial = OpaqueMaterial(
-        Identifier="25mm Stucco",
-        Roughness="Smooth",
-        Thickness=0.0254,
-        Conductivity=0.7195184959232496,
-        Density=1856.0042437235265,
-        SpecificHeat=839.4583814522845,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_4InNormalweightConcreteFloor: OpaqueMaterial = OpaqueMaterial(
-        Identifier="4 in. Normalweight Concrete Floor",
-        Roughness="MediumRough",
-        Thickness=0.1016,
-        Conductivity=2.308455174420426,
-        Density=2322.005309227381,
-        SpecificHeat=831.4635397241673,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_4InNormalweightConcreteWall: OpaqueMaterial = OpaqueMaterial(
-        Identifier="4 in. Normalweight Concrete Wall",
-        Roughness="MediumRough",
-        Thickness=0.1016,
-        Conductivity=2.308455174420426,
-        Density=2322.005309227381,
-        SpecificHeat=831.4635397241673,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_58InGypsumBoard: OpaqueMaterial = OpaqueMaterial(
-        Identifier="5/8 in. Gypsum Board",
-        Roughness="MediumSmooth",
-        Thickness=0.0159,
-        Conductivity=0.15989299909405463,
-        Density=800.0018291911765,
-        SpecificHeat=1089.2971854559414,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_58InPlywood: OpaqueMaterial = OpaqueMaterial(
-        Identifier="5/8 in. Plywood",
-        Roughness="Smooth",
-        Thickness=0.0159,
-        Conductivity=0.11991974932054163,
-        Density=544.0012438499998,
-        SpecificHeat=1209.2198113776988,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_6InHeavyweightConcreteRoof: OpaqueMaterial = OpaqueMaterial(
-        Identifier="6 in. Heavyweight Concrete Roof",
-        Roughness="MediumRough",
-        Thickness=0.15239999999999998,
-        Conductivity=2.308455174420426,
-        Density=2322.005309227381,
-        SpecificHeat=831.4635397241673,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_6InNormalweightConcreteFloor: OpaqueMaterial = OpaqueMaterial(
-        Identifier="6 in. Normalweight Concrete Floor",
-        Roughness="MediumRough",
-        Thickness=0.15239999999999998,
-        Conductivity=2.308455174420426,
-        Density=2322.005309227381,
-        SpecificHeat=831.4635397241673,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_8InConcreteBlockBasementWall: OpaqueMaterial = OpaqueMaterial(
-        Identifier="8 in. Concrete Block Basement Wall",
-        Roughness="MediumRough",
-        Thickness=0.2032,
-        Conductivity=1.325113229991986,
-        Density=1842.0042117126811,
-        SpecificHeat=911.4119570053389,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_8InConcreteBlockWall: OpaqueMaterial = OpaqueMaterial(
-        Identifier="8 in. Concrete Block Wall",
-        Roughness="MediumRough",
-        Thickness=0.2032,
-        Conductivity=0.7195184959232496,
-        Density=800.0018291911765,
-        SpecificHeat=831.4635397241673,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_8InNormalweightConcreteFloor: OpaqueMaterial = OpaqueMaterial(
-        Identifier="8 in. Normalweight Concrete Floor",
-        Roughness="MediumRough",
-        Thickness=0.2032,
-        Conductivity=2.308455174420426,
-        Density=2322.005309227381,
-        SpecificHeat=831.4635397241673,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_8InNormalweightConcreteWall: OpaqueMaterial = OpaqueMaterial(
-        Identifier="8 in. Normalweight Concrete Wall",
-        Roughness="MediumRough",
-        Thickness=0.2032,
-        Conductivity=2.308455174420426,
-        Density=2322.005309227381,
-        SpecificHeat=831.4635397241673,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_8InConcreteHwRefbldg: OpaqueMaterial = OpaqueMaterial(
-        Identifier="8IN CONCRETE HW RefBldg",
-        Roughness="Rough",
-        Thickness=0.2032,
-        Conductivity=1.3101232613269185,
-        Density=2240.0051217352993,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_8InConcreteHw: OpaqueMaterial = OpaqueMaterial(
-        Identifier="8IN Concrete HW",
-        Roughness="MediumRough",
-        Thickness=0.2033000000000001,
-        Conductivity=1.7284433202067357,
-        Density=2243.0051285947593,
-        SpecificHeat=836.4603158042426,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.65,
-        VisibleAbsorptance=0.65,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_AcousticCeiling: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Acoustic Ceiling",
-        Roughness="MediumSmooth",
-        Thickness=0.0127,
-        Conductivity=0.056961880927257194,
-        Density=288.0006585088233,
-        SpecificHeat=1338.1366342435858,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.2,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_AsphaltShingles: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Asphalt Shingles",
-        Roughness="VeryRough",
-        Thickness=0.003199999999999976,
-        Conductivity=0.03997324977351388,
-        Density=1120.002560867648,
-        SpecificHeat=1259.1875721784268,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_AtticfloorInsulation: OpaqueMaterial = OpaqueMaterial(
-        Identifier="AtticFloor Insulation",
-        Roughness="MediumRough",
-        Thickness=0.23789999999999986,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_BuiltUpRoofing: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Built-up Roofing",
-        Roughness="Rough",
-        Thickness=0.009499999999999998,
-        Conductivity=0.15989299909405463,
-        Density=1120.002560867648,
-        SpecificHeat=1459.0586153813556,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_BuiltUpRoofingHighlyReflective: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Built-up Roofing - Highly Reflective",
-        Roughness="Rough",
-        Thickness=0.009499999999999998,
-        Conductivity=0.15989299909405463,
-        Density=1120.002560867648,
-        SpecificHeat=1459.0586153813556,
-        ThermalAbsorptance=0.75,
-        SolarAbsorptance=0.45,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_BulkStorageProductsMaterial: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Bulk Storage Products Material",
-        Roughness="Rough",
-        Thickness=2.4384049429641195,
-        Conductivity=1.4410356988618083,
-        Density=200.23111933632342,
-        SpecificHeat=836.2604510890168,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Carpet34InCbes: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Carpet - 3/4 in. CBES",
-        Roughness="Smooth",
-        Thickness=0.019049999999999997,
-        Conductivity=0.05000063634028838,
-        Density=288.33299999999997,
-        SpecificHeat=1380.753138075314,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_F08MetalSurface: OpaqueMaterial = OpaqueMaterial(
-        Identifier="F08 Metal surface",
-        Roughness="Smooth",
-        Thickness=0.0008000000000000003,
-        Conductivity=45.249718743617805,
-        Density=7824.017889489713,
-        SpecificHeat=499.6776080073138,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_F16AcousticTile: OpaqueMaterial = OpaqueMaterial(
-        Identifier="F16 Acoustic tile",
-        Roughness="MediumSmooth",
-        Thickness=0.019100000000000002,
-        Conductivity=0.05995987466027089,
-        Density=368.0008414279416,
-        SpecificHeat=589.6195774486317,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.3,
-        VisibleAbsorptance=0.3,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_G0113MmGypsumBoard: OpaqueMaterial = OpaqueMaterial(
-        Identifier="G01 13mm gypsum board",
-        Roughness="Smooth",
-        Thickness=0.0127,
-        Conductivity=0.15992392281605958,
-        Density=800.0023344,
-        SpecificHeat=1090.0261589958159,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.5,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_G01A19MmGypsumBoard: OpaqueMaterial = OpaqueMaterial(
-        Identifier="G01a 19mm gypsum board",
-        Roughness="MediumSmooth",
-        Thickness=0.018999999999999996,
-        Conductivity=0.15989299909405608,
-        Density=800.0018291911781,
-        SpecificHeat=1089.2971854559455,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.4,
-        VisibleAbsorptance=0.4,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_G0525MmWood: OpaqueMaterial = OpaqueMaterial(
-        Identifier="G05 25mm wood",
-        Roughness="MediumSmooth",
-        Thickness=0.0254,
-        Conductivity=0.14989968665067757,
-        Density=608.0013901852949,
-        SpecificHeat=1628.949002103841,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.5,
-        VisibleAbsorptance=0.5,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Ground_Floor_R11_T2013: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ground_Floor_R11_T2013",
-        Roughness="Smooth",
-        Thickness=0.044227242,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Ground_Floor_R17_T2013: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ground_Floor_R17_T2013",
-        Roughness="Smooth",
-        Thickness=0.06891629599999999,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Ground_Floor_R22_T2013: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Ground_Floor_R22_T2013",
-        Roughness="Smooth",
-        Thickness=0.08918575,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_GypsumBoard12InCbes: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Gypsum Board - 1/2 in. CBES",
-        Roughness="Smooth",
-        Thickness=0.0127,
-        Conductivity=0.16000030671169507,
-        Density=640.74,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_GypsumOrPlasterBoard38In: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Gypsum Or Plaster Board - 3/8 in.",
-        Roughness="MediumSmooth",
-        Thickness=0.009499999999999998,
-        Conductivity=0.5796121217159504,
-        Density=800.0018291911765,
-        SpecificHeat=1089.2971854559414,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_HwConcrete: OpaqueMaterial = OpaqueMaterial(
-        Identifier="HW CONCRETE",
-        Roughness="Rough",
-        Thickness=0.1016,
-        Conductivity=1.310376644700027,
-        Density=2240.0065363199997,
-        SpecificHeat=836.8200836820084,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_HwConcrete8In: OpaqueMaterial = OpaqueMaterial(
-        Identifier="HW CONCRETE 8 in",
-        Roughness="Rough",
-        Thickness=0.2032,
-        Conductivity=1.310376644700027,
-        Density=2240.0065363199997,
-        SpecificHeat=836.8200836820084,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_I0125MmInsulationBoard: OpaqueMaterial = OpaqueMaterial(
-        Identifier="I01 25mm insulation board",
-        Roughness="MediumRough",
-        Thickness=0.0254,
-        Conductivity=0.029979937330135372,
-        Density=43.000098319025845,
-        SpecificHeat=1209.2198113776988,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.6,
-        VisibleAbsorptance=0.6,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_I0250MmInsulationBoard: OpaqueMaterial = OpaqueMaterial(
-        Identifier="I02 50mm insulation board",
-        Roughness="MediumRough",
-        Thickness=0.0508,
-        Conductivity=0.029979937330135372,
-        Density=43.000098319025845,
-        SpecificHeat=1209.2198113776988,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.6,
-        VisibleAbsorptance=0.6,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_IeadNonresRoofInsulation176: OpaqueMaterial = OpaqueMaterial(
-        Identifier="IEAD NonRes Roof Insulation-1.76",
-        Roughness="MediumRough",
-        Thickness=0.07398050297987159,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_IeadRoofInsulationR347Ip: OpaqueMaterial = OpaqueMaterial(
-        Identifier="IEAD Roof Insulation R-3.47 IP",
-        Roughness="MediumRough",
-        Thickness=0.029942459748632622,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Insulation1M: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Insulation 1m",
-        Roughness="Smooth",
-        Thickness=0.9999979999999999,
-        Conductivity=0.01999967801037276,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_M01100MmBrick: OpaqueMaterial = OpaqueMaterial(
-        Identifier="M01 100mm brick",
-        Roughness="MediumRough",
-        Thickness=0.1016,
-        Conductivity=0.8894048074606842,
-        Density=1920.0043900588325,
-        SpecificHeat=789.4906206515565,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_M11100MmLightweightConcrete: OpaqueMaterial = OpaqueMaterial(
-        Identifier="M11 100mm lightweight concrete",
-        Roughness="MediumRough",
-        Thickness=0.1016,
-        Conductivity=0.5296455594990593,
-        Density=1280.0029267058849,
-        SpecificHeat=839.4583814522887,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.5,
-        VisibleAbsorptance=0.5,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_M15200MmHeavyweightConcrete: OpaqueMaterial = OpaqueMaterial(
-        Identifier="M15 200mm heavyweight concrete",
-        Roughness="MediumRough",
-        Thickness=0.2032,
-        Conductivity=1.9486959264588086,
-        Density=2240.0051217352993,
-        SpecificHeat=899.4196944131631,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.5,
-        VisibleAbsorptance=0.5,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MatCc054HwConcrete: OpaqueMaterial = OpaqueMaterial(
-        Identifier="MAT-CC05 4 HW CONCRETE",
-        Roughness="Rough",
-        Thickness=0.1016,
-        Conductivity=1.3101232613269185,
-        Density=2240.0051217352993,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.85,
-        VisibleAbsorptance=0.85,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MatCc058HwConcrete: OpaqueMaterial = OpaqueMaterial(
-        Identifier="MAT-CC05 8 HW CONCRETE",
-        Roughness="Rough",
-        Thickness=0.2032,
-        Conductivity=1.3101232613269185,
-        Density=2240.0051217352993,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.85,
-        VisibleAbsorptance=0.85,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MassNonresWallInsulation043: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Mass NonRes Wall Insulation-0.43",
-        Roughness="MediumRough",
-        Thickness=0.0004367814266280437,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MassWallInsulationR423Ip: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Mass Wall Insulation R-4.23 IP",
-        Roughness="MediumRough",
-        Thickness=0.0365371685816,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalBuildingSemiCondWallInsulation054: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Building Semi-Cond Wall Insulation-0.54",
-        Roughness="MediumRough",
-        Thickness=0.015345987404915393,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalBuildingWallInsulationR414Ip: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Building Wall Insulation R-4.14 IP",
-        Roughness="MediumRough",
-        Thickness=0.0356729086642,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalDecking: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Decking",
-        Roughness="MediumSmooth",
-        Thickness=0.0014999999999999994,
-        Conductivity=44.97590198266915,
-        Density=7680.017560235314,
-        SpecificHeat=418.1302223805201,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.6,
-        VisibleAbsorptance=0.6,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalRoofInsulationR521Ip: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Roof Insulation R-5.21 IP",
-        Roughness="MediumRough",
-        Thickness=0.044938850457399995,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalRoofSurface: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Roof Surface",
-        Roughness="Smooth",
-        Thickness=0.0007999999999999979,
-        Conductivity=45.24971874361766,
-        Density=7824.017889489713,
-        SpecificHeat=499.67760800730963,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalRoofSurfaceHighlyReflective: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Roof Surface - Highly Reflective",
-        Roughness="Smooth",
-        Thickness=0.0007999999999999979,
-        Conductivity=45.24971874361766,
-        Density=7824.017889489713,
-        SpecificHeat=499.67760800730963,
-        ThermalAbsorptance=0.75,
-        SolarAbsorptance=0.45,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalRoofing: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Roofing",
-        Roughness="MediumSmooth",
-        Thickness=0.0014999999999999994,
-        Conductivity=44.97590198266915,
-        Density=7680.017560235314,
-        SpecificHeat=418.1302223805201,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.6,
-        VisibleAbsorptance=0.6,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalRoofingHighlyReflective: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Roofing - Highly Reflective",
-        Roughness="MediumSmooth",
-        Thickness=0.0014999999999999994,
-        Conductivity=44.97590198266915,
-        Density=7680.017560235314,
-        SpecificHeat=418.1302223805201,
-        ThermalAbsorptance=0.75,
-        SolarAbsorptance=0.45,
-        VisibleAbsorptance=0.6,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalSemiCondRoofInsulation105: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Semi-Cond Roof Insulation-1.05",
-        Roughness="MediumRough",
-        Thickness=0.04226733019063131,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalSiding: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Siding",
-        Roughness="Smooth",
-        Thickness=0.0014999999999999994,
-        Conductivity=44.92993274542956,
-        Density=7688.877580493597,
-        SpecificHeat=409.7356385659975,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalStandingSeam116InCbes: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Standing Seam - 1/16 in. CBES",
-        Roughness="MediumRough",
-        Thickness=0.001524,
-        Conductivity=0.579999310186949,
-        Density=7820.552070000001,
-        SpecificHeat=502.09205020920496,
-        ThermalAbsorptance=0.85,
-        SolarAbsorptance=0.37,
-        VisibleAbsorptance=0.85,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MetalSurface: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Metal Surface",
-        Roughness="Smooth",
-        Thickness=0.0007999999999999979,
-        Conductivity=45.24971874361766,
-        Density=7824.017889489713,
-        SpecificHeat=499.67760800730963,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Nacm_Carpet34In: OpaqueMaterial = OpaqueMaterial(
-        Identifier="NACM_Carpet 3/4in",
-        Roughness="Smooth",
-        Thickness=0.019049999999999997,
-        Conductivity=0.05000063634028838,
-        Density=288.33299999999997,
-        SpecificHeat=1380.753138075314,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.9,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Nacm_Concrete4In_140LbFt3: OpaqueMaterial = OpaqueMaterial(
-        Identifier="NACM_Concrete 4in_140lb/ft3",
-        Roughness="MediumRough",
-        Thickness=0.1016,
-        Conductivity=1.9500003149271867,
-        Density=2239.0659299999998,
-        SpecificHeat=920.5020920502092,
-        ThermalAbsorptance=0.75,
-        SolarAbsorptance=0.92,
-        VisibleAbsorptance=0.75,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Nacm_GypsumBoard58In: OpaqueMaterial = OpaqueMaterial(
-        Identifier="NACM_Gypsum Board 5/8in",
-        Roughness="MediumRough",
-        Thickness=0.016002,
-        Conductivity=0.16000030671169507,
-        Density=640.74,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.5,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Plywood58InCbes: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Plywood - 5/8 in. CBES",
-        Roughness="Smooth",
-        Thickness=0.016002,
-        Conductivity=0.11999950937659305,
-        Density=480.555,
-        SpecificHeat=1882.8451882845188,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_R1_R1420: OpaqueMaterial = OpaqueMaterial(
-        Identifier="R1_R14.20",
-        Roughness="Smooth",
-        Thickness=0.05756529,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_R2_R1290: OpaqueMaterial = OpaqueMaterial(
-        Identifier="R2_R12.90",
-        Roughness="Smooth",
-        Thickness=0.052295298000000004,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_R3_R1774: OpaqueMaterial = OpaqueMaterial(
-        Identifier="R3_R17.74",
-        Roughness="Smooth",
-        Thickness=0.071916036,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_R4_R2028: OpaqueMaterial = OpaqueMaterial(
-        Identifier="R4_R20.28",
-        Roughness="Smooth",
-        Thickness=0.082211418,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_R_R30: OpaqueMaterial = OpaqueMaterial(
-        Identifier="R_R30",
-        Roughness="Smooth",
-        Thickness=0.121616978,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_R_R38: OpaqueMaterial = OpaqueMaterial(
-        Identifier="R_R38",
-        Roughness="Smooth",
-        Thickness=0.154048206,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_R_T24_2013_2486: OpaqueMaterial = OpaqueMaterial(
-        Identifier="R_T24_2013_24.86",
-        Roughness="Smooth",
-        Thickness=0.100779834,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_RoofInsulation18: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Roof Insulation [18]",
-        Roughness="MediumRough",
-        Thickness=0.16929999999999995,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_RoofMembrane: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Roof Membrane",
-        Roughness="VeryRough",
-        Thickness=0.009499999999999998,
-        Conductivity=0.15989299909405608,
-        Density=1121.29256381722,
-        SpecificHeat=1459.0586153813556,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_RoofMembraneHighlyReflective: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Roof Membrane - Highly Reflective",
-        Roughness="VeryRough",
-        Thickness=0.009499999999999998,
-        Conductivity=0.15989299909405608,
-        Density=1121.29256381722,
-        SpecificHeat=1459.0586153813556,
-        ThermalAbsorptance=0.75,
-        SolarAbsorptance=0.45,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_StdWood6In: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Std Wood 6 in.",
-        Roughness="MediumSmooth",
-        Thickness=0.14999999999999997,
-        Conductivity=0.11991974932054163,
-        Density=540.0012347040437,
-        SpecificHeat=1209.2198113776988,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_StdWood6InchFurnishings: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Std Wood 6inch Furnishings",
-        Roughness="MediumSmooth",
-        Thickness=0.150114,
-        Conductivity=0.11999950937659305,
-        Density=539.983635,
-        SpecificHeat=1213.389121338912,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_SteelFrameNonresWallInsulation073: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Steel Frame NonRes Wall Insulation-0.73",
-        Roughness="MediumRough",
-        Thickness=0.020278246975300705,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_SteelFrameWallInsulationR102Ip: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Steel Frame Wall Insulation R-1.02 IP",
-        Roughness="MediumRough",
-        Thickness=0.008836428479,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_SteelFrameCavity: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Steel Frame/Cavity",
-        Roughness="MediumRough",
-        Thickness=0.08890018021223342,
-        Conductivity=0.08405993542170573,
-        Density=641.3803214581126,
-        SpecificHeat=501.75627065341007,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Stucco78InCbes: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Stucco - 7/8 in. CBES",
-        Roughness="MediumRough",
-        Thickness=0.022352,
-        Conductivity=0.7000002608778985,
-        Density=1855.102485,
-        SpecificHeat=836.8200836820084,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_W1_R860: OpaqueMaterial = OpaqueMaterial(
-        Identifier="W1_R8.60",
-        Roughness="Smooth",
-        Thickness=0.034862008,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_W2_R1113: OpaqueMaterial = OpaqueMaterial(
-        Identifier="W2_R11.13",
-        Roughness="Smooth",
-        Thickness=0.045119036,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_W3_R1136: OpaqueMaterial = OpaqueMaterial(
-        Identifier="W3_R11.36",
-        Roughness="Smooth",
-        Thickness=0.04605147,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_W4_R1262: OpaqueMaterial = OpaqueMaterial(
-        Identifier="W4_R12.62",
-        Roughness="Smooth",
-        Thickness=0.05115915599999999,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_W_T24_2013_R1399: OpaqueMaterial = OpaqueMaterial(
-        Identifier="W_T24_2013_R13.99",
-        Roughness="Smooth",
-        Thickness=0.056714136,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_W_M1_R15: OpaqueMaterial = OpaqueMaterial(
-        Identifier="W_m1_R15",
-        Roughness="Smooth",
-        Thickness=0.060808362,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_W_M2_R19: OpaqueMaterial = OpaqueMaterial(
-        Identifier="W_m2_R19",
-        Roughness="Smooth",
-        Thickness=0.077023976,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_W_M3_R21: OpaqueMaterial = OpaqueMaterial(
-        Identifier="W_m3_R21",
-        Roughness="Smooth",
-        Thickness=0.08513190999999999,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_WallInsulation31: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Wall Insulation [31]",
-        Roughness="MediumRough",
-        Thickness=0.03370000000000008,
-        Conductivity=0.043171109755394975,
-        Density=91.0002080704965,
-        SpecificHeat=836.4603158042426,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.5,
-        VisibleAbsorptance=0.5,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_WoodFrameNonresWallInsulation073: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Wood Frame NonRes Wall Insulation-0.73",
-        Roughness="MediumRough",
-        Thickness=0.020278246975300705,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_WoodFrameWallInsulationR161Ip: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Wood Frame Wall Insulation R-1.61 IP",
-        Roughness="MediumRough",
-        Thickness=0.013873826709999999,
-        Conductivity=0.04896723097255453,
-        Density=265.0006059195773,
-        SpecificHeat=836.2604447610418,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_WoodSiding: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Wood Siding",
-        Roughness="MediumSmooth",
-        Thickness=0.010000000000000004,
-        Conductivity=0.10992643687716327,
-        Density=544.6212452676245,
-        SpecificHeat=1209.2198113776988,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.78,
-        VisibleAbsorptance=0.78,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Ceiling_2_Insulation: OpaqueMaterial = OpaqueMaterial(
-        Identifier="ceiling_2_Insulation",
-        Roughness="Smooth",
-        Thickness=0.100778564,
-        Conductivity=0.02300770107232491,
-        Density=16.0185,
-        SpecificHeat=1129.7071129707113,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_DrySand: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Dry Sand",
-        Roughness="Rough",
-        Thickness=0.2,
-        Conductivity=0.33,
-        Density=1555.0,
-        SpecificHeat=800.0,
-        ThermalAbsorptance=0.85,
-        SolarAbsorptance=0.65,
-        VisibleAbsorptance=0.65,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_DryDust: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Dry Dust",
-        Roughness="Rough",
-        Thickness=0.2,
-        Conductivity=0.5,
-        Density=1600.0,
-        SpecificHeat=1026.0,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.7,
-        VisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_MoistSoil: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Moist Soil",
-        Roughness="Rough",
-        Thickness=0.2,
-        Conductivity=1.0,
-        Density=1250.0,
-        SpecificHeat=1252.0,
-        ThermalAbsorptance=0.92,
-        SolarAbsorptance=0.75,
-        VisibleAbsorptance=0.75,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_Mud: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Mud",
-        Roughness="MediumRough",
-        Thickness=0.2,
-        Conductivity=1.4,
-        Density=1840.0,
-        SpecificHeat=1480.0,
-        ThermalAbsorptance=0.95,
-        SolarAbsorptance=0.8,
-        VisibleAbsorptance=0.8,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_ConcretePavement: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Concrete Pavement",
-        Roughness="MediumRough",
-        Thickness=0.2,
-        Conductivity=1.73,
-        Density=2243.0,
-        SpecificHeat=837.0,
-        ThermalAbsorptance=0.9,
-        SolarAbsorptance=0.65,
-        VisibleAbsorptance=0.65,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_AsphaltPavement: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Asphalt Pavement",
-        Roughness="MediumRough",
-        Thickness=0.2,
-        Conductivity=0.75,
-        Density=2360.0,
-        SpecificHeat=920.0,
-        ThermalAbsorptance=0.93,
-        SolarAbsorptance=0.87,
-        VisibleAbsorptance=0.87,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_SolidRock: OpaqueMaterial = OpaqueMaterial(
-        Identifier="Solid Rock",
-        Roughness="MediumRough",
-        Thickness=0.2,
-        Conductivity=3.0,
-        Density=2700.0,
-        SpecificHeat=790.0,
-        ThermalAbsorptance=0.96,
-        SolarAbsorptance=0.55,
-        VisibleAbsorptance=0.55,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
-    LBT_GrassyLawn: OpaqueVegetationMaterial = OpaqueVegetationMaterial(
-        Identifier="Grassy Lawn",
-        PlantHeight=0.2,
-        LeafAreaIndex=1.0,
-        LeafReflectivity=0.22,
-        LeafEmissivity=0.95,
-        MinStomatalResist=180.0,
-        Roughness="MediumRough",
-        Thickness=0.1,
-        Conductivity=0.35,
-        Density=1100.0,
-        SpecificHeat=1200.0,
-        SoilThermalAbsorptance=0.9,
-        SoilSolarAbsorptance=0.7,
-        SoilVisibleAbsorptance=0.7,
-        Source=f"LadybugTools honeybee_energy_standards v{HONEYBEE_MATERIAL_STANDARDS_VERSION}",
-    )
+    Returns:
+        _EnergyMaterialBase: The material.
+    """
+    # create dict of materials
+    d = {mat.identifier: mat for mat in materials()}
+    try:
+        return d[material_identifier]
+    except KeyError:
+        try:
+            return opaque_material_by_identifier(material_identifier)
+        except ValueError as exc:
+            possible_materials = get_close_matches(
+                material_identifier, d.keys(), cutoff=0.1
+            )
+            raise KeyError(
+                f"Unknown material name: '{material_identifier}'. Did you mean {possible_materials}"
+            ) from exc
+
+
+Materials = Enum(
+    "Materials",
+    {sanitise_string(material.identifier): material for material in materials()},
+)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/model.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/model.py
index 44665ade..689e89d8 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/model.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/model.py
@@ -1,70 +1,83 @@
-import uuid
-from typing import List
+"""Methods for creating the generic external-comfort model.
+"""
 
 from honeybee.boundarycondition import boundary_conditions
 from honeybee.facetype import face_types
 from honeybee.model import Face, Model, Room, Shade
 from honeybee_energy.construction.opaque import OpaqueConstruction
 from honeybee_energy.construction.shade import ShadeConstruction
-from honeybee_energy.material.opaque import _EnergyMaterialOpaqueBase
+from honeybee_energy.material.opaque import (
+    EnergyMaterial,
+    EnergyMaterialVegetation,
+    _EnergyMaterialOpaqueBase,
+)
 from honeybee_radiance.sensorgrid import Sensor, SensorGrid
 from ladybug_geometry.geometry3d import Point3D, Vector3D
 
+from .material import _material_equality
+
+_ZONE_WIDTH = 10
+_ZONE_DEPTH = 10
+_GROUND_THICKNESS = 1
+_SHADE_HEIGHT_ABOVE_GROUND = 3
+_SHADE_THICKNESS = 0.2
+
 
 def opaque_to_shade(construction: OpaqueConstruction) -> ShadeConstruction:
-    """Convert an opaque construction to a shade construction."""
+    """Convert a Honeybee OpaqueConstruction to a Honeybee ShadeConstruction.
+
+    Args:
+        construction (OpaqueConstruction):
+            A Honeybee OpaqueConstruction.
+
+    Returns:
+        ShadeConstruction:
+            A Honeybee ShadeConstruction.
+
+    Raises:
+        TypeError: If construction is not of type OpaqueConstruction.
+    """
+
+    if not isinstance(construction, OpaqueConstruction):
+        raise TypeError("construction must be of type OpaqueConstruction.")
+
     return ShadeConstruction(
-        identifier="{construction.identifier}_shade",
+        identifier=f"{construction.identifier}_shade",
         solar_reflectance=construction.outside_solar_reflectance,
         visible_reflectance=construction.outside_visible_reflectance,
         is_specular=False,
     )
 
 
-def equality(model0: Model, model1: Model, include_identifier: bool = False) -> bool:
-    """Check for equality between two models, with regards to their material
-        properties.
+def single_layer_construction(
+    material: _EnergyMaterialOpaqueBase,
+) -> OpaqueConstruction:
+    """Create a single layer Honeybee OpaqueConstruction from a Honeybee _EnergyMaterialOpaqueBase object.
 
     Args:
-        model0 (Model):
-            A honeybee model.
-        model1 (Model):
-            A honeybee model.
-        include_identifier (bool, optional):
-            Include the identifier (name) of the model in the quality check.
-                Defaults to False.
+        material (_EnergyMaterialOpaqueBase):
+            A Honeybee _EnergyMaterialOpaqueBase object.
 
     Returns:
-        bool:
-            True if models are equal.
-    """
+        OpaqueConstruction:
+            A single layer Honeybee OpaqueConstruction.
 
-    if not isinstance(model0, Model) or not isinstance(model1, Model):
-        raise TypeError("Both inputs must be of type Model.")
-
-    if include_identifier:
-        if model0.identifier != model1.identifier:
-            return False
-
-    # Check ground material properties
-    gnd0_material = model0.faces[5].properties.energy.construction.materials[0]
-    gnd1_material = model1.faces[5].properties.energy.construction.materials[0]
-    gnd_materials_match: bool = str(gnd0_material) == str(gnd1_material)
+    Raises:
+        TypeError: If material is not of type _EnergyMaterialOpaqueBase.
+    """
 
-    # Check shade material properties
-    shd0_material = model0.faces[-6].properties.energy.construction.materials[0]
-    shd1_material = model1.faces[-6].properties.energy.construction.materials[0]
-    shd_materials_match: bool = str(shd0_material) == str(shd1_material)
+    if not isinstance(material, _EnergyMaterialOpaqueBase):
+        raise TypeError("material must be of type _EnergyMaterialOpaqueBase.")
 
-    return gnd_materials_match and shd_materials_match
+    return OpaqueConstruction(material.identifier, [material])
 
 
-def _create_ground_zone(
+def _ground_zone(
     construction: OpaqueConstruction,
     shaded: bool = False,
-    width: float = 10,
-    depth: float = 10,
-    thickness: float = 1,
+    width: float = _ZONE_WIDTH,
+    depth: float = _ZONE_DEPTH,
+    thickness: float = _GROUND_THICKNESS,
 ) -> Room:
     """Create a ground zone with boundary conditions and face identifiers named
          per external comfort workflow.
@@ -75,11 +88,11 @@ def _create_ground_zone(
         shaded (bool, optional):
             A flag to describe whether this zone is shaded. Defaults to False.
         width (float, optional):
-            The width (x-dimension) of the ground zone. Defaults to 10m.
+            The width (x-dimension) of the ground zone.
         depth (float, optional):
-            The depth (y-dimension) of the ground zone. Defaults to 10m.
+            The depth (y-dimension) of the ground zone.
         thickness (float, optional):
-            The thickness (z-dimension) of the ground zone. Defaults to 1m.
+            The thickness (z-dimension) of the ground zone.
 
     Returns:
         Room:
@@ -97,7 +110,7 @@ def _create_ground_zone(
     )
 
     # apply ground construction
-    ground_zone.properties.energy.make_ground(construction)
+    ground_zone.properties.energy.make_ground(construction)  # pylint: disable=no-member
 
     for face in ground_zone.faces:
         face: Face
@@ -116,12 +129,12 @@ def _create_ground_zone(
     return ground_zone
 
 
-def _create_shade_zone(
+def _shade_zone(
     construction: OpaqueConstruction,
-    width: float = 10,
-    depth: float = 10,
-    shade_height: float = 3,
-    shade_thickness: float = 0.2,
+    width: float = _ZONE_WIDTH,
+    depth: float = _ZONE_DEPTH,
+    shade_height: float = _SHADE_HEIGHT_ABOVE_GROUND,
+    shade_thickness: float = _SHADE_THICKNESS,
 ) -> Room:
     """Create a shade zone with boundary conditions and face identifiers named
         per external comfort workflow.
@@ -130,13 +143,13 @@ def _create_shade_zone(
         construction (OpaqueConstruction):
             A construction for the shade zones faces.
         width (float, optional):
-            The width (x-dimension) of the shaded zone. Defaults to 10m.
+            The width (x-dimension) of the shaded zone.
         depth (float, optional):
-            The depth (y-dimension) of the shaded zone. Defaults to 10m.
+            The depth (y-dimension) of the shaded zone.
         shade_height (float, optional):
-            The height of the shade. Default is 3m.
+            The height of the shade.
         shade_thickness (float, optional):
-            The thickness of the shade. Default is 0.2m.
+            The thickness of the shade.
 
     Returns:
         Room:
@@ -151,9 +164,11 @@ def _create_shade_zone(
         origin=Point3D(-width / 2, -depth / 2, shade_height),
     )
     # convert zone into a plenum
+    # pylint: disable=no-member
     shade_zone.properties.energy.make_plenum(False, True, False)
     # apply high infiltration rate
     shade_zone.properties.energy.absolute_infiltration_ach(30)
+    # pylint: enable=no-member
 
     for face in shade_zone.faces:
         face: Face
@@ -187,12 +202,12 @@ def _create_shade_zone(
     return shade_zone
 
 
-def _create_shade_valence(
+def _shade_valence(
     construction: OpaqueConstruction,
-    width: float = 10,
-    depth: float = 10,
-    shade_height: float = 3,
-) -> List[Shade]:
+    width: float = _ZONE_WIDTH,
+    depth: float = _ZONE_DEPTH,
+    shade_height: float = _SHADE_HEIGHT_ABOVE_GROUND,
+) -> list[Shade]:
     """Create a massless shade around the location being assessed for a shaded
         external comfort condition.
 
@@ -200,11 +215,11 @@ def _create_shade_valence(
         construction (OpaqueConstruction):
             A construction for the shade faces.
         width (float, optional):
-            The width (x-dimension) of the shaded zone. Defaults to 10m.
+            The width (x-dimension) of the shaded zone.
         depth (float, optional):
-            The depth (y-dimension) of the shaded zone. Defaults to 10m.
+            The depth (y-dimension) of the shaded zone.
         shade_height (float, optional):
-            The height of the shade. Default is 3m.
+            The height of the shade.
 
     Returns:
         List[Shade]:
@@ -256,58 +271,53 @@ def _create_shade_valence(
         ),
     ]
     for shade in shades:
+        # pylint: disable=no-member
         shade.properties.energy.construction = shade_construction
+        # pylint: enable=no-member
 
     return shades
 
 
-def single_layer_construction(
-    material: _EnergyMaterialOpaqueBase,
-) -> OpaqueConstruction:
-    """Create a single layer construction from a material."""
-    return OpaqueConstruction(material.identifier, [material])
-
-
 def create_model(
+    identifier: str,
     ground_material: _EnergyMaterialOpaqueBase,
     shade_material: _EnergyMaterialOpaqueBase,
-    identifier: str = None,
 ) -> Model:
     """
     Create a model containing geometry describing a shaded and unshaded
         external comfort scenario, including sensor grids for simulation.
 
     Args:
+        identifier (str):
+            A unique identifier for the model.
         ground_material (_EnergyMaterialOpaqueBase):
             A surface material for the ground zones topmost face.
         shade_material (_EnergyMaterialOpaqueBase):
             A surface material for the shade zones faces.
-        identifier (str, optional):
-            A unique identifier for the model. Defaults to None which will
-            generate a unique identifier. This is useful for testing purposes!
 
     Returns:
         Model:
             A model containing geometry describing a shaded and unshaded
             external comfort scenario, including sensor grids for simulation.
     """
+
     displacement_vector = Vector3D(y=200)
 
-    # convert materials to single-leayer constructions
+    # convert materials to single-layer constructions
     ground_construction = single_layer_construction(ground_material)
     shade_construction = single_layer_construction(shade_material)
 
     # unshaded case
-    ground_zone_unshaded = _create_ground_zone(ground_construction, shaded=False)
+    ground_zone_unshaded = _ground_zone(ground_construction, shaded=False)
 
     # shaded case
-    ground_zone_shaded = _create_ground_zone(ground_construction, shaded=True)
+    ground_zone_shaded = _ground_zone(ground_construction, shaded=True)
     ground_zone_shaded.move(displacement_vector)
 
-    shade_zone = _create_shade_zone(shade_construction)
+    shade_zone = _shade_zone(shade_construction)
     shade_zone.move(displacement_vector)
 
-    shades = _create_shade_valence(shade_construction)
+    shades = _shade_valence(shade_construction)
     for shade in shades:
         shade.move(displacement_vector)
 
@@ -345,9 +355,6 @@ def create_model(
         ),
     ]
 
-    if identifier is None:
-        identifier = str(uuid.uuid4())
-
     model = Model(
         identifier=identifier,
         rooms=[ground_zone_unshaded, ground_zone_shaded, shade_zone],
@@ -358,3 +365,97 @@ def create_model(
     model.properties.radiance.sensor_grids = sensor_grids  # pylint: disable=no-member
 
     return model
+
+
+def get_ground_material(model: Model) -> _EnergyMaterialOpaqueBase:
+    """Get the ground material from a model.
+
+    Args:
+        model (Model):
+            A honeybee model.
+
+    Returns:
+        _EnergyMaterialOpaqueBase:
+            The ground material.
+    """
+
+    if not isinstance(model, Model):
+        raise TypeError("model must be of type Model.")
+
+    return model.faces_by_identifier(["GROUND_ZONE_UP_UNSHADED"])[
+        0
+    ].properties.energy.construction.materials[0]
+
+
+def get_shade_material(model: Model) -> _EnergyMaterialOpaqueBase:
+    """Get the shade material from a model.
+
+    Args:
+        model (Model):
+            A honeybee model.
+
+    Returns:
+        _EnergyMaterialOpaqueBase:
+            The shade material.
+    """
+
+    if not isinstance(model, Model):
+        raise TypeError("model must be of type Model.")
+
+    return model.faces_by_identifier(["SHADE_ZONE_DOWN"])[
+        0
+    ].properties.energy.construction.materials[0]
+
+
+def get_ground_reflectance(model: Model) -> float:
+    """Get the ground/floor reflectance from a model.
+
+    Args:
+        model (Model):
+            A honeybee model.
+
+    Returns:
+        float:
+            The ground/floor reflectance.
+    """
+
+    if not isinstance(model, Model):
+        raise TypeError("model must be of type Model.")
+
+    _material: EnergyMaterial | EnergyMaterialVegetation = get_ground_material(model)
+    return _material.solar_reflectance
+
+
+def model_equality(model0: Model, model1: Model) -> bool:
+    """Check for equality between two models, with regards to their material
+        properties.
+
+    Args:
+        model0 (Model):
+            A honeybee model.
+        model1 (Model):
+            A honeybee model.
+
+    Returns:
+        bool:
+            True if models are equal.
+    """
+
+    if not isinstance(model0, Model) or not isinstance(model1, Model):
+        raise TypeError("Both inputs must be of type Model.")
+
+    # Check identifiers match
+    if model0.identifier != model1.identifier:
+        return False
+
+    # Check ground materials match
+    gnd_materials_match = _material_equality(
+        *[get_ground_material(model) for model in [model0, model1]]
+    )
+
+    # Check shade materials match
+    shd_materials_match = _material_equality(
+        *[get_shade_material(model) for model in [model0, model1]]
+    )
+
+    return gnd_materials_match and shd_materials_match
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/shelter.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/shelter.py
index f16d5365..498290ec 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/shelter.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/shelter.py
@@ -1,725 +1,88 @@
-from __future__ import annotations
-
+"""Module for creating shelter objects."""
+# pylint: disable=E0401
 import json
-from dataclasses import dataclass, field
+import warnings
 from enum import Enum
-from types import FunctionType
-from typing import Any, Dict, List
-
-import matplotlib.pyplot as plt
-import numpy as np
-from honeybee.face import Face
-from ladybug.epw import EPW
-from ladybug.sunpath import Sun
-from ladybug.viewsphere import ViewSphere
-from ladybug_geometry.geometry3d import Face3D, Point3D, Ray3D, Vector3D
-from mpl_toolkits import mplot3d
-
-from ..bhomutil.bhom_object import BHoMObject
-from ..bhomutil.encoder import BHoMEncoder
-from ..helpers import wind_speed_at_height
-from ..ladybug_extension.epw import sun_position_list, unique_wind_speed_direction
-
-_LINEAR_SHELTER_VERTICES_NORTH_SOUTH = [
-    Point3D(2, -1000, 3.5),
-    Point3D(2, 1000, 3.5),
-    Point3D(-2, 1000, 3.5),
-    Point3D(-2, -1000, 3.5),
-]
-_OVERHEAD_SHELTER_VERTICES_SMALL = [
-    Point3D(2, 0, 3.5),
-    Point3D(1.879385, 0.68404, 3.5),
-    Point3D(1.532089, 1.285575, 3.5),
-    Point3D(1, 1.732051, 3.5),
-    Point3D(0.347296, 1.969616, 3.5),
-    Point3D(-0.347296, 1.969616, 3.5),
-    Point3D(-1.0, 1.732051, 3.5),
-    Point3D(-1.532089, 1.285575, 3.5),
-    Point3D(-1.879385, 0.68404, 3.5),
-    Point3D(-2, 0, 3.5),
-    Point3D(-1.879385, -0.68404, 3.5),
-    Point3D(-1.532089, -1.285575, 3.5),
-    Point3D(-1, -1.732051, 3.5),
-    Point3D(-0.347296, -1.969616, 3.5),
-    Point3D(0.347296, -1.969616, 3.5),
-    Point3D(1.0, -1.732051, 3.5),
-    Point3D(1.532089, -1.285575, 3.5),
-    Point3D(1.879385, -0.68404, 3.5),
-]
-_OVERHEAD_SHELTER_VERTICES_LARGE = [
-    Point3D(250, 0, 3.5),
-    Point3D(234.923155, 85.505036, 3.5),
-    Point3D(191.511111, 160.696902, 3.5),
-    Point3D(125, 216.506351, 3.5),
-    Point3D(43.412044, 246.201938, 3.5),
-    Point3D(-43.412044, 246.201938, 3.5),
-    Point3D(-125.0, 216.506351, 3.5),
-    Point3D(-191.511111, 160.696902, 3.5),
-    Point3D(-234.923155, 85.505036, 3.5),
-    Point3D(-250, 0, 3.5),
-    Point3D(-234.923155, -85.505036, 3.5),
-    Point3D(-191.511111, -160.696902, 3.5),
-    Point3D(-125, -216.506351, 3.5),
-    Point3D(-43.412044, -246.201938, 3.5),
-    Point3D(43.412044, -246.201938, 3.5),
-    Point3D(125.0, -216.506351, 3.5),
-    Point3D(191.511111, -160.696902, 3.5),
-    Point3D(234.923155, -85.505036, 3.5),
-]
-_DIRECTIONAL_SHELTER_VERTICES_NORTH = [
-    Point3D(1, 1, 0),
-    Point3D(-1, 1, 0),
-    Point3D(-1, 1, 5),
-    Point3D(1, 1, 5),
-]
-_CANOPY_NORTH = [
-    Point3D(1, 1, 5),
-    Point3D(-1, 1, 5),
-    Point3D(-1, -1, 5),
-    Point3D(1, -1, 5),
-]
-
-
-@dataclass(init=True, repr=True, eq=True)
-class Shelter(BHoMObject):
-
-    """A Shelter object, used to determine exposure to sun, sky and wind.
-
-    Args:
-        vertices (List[List[float]]):
-            A list of planar [x, y, z] coordinates representing the vertices of the
-            object providing shelter..
-        wind_porosity (float, optional):
-            The transmissivity of the shelter to wind. Defaults to 0.
-        radiation_porosity (float, optional):
-            The transmissivity of the shelter to radiation (from surfaces, sky
-            and sun). Defaults to 0.
-
-    Returns:
-        Shelter: A Shelter object.
-    """
-
-    Vertices: List[Point3D] = field(
-        init=True, repr=False
-    )  # a list of xyz coordinates representing the vertices of the sheltering object
-    WindPorosity: float = field(init=True, repr=True, compare=True, default=0)
-    RadiationPorosity: float = field(init=True, repr=True, compare=True, default=0)
-
-    _t: str = field(
-        init=False, repr=False, compare=True, default="BH.oM.LadybugTools.Shelter"
-    )
-
-    def __post_init__(self) -> None:
-        # handle nulls
-        if self.Vertices is None:
-            raise ValueError("vertices cannot be None")
-        if self.WindPorosity is None:
-            self.WindPorosity = 0
-        if self.RadiationPorosity is None:
-            self.RadiationPorosity = 0
-
-        if (not 0 <= self.WindPorosity <= 1) or (not 0 <= self.RadiationPorosity <= 1):
-            raise ValueError("porosity values must be between 0 and 1")
-
-        if all([self.WindPorosity == 1, self.RadiationPorosity == 1]):
-            raise ValueError(
-                "This shelter would have no effect as it does not impact wind or radiation exposure."
-            )
-
-        # create face for shelter
-        self.face = Face3D(self.Vertices)
-        self.face.check_planar(0.001)
-
-        # create origin pt - representing an analytical person-point
-        self.origin = Point3D(0, 0, 1.2)
-
-        # wrap methods within this class
-        super().__post_init__()
-
-    @classmethod
-    def from_dict(cls, dictionary: Dict[str, Any]) -> Shelter:
-        """Create this object from a dictionary."""
-
-        if all(isinstance(i, Point3D) for i in dictionary["Vertices"]):
-            # from a standard dictionary, no special treatment required
-
-            return cls(
-                WindPorosity=dictionary["WindPorosity"],
-                RadiationPorosity=dictionary["RadiationPorosity"],
-                Vertices=dictionary["Vertices"],
-            )
-
-        vertices = [Point3D(i["X"], i["Y"], i["Z"]) for i in dictionary["Vertices"]]
-
-        return cls(
-            WindPorosity=dictionary["WindPorosity"],
-            RadiationPorosity=dictionary["RadiationPorosity"],
-            Vertices=vertices,
-        )
-
-    @classmethod
-    def from_json(cls, json_string: str) -> Shelter:
-        """Create this object from a JSON string."""
-
-        dictionary = json.loads(json_string)
-
-        return cls.from_dict(dictionary)
-
-    @classmethod
-    def from_lb_face3d(
-        cls, face: Face3D, wind_porosity: float = 0, radiation_porosity: float = 0
-    ) -> Shelter:
-        """Create a shelter object from a Ladybug Face3D object."""
-        return cls(
-            vertices=face.vertices,
-            wind_porosity=wind_porosity,
-            radiation_porosity=radiation_porosity,
-        )
-
-    @classmethod
-    def from_hb_face(cls, face: Face) -> Shelter:
-        """Create a shelter object from a Honeybee Face object."""
-        vertices = face.vertices
-        wind_porosity = 0
-        radiation_porosity = (
-            0
-            if face.properties.radiance.modifier.is_opaque
-            else face.properties.radiance.modifier.average_transmittance
-        )
-        return cls(
-            Vertices=vertices,
-            WindPorosity=wind_porosity,
-            RadiationPorosity=radiation_porosity,
-        )
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Return this object as it's dictionary equivalent."""
-        dictionary = {}
-        for k, v in self.__dict__.items():
-            if isinstance(getattr(self, k), FunctionType):
-                continue
-            dictionary[k] = v
-        dictionary["_t"] = self._t
-        return dictionary
+from pathlib import Path
 
-    @property
-    def vertices(self) -> List[Point3D]:
-        """A handy accessor using proper Python naming conventions."""
-        return self.Vertices
+# pylint: enable=E0401
 
-    @property
-    def wind_porosity(self) -> float:
-        """A handy accessor using proper Python naming conventions."""
-        return self.WindPorosity
-
-    @property
-    def radiation_porosity(self) -> float:
-        """A handy accessor using proper Python naming conventions."""
-        return self.RadiationPorosity
-
-    def to_json(self) -> str:
-        """Return this object as it's JSON string equivalent."""
-        return json.dumps(self.to_dict(), cls=BHoMEncoder)
-
-    def rotate(self, angle: float, center: Point3D = Point3D()) -> Shelter:
-        """Rotate the shelter about 0, 0, 0 by the given angle in degrees clockwise from north at 0.
-
-        Args:
-            angle (float):
-                An angle in degrees.
-            center (Point3D, optional):
-                A Point3D representing the center of rotation. Defaults to 0, 0, 0.
-
-        Returns:
-            Shelter:
-                The rotated shelter object.
-        """
-        angle = np.deg2rad(-angle)
-        rotated_vertices = self.face.rotate_xy(angle, center).vertices
-        return Shelter(rotated_vertices, self.WindPorosity, self.RadiationPorosity)
-
-    def move(self, vector: Vector3D) -> Shelter:
-        """Move the shelter by the given vector.
+import numpy as np
+import pandas as pd
+from ._shelterbase import Shelter
+
+
+class TreeShelter(Enum):
+    """_"""
+
+    ACER_PLATANOIDES = "Norway maple"
+    AESCULUS_HIPPOCASTANUM = "European horse-chestnut"
+    BETULA_PENDULA = "Silver birch"
+    CARPINUS_BETULUS = "European hornbeam"
+    FAGUS_SYLVATICA = "European beech"
+    FRAXINUS_AMERICANA = "White ash"
+    MAGNOLIA_KOBUS = "Kobushi magnolia"
+    MALUS_FLORIBUNDA = "Japanese flowering crabapple"
+    PHOENIX_DACTYLIFERA = "Date palm"
+    PRUNUS_CERASIFERA = "Cherry plum"
+    PRUNUS_SERRULATA = "East Asian Cherry"
+    QUERCUS_PALUSTRIS = "Spanish oak"
+    TILIA_X_EUCHLORA = "Caucasian lime"
+
+    def shelter(self, northern_hemisphere: bool = True) -> Shelter:
+        """Get a shelter object for this tree species.
 
         Args:
-            vector (Vector3D):
-                A Vector3D representing the movement.
+            northern_hemisphere (bool, optional):
+                If True, then the tree will have leafcover over the northern
+                hemisphere summer months. If False then this will be shifted by
+                6-months to approximate the change for the southern hemisphere.
+                Defaults to True.
 
         Returns:
             Shelter:
-                The moved shelter object.
-        """
-        moved_vertices = self.face.move(vector).vertices
-        return Shelter(moved_vertices, self.WindPorosity, self.RadiationPorosity)
-
-    def sky_exposure(self, include_radiation_porosity: bool = True) -> float:
-        """Determine the proportion of sky the analytical point is exposed to. Also account for radiation_porosity in that exposure.
-
-        Args:
-            include_radiation_porosity (bool, optional):
-                If True, then increase exposure according to shelter porosity. Defaults to True.
-
-        Returns:
-            float:
-                A value between 0 and 1 denoting the proportion of sky exposure.
-        """
-        view_sphere = ViewSphere()
-        rays = [
-            Ray3D(self.origin, vector) for vector in view_sphere.reinhart_dome_vectors
-        ]
-        n_intersections = sum(bool(self.face.intersect_line_ray(ray)) for ray in rays)
-        if include_radiation_porosity:
-            return 1 - ((n_intersections / len(rays)) * (1 - self.RadiationPorosity))
-        return 1 - (n_intersections / len(rays))
-
-    def sun_exposure(self, sun: Sun, include_radiation_porosity: bool = True) -> float:
-        """Determine the proportion of sun the analytical point is exposed to. Also account for radiation_porosity in that exposure.
-
-        Args:
-            sun (Sun):
-                A LB Sun object.
-            include_radiation_porosity (bool, optional):
-                If True, then increase exposure according to shelter porosity. Defaults to True.
-
-        Returns:
-            float:
-                A value between 0 and 1 denoting the proportion of sun exposure.
-        """
-        if sun.altitude < 0:
-            return 0
-        ray = Ray3D(self.origin, (sun.position_3d() - self.origin).normalize())
-        if self.face.intersect_line_ray(ray) is None:
-            return 1
-        return self.RadiationPorosity if include_radiation_porosity else 0
-
-    def annual_sun_exposure(
-        self, epw: EPW, include_radiation_porosity: bool = True
-    ) -> List[float]:
-        """Calculate annual hourly sun exposure. Overnight hours where sun is below horizon default to 0 sun visibility.
-
-        Args:
-            epw (EPW):
-                A Ladybug EPW object.
-            include_radiation_porosity (bool, optional):
-                If True, then increase exposure according to shelter porosity. Defaults to True.
-
-        Returns:
-            List[float]:
-                A list of annual hourly values denoting sun exposure values.
-        """
-        suns = sun_position_list(epw)
-        return [self.sun_exposure(sun, include_radiation_porosity) for sun in suns]
-
-    def effective_wind_speed(
-        self,
-        wind_direction: float,
-        wind_speed: float,
-        edge_acceleration_factor: float = 1.1,
-        obstruction_band_width: float = 5,
-    ) -> float:
-        """Determine the effective wind speed from a given direction based on shelter obstruction and edge acceleration effects.
-
-        Wind is considered "obstructured" in some way if its direction +/-5 degrees of the shelter.
-        When fully obstructed, then the porosity of the shelter is used to reduce wind speed.
-        Where partially obstructed, the edge effects are expected based on the level of porosity
-        of the shelter.
-        Where not obstructed, then wind is kept per input.
-
-        Args:
-            wind_direction (float):
-                A single wind direction between 0-360.
-            wind_speed (float):
-                A wind speed in m/s.
-            edge_acceleration_factor (float, optional):
-                The proportional increase in wind speed due to edge acceleration around a shelter edge. Defaults to 1.1.
-            obstruction_band_width (float, optional):
-                The azimuthal range over which obstruction is checked. Defaults to 5.
-
-        Returns:
-            float:
-                A resultant wind speed subject to obstruction from the shelter.
+                A Shelter object.
         """
 
-        if wind_speed == 0:
-            return 0
-
-        if self.WindPorosity == 1:
-            return wind_speed
-
-        # create components for sample vectors
-        left_rad = np.deg2rad((wind_direction - obstruction_band_width) % 360)
-        left_x = np.sin(left_rad)
-        left_y = np.cos(left_rad)
-        center_rad = np.deg2rad(wind_direction)
-        center_x = np.sin(center_rad)
-        center_y = np.cos(center_rad)
-        right_rad = np.deg2rad((wind_direction + obstruction_band_width) % 360)
-        right_x = np.sin(right_rad)
-        right_y = np.cos(right_rad)
+        # load json file
+        with open(
+            Path(__file__).parent.parent.parent / "data" / r"vegetation.json", "r"
+        ) as fp:
+            tree_config = json.load(fp)[self.name]
 
-        # create sample vectors
-        sample_vectors = []
-        for _z in np.linspace(0, 1, 5):
-            sample_vectors.extend(
+        idx = pd.date_range("2017-01-01", periods=8760, freq="60T")
+        if tree_config["deciduous"]:
+            vals = np.stack(
                 [
-                    Vector3D(left_x, left_y, _z),
-                    Vector3D(center_x, center_y, _z),
-                    Vector3D(right_x, right_y, _z),
-                ]
-            )
-
-        # check intersections
-        rays = [Ray3D(self.origin, vector) for vector in sample_vectors]
-        n_intersections = sum(bool(self.face.intersect_line_ray(ray)) for ray in rays)
-
-        # return effective wind speed
-        if n_intersections == len(rays):
-            # fully obstructed
-            return wind_speed * self.WindPorosity
-        if n_intersections == 0:
-            # fully unobstructed
-            return wind_speed
-        # get the propostion of obstruction adn adjust acceleration based on that proportion
-        proportion_obstructed = n_intersections / len(rays)
-        return (
-            (wind_speed * self.WindPorosity)
-            * proportion_obstructed  # obstructed wind component
-        ) + (
-            wind_speed
-            * edge_acceleration_factor
-            * (1 - proportion_obstructed)  # edge wind component
-        )
-
-    def annual_effective_wind_speed(
-        self,
-        epw: EPW,
-        edge_acceleration_factor: float = 1.1,
-        obstruction_band_width: float = 5,
-        height_above_ground: float = 10,
-        reference_height: float = 10,
-        terrain_roughness_length: float = 0.03,
-    ) -> List[float]:
-        """Determine the effective wind speed from a given direction based on shelter obstruction and edge acceleration effects.
-
-        Wind is considered "obstructured" in some way if its direction +/-5 degrees of the shelter.
-        When fully obstructed, then the porosity of the shelter is used to reduce wind speed.
-        Where partially obstructed, the edge effects are expected based on the level of porosity
-        of the shelter.
-        Where not obstructed, then wind is kept per input.
-
-        Args:
-            epw (EPW):
-                A Ladybug EPW object.
-            edge_acceleration_factor (float, optional):
-                The proportional increase in wind speed due to edge acceleration around a shelter edge. Defaults to 1.1.
-            obstruction_band_width (float, optional):
-                The azimuthal range over which obstruction is checked. Defaults to 5.
-            height_above_ground (float, optional):
-                The height above ground in meters at which the wind speed is measured. Defaults to 10 which is typical for an EPW.
-            reference_height (float, optional):
-                The height above ground in meters at which the reference wind speed is measured. Defaults to 10 which is typical for an EPW.
-            terrain_roughness_length (float, optional):
-                The terrain roughness length in meters. Defaults to 0.03 which is typical open flat terrain with a few isolated obstacles.
-
-        Returns:
-            List[float]:
-                A resultant list of EPW aligned wind speeds subject to obstruction from the shelter.
-        """
-        _ws_wd = unique_wind_speed_direction(epw)
-
-        # adjust for wind speed at height
-        ws_wd = []
-        for ws, wd in _ws_wd:
-            ws_wd.append(
-                (
-                    wind_speed_at_height(
-                        reference_value=ws,
-                        reference_height=reference_height,
-                        target_height=height_above_ground,
-                        terrain_roughness_length=terrain_roughness_length,
-                        log_function=True,
+                    np.where(
+                        (idx < pd.to_datetime(tree_config["regrowth_period"][0]))
+                        | (idx > pd.to_datetime(tree_config["drop_period"][1])),
+                        tree_config["porosity_bare"],
+                        np.nan,
                     ),
-                    wd,
-                )
-            )
-
-        ws_effective = {}
-        for ws, wd in ws_wd:
-            ws_effective[(ws, wd)] = self.effective_wind_speed(
-                wd, ws, edge_acceleration_factor, obstruction_band_width
-            )
-        # cast back to original epw
-        effective_wind_speeds = []
-        for ws, wd in list(zip(*[epw.wind_speed.values, epw.wind_direction.values])):
-            effective_wind_speeds.append(ws_effective[(ws, wd)])
-        return effective_wind_speeds
-
-    def set_porosity(self, porosity: float) -> Shelter:
-        """Return this shelter with an adjusted porosity value applied to both wind and radiation components."""
-        return Shelter(self.Vertices, porosity, porosity)
-
-    def visualise(self) -> plt.Figure:
-        """Visualise this shelter to check validity and that it exists where you think it should!"""
-
-        fig = plt.figure(figsize=(5, 5))
-        ax = mplot3d.Axes3D(fig, auto_add_to_figure=False)
-        fig.add_axes(ax)
-        ax.scatter(*self.origin.to_array())
-        # add shelter as a polygon
-        vtx = np.array([i.to_array() for i in self.face.vertices])
-        tri = mplot3d.art3d.Poly3DCollection([vtx])
-        tri.set_color("grey")
-        tri.set_alpha(0.5)
-        tri.set_edgecolor("k")
-        ax.add_collection3d(tri)
-        # format axes
-        ax.set_xlabel("x")
-        ax.set_ylabel("y")
-        ax.set_zlabel("z")
-        # set lims
-        ax.set_xlim(min(i[0] for i in vtx), max(i[0] for i in vtx))
-        ax.set_ylim(min(i[1] for i in vtx), max(i[1] for i in vtx))
-        # pylint: disable=no-member
-        ax.set_zlim(min(i[2] for i in vtx), max(i[2] for i in vtx))
-        # pylint: enable=no-member
-        return fig
-
-
-def sky_exposure(
-    shelters: List[Shelter], include_radiation_porosity: bool = True
-) -> float:
-    """Determine the proportion of sky the analytical point is exposed to under a combination of shelters. Also account for radiation_porosity in that exposure.
-
-    Args:
-        shelters (List[Shelter]):
-            A list of shelter objects.
-        include_radiation_porosity (bool, optional):
-            If True, then increase exposure according to shelter porosity. Defaults to True.
-
-    Returns:
-        float:
-            A value between 0 and 1 denoting the proportion of sky exposure.
-    """
-    if len(shelters) == 0:
-        return 1
-
-    view_sphere = ViewSphere()
-    rays = [
-        Ray3D(shelters[0].origin, vector)
-        for vector in view_sphere.reinhart_dome_vectors
-    ]
-
-    # get intersections for each patch, for each shelter
-    intersections = []
-    for shelter in shelters:
-        intersections.append(
-            [
-                1
-                if shelter.face.intersect_line_ray(ray) is None
-                else (shelter.RadiationPorosity if include_radiation_porosity else 0)
-                for ray in rays
-            ]
-        )
-    return np.array(intersections).prod(axis=0).sum() / len(rays)
-
-
-def sun_exposure(
-    shelters: List[Shelter], sun: Sun, include_radiation_porosity: bool = True
-) -> float:
-    """Determine the proportion of sun the analytical point is exposed to under a combination of shelters. Also account for radiation_porosity in that exposure.
-
-    Args:
-        shelters (List[Shelter]):
-            A list of shelter objects.
-        sun (Sun):
-            A LB Sun object.
-        include_radiation_porosity (bool, optional):
-            If True, then increase exposure according to shelter porosity. Defaults to True.
-
-    Returns:
-        float:
-            A value between 0 and 1 denoting the overall proportion of sky exposure.
-    """
-    if len(shelters) == 0:
-        return 1 if sun.altitude > 0 else 0
-
-    sun_exposures = []
-    for shelter in shelters:
-        sun_exposures.append(shelter.sun_exposure(sun, include_radiation_porosity))
-    return np.prod(sun_exposures)
-
-
-def annual_sun_exposure(
-    shelters: List[Shelter], epw: EPW, include_radiation_porosity: bool = True
-) -> List[float]:
-    """Calculate annual hourly sun exposure under a set of shelters. Where sun is below horizon default to 0 sun visibility.
-
-    Args:
-        shelters (List[Shelter]):
-            A list of shelter objects.
-        epw (EPW):
-            A Ladybug EPW object.
-        include_radiation_porosity (bool, optional):
-            If True, then increase exposure according to shelter porosity. Defaults to True.
-
-    Returns:
-        List[float]:
-            A list of annual hourly values denoting sun exposure values.
-    """
-
-    suns = sun_position_list(epw)
-    if len(shelters) == 0:
-        return [1 if sun.altitude > 0 else 0 for sun in suns]
-
-    sun_exposures = []
-    for shelter in shelters:
-        sun_exposures.append(
-            [shelter.sun_exposure(sun, include_radiation_porosity) for sun in suns]
-        )
-    return np.prod(sun_exposures, axis=0)
-
-
-def effective_wind_speed(
-    shelters: List[Shelter],
-    wind_speed: float,
-    wind_direction: float,
-    edge_acceleration_factor: float = 1.1,
-    obstruction_band_width: float = 5,
-) -> float:
-    """Determine the effective wind speed from a given direction based on multiple obstructing shelters and edge acceleration effects.
-
-    Wind is considered "obstructured" in some way if its direction +/-5 degrees of the shelter.
-    When fully obstructed, then the porosity of the shelter is used to reduce wind speed.
-    Where partially obstructed, the edge effects are expected based on the level of porosity
-    of the shelter.
-    Where not obstructed, then wind is kept per input.
-
-    Args:
-        shelters (List[Shelter]):
-            A list of shelter objects.
-        wind_direction (float):
-            A single wind direction between 0-360.
-        wind_speed (float):
-            A wind speed in m/s.
-        edge_acceleration_factor (float, optional):
-            The proportional increase in wind speed due to edge acceleration around a shelter edge. Defaults to 1.1.
-        obstruction_band_width (float, optional):
-            The azimuthal range over which obstruction is checked. Defaults to 5.
-
-    Returns:
-        float:
-            A resultant wind speed subject to obstruction from the shelter.
-    """
-
-    if len(shelters) == 0:
-        return wind_speed
-
-    effective_wind_speeds = []
-    for shelter in shelters:
-        effective_wind_speeds.append(
-            shelter.effective_wind_speed(
-                wind_direction,
-                wind_speed,
-                edge_acceleration_factor,
-                obstruction_band_width,
+                    np.where(
+                        (idx > pd.to_datetime(tree_config["regrowth_period"][1]))
+                        & (idx < pd.to_datetime(tree_config["drop_period"][0])),
+                        tree_config["porosity_leafed"],
+                        np.nan,
+                    ),
+                ]
             )
-        )
-    return np.min(effective_wind_speeds)
-
-
-def annual_effective_wind_speed(
-    shelters: List[Shelter],
-    epw: EPW,
-    edge_acceleration_factor: float = 1.1,
-    obstruction_band_width: float = 5,
-) -> List[float]:
-    """Determine the effective wind speed from a given direction based on multiple obstructing shelters and edge acceleration effects.
-
-    Wind is considered "obstructured" in some way if its direction +/-5 degrees of the shelter.
-    When fully obstructed, then the porosity of the shelter is used to reduce wind speed.
-    Where partially obstructed, the edge effects are expected based on the level of porosity
-    of the shelter.
-    Where not obstructed, then wind is kept per input.
-
-    Args:
-        shelters (List[Shelter]):
-            A list of shelter objects.
-        epw (EPW):
-            A Ladybug EPW object.
-        edge_acceleration_factor (float, optional):
-            The proportional increase in wind speed due to edge acceleration around a shelter edge. Defaults to 1.1.
-        obstruction_band_width (float, optional):
-            The azimuthal range over which obstruction is checked. Defaults to 5.
-
-    Returns:
-        List[float]:
-            A resultant list of EPW aligned wind speeds subject to obstruction from the shelter.
-    """
-    if len(shelters) == 0:
-        return epw.wind_speed
-
-    ws_wd = unique_wind_speed_direction(epw)
-    all_effective_wind_speeds = []
-    for shelter in shelters:
-        ws_effective = {}
-        for ws, wd in ws_wd:
-            ws_effective[(ws, wd)] = shelter.effective_wind_speed(
-                wd, ws, edge_acceleration_factor, obstruction_band_width
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", category=RuntimeWarning)
+                vals = np.nanmax(vals, axis=0)
+            _porosity = (
+                pd.Series(vals, index=idx)
+                .interpolate(method="polynomial", order=2)
+                .values
             )
-        # cast back to original epw
-        effective_wind_speeds = []
-        for ws, wd in list(zip(*[epw.wind_speed.values, epw.wind_direction.values])):
-            effective_wind_speeds.append(ws_effective[(ws, wd)])
-        all_effective_wind_speeds.append(effective_wind_speeds)
-    return np.min(all_effective_wind_speeds, axis=0)
-
-
-class Shelters(Enum):
-    """A list of pre-defined Shelter forms."""
-
-    NORTH_SOUTH_LINEAR = Shelter(
-        Vertices=_LINEAR_SHELTER_VERTICES_NORTH_SOUTH,
-    )
-    EAST_WEST_LINEAR = Shelter(
-        Vertices=_LINEAR_SHELTER_VERTICES_NORTH_SOUTH,
-    ).rotate(90)
-    NORTHEAST_SOUTHWEST_LINEAR = Shelter(
-        Vertices=_LINEAR_SHELTER_VERTICES_NORTH_SOUTH,
-    ).rotate(45)
-    NORTHWEST_SOUTHEAST_LINEAR = Shelter(
-        Vertices=_LINEAR_SHELTER_VERTICES_NORTH_SOUTH,
-    ).rotate(135)
+        else:
+            _porosity = np.ones(8760) * tree_config["porosity_leafed"]
 
-    OVERHEAD_SMALL = Shelter(
-        Vertices=_OVERHEAD_SHELTER_VERTICES_SMALL,
-    )
-    OVERHEAD_LARGE = Shelter(
-        Vertices=_OVERHEAD_SHELTER_VERTICES_LARGE,
-    )
-    CANOPY_N_E_S_W = Shelter(Vertices=_CANOPY_NORTH)
-    CANOPY_NE_SE_SW_NW = Shelter(Vertices=_CANOPY_NORTH).rotate(45)
+        if not northern_hemisphere:
+            _porosity = np.roll(_porosity, int(8760 / 2))
 
-    NORTH = Shelter(
-        Vertices=_DIRECTIONAL_SHELTER_VERTICES_NORTH,
-    )
-    NORTHEAST = Shelter(
-        Vertices=_DIRECTIONAL_SHELTER_VERTICES_NORTH,
-    ).rotate(45)
-    EAST = Shelter(
-        Vertices=_DIRECTIONAL_SHELTER_VERTICES_NORTH,
-    ).rotate(90)
-    SOUTHEAST = Shelter(
-        Vertices=_DIRECTIONAL_SHELTER_VERTICES_NORTH,
-    ).rotate(135)
-    SOUTH = Shelter(
-        Vertices=_DIRECTIONAL_SHELTER_VERTICES_NORTH,
-    ).rotate(180)
-    SOUTHWEST = Shelter(
-        Vertices=_DIRECTIONAL_SHELTER_VERTICES_NORTH,
-    ).rotate(225)
-    WEST = Shelter(
-        Vertices=_DIRECTIONAL_SHELTER_VERTICES_NORTH,
-    ).rotate(270)
-    NORTHWEST = Shelter(
-        Vertices=_DIRECTIONAL_SHELTER_VERTICES_NORTH,
-    ).rotate(315)
+        return Shelter.from_overhead_circle(
+            radius=tree_config["average_radius"],
+            height_above_ground=tree_config["average_height"],
+        ).set_porosity(_porosity.tolist())
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/simulate.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/simulate.py
index ac1279b6..2bf50aea 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/simulate.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/simulate.py
@@ -1,1197 +1,214 @@
-from __future__ import annotations
-
-import contextlib
-import getpass
-import io
+"""_"""
+# pylint: disable=E0401
 import itertools
-import json
-import shutil
 import warnings
-from dataclasses import dataclass, field
-from pathlib import Path
-from types import FunctionType
-from typing import Any, Dict, List, Tuple, Union
+
+# pylint: enable=E0401
 
 import numpy as np
 import pandas as pd
-from honeybee.config import folders as hb_folders
-from honeybee.model import Model
-from honeybee_energy.material.opaque import EnergyMaterial, EnergyMaterialVegetation
-from honeybee_energy.run import run_idf, run_osw, to_openstudio_osw
-from honeybee_energy.simulation.parameter import (
-    RunPeriod,
-    ShadowCalculation,
-    SimulationControl,
-    SimulationOutput,
-    SimulationParameter,
-)
-from ladybug.epw import EPW, AnalysisPeriod, HourlyContinuousCollection
-from ladybug.wea import Wea
-from ladybug_comfort.collection.solarcal import (
-    HorizontalRefSolarCal,
-    HorizontalSolarCal,
-    OutdoorSolarCal,
-)
-from ladybug_comfort.parameter.solarcal import SolarCalParameter
-from lbt_recipes.recipe import Recipe, RecipeSettings
-from matplotlib import pyplot as plt
 from tqdm import tqdm
 
-from ..bhomutil.analytics import CONSOLE_LOGGER
-from ..bhomutil.bhom_object import BHoMObject
-from ..bhomutil.encoder import (
-    BHoMEncoder,
-    fix_bhom_jsondict,
-    inf_dtype_to_inf_str,
-    inf_str_to_inf_dtype,
-)
-from ..helpers import evaporative_cooling_effect, sanitise_string
-from ..honeybee_extension.results import load_ill, load_res, load_sql, make_annual
-from ..ladybug_extension.analysis_period import describe_analysis_period
-from ..ladybug_extension.datacollection import (
-    average,
-    collection_from_series,
-    collection_to_series,
-)
-from ..ladybug_extension.epw import epw_to_dataframe
-from ..ladybug_extension.epw import equality as epw_eq
-from ..ladybug_extension.epw import get_filename
-from . import QUEENBEE_PATH
-from .ground_temperature import energyplus_strings
-from .material import OpaqueMaterial, OpaqueVegetationMaterial, material_from_dict
-from .model import create_model
-from .model import equality as model_eq
-from .utci.calculate import utci
-
-
-def working_directory(model: Model, create: bool = False) -> Path:
-    """Get the working directory (where simulation results will be stored) for the given model, and
-        create it if it doesn't already exist.
-
-    Args:
-        model (Model): A honeybee Model.
-        create (bool, optional): Set to True to create the directory. Default is False.
-
-    Returns:
-        Path: The simulation directory associated with the given model.
-    """
-
-    hb_folders.default_simulation_folder = f"C:/Users/{getpass.getuser()}/simulation"
-
-    working_dir: Path = Path(hb_folders.default_simulation_folder) / model.identifier
-    if create:
-        working_dir.mkdir(parents=True, exist_ok=True)
-
-    return working_dir
-
-
-def simulation_id(
-    epw_file: Path,
-    ground_material: Union[EnergyMaterial, EnergyMaterialVegetation],
-    shade_material: Union[EnergyMaterial, EnergyMaterialVegetation],
-) -> str:
-    """Create an ID for a simulation.
-
-    Args:
-        epw_file (Path):
-            The path to an EPW file.
-        ground_material (Union[OpaqueMaterial, OpaqueVegetationMaterial]):
-            A material object.
-        shade_material (Union[OpaqueMaterial, OpaqueVegetationMaterial]):
-            A material object.
-
-    Returns:
-        str:
-            An ID for the siulation run using this combination of inputs.
-    """
-
-    epw_id = sanitise_string(Path(epw_file).stem)
-    ground_material_id = sanitise_string(ground_material.identifier)
-    shade_material_id = sanitise_string(shade_material.identifier)
-    id_string = f"{epw_id}__{ground_material_id}__{shade_material_id}"
-    if len(id_string) > 100:
-        warnings.warn(
-            "simulation ID would be longer than 100 characters. In order for this to work it needs to be shortened. As such it might make things break if we try to reload this configuration in the future!"
-        )
-        id_string = id_string[:100]
-    return id_string
-
-
-def surface_temperature(
-    model: Model, epw: EPW
-) -> Dict[str, HourlyContinuousCollection]:
-    """Run EnergyPlus on a model and return the results.
-
-    Args:
-        model (Model): A honeybee Model to be run through EnergyPlus.
-        epw (EPW): An EPW object to be used for the simulation.
-
-    Returns:
-        Dict[str, LBTHourlyContinuousCollection]: A dictionary containing
-            surface temperature-related collections.
-    """
-
-    wd = working_directory(model, True)
-
-    sql_path = wd / "run" / "eplusout.sql"
-
-    if surface_temperature_results_exist(model, epw):
-        CONSOLE_LOGGER.info(f"[{model.identifier}] - Loading surface temperature")
-        return surface_temperature_results_load(sql_path, epw)
-
-    epw.save((wd / get_filename(epw, True)).as_posix())
-
-    CONSOLE_LOGGER.info(f"[{model.identifier}] - Simulating surface temperature")
-
-    # Write model JSON
-    model_dict = model.to_dict(triangulate_sub_faces=True)
-    model_json = wd / f"{model.identifier}.hbjson"
-    with open(model_json, "w", encoding="utf-8") as fp:
-        json.dump(model_dict, fp)
-
-    # Write simulation parameter JSON
-    sim_output = SimulationOutput(
-        outputs=["Surface Outside Face Temperature"],
-        include_sqlite=True,
-        summary_reports=None,
-        include_html=False,
-    )
-
-    sim_control = SimulationControl(
-        do_zone_sizing=False,
-        do_system_sizing=False,
-        do_plant_sizing=False,
-        run_for_sizing_periods=False,
-        run_for_run_periods=True,
-    )
-    sim_period = RunPeriod.from_analysis_period(
-        AnalysisPeriod(), start_day_of_week="Monday"
-    )
-    shadow_calc = ShadowCalculation(
-        solar_distribution="FullExteriorWithReflections",
-        calculation_method="PolygonClipping",
-        calculation_update_method="Timestep",
-    )
-    sim_par = SimulationParameter(
-        output=sim_output,
-        simulation_control=sim_control,
-        shadow_calculation=shadow_calc,
-        terrain_type="Country",
-        run_period=sim_period,
-        timestep=10,
-    )
-    sim_par_dict = sim_par.to_dict()
-    sim_par_json = wd / "simulation_parameter.json"
-    with open(sim_par_json, "w", encoding="utf-8") as fp:
-        json.dump(sim_par_dict, fp)
-
-    # Create OpenStudio workflow
-    osw = to_openstudio_osw(
-        wd.as_posix(),
-        model_json.as_posix(),
-        sim_par_json.as_posix(),
-        additional_measures=None,
-        epw_file=epw.file_path,
-    )
-
-    # Convert workflow to IDF file
-    _, idf = run_osw(osw, silent=False)
-
-    # Add ground monthly ground temperature strings to IDF
-    with open(idf, "r", encoding="utf-8") as fp:
-        idf_string = fp.read()
-    idf_string += f"\n\n{energyplus_strings(epw)}"
-    with open(idf, "w", encoding="utf-8") as fp:
-        idf_string = fp.write(idf_string)
-
-    # Simulate IDF
-    _, _, _, _, _ = run_idf(idf, epw.file_path, silent=False)
-
-    return surface_temperature_results_load(sql_path, epw)
-
-
-def surface_temperature_results_load(
-    sql_path: Path,
-    epw: EPW,
-) -> Dict[str, HourlyContinuousCollection]:
-    """Load results from the surface temperature simulation.
-
-    Args:
-        sql_path (Path): An SQL file containing EnergyPlus results.
-        epw (EPW): An EPW file. Required to get the temperature of the
-            sky for an unshaded case.
-
-    Returns:
-        Dict[str, HourlyContinuousCollection]: A dictionary containing
-            surface temperature-related collections.
-    """
-
-    df = load_sql(sql_path)
-
-    return {
-        "ShadedDownTemperature": collection_from_series(
-            df.filter(regex="GROUND_ZONE_UP_SHADED")
-            .droplevel([0, 1, 2], axis=1)
-            .squeeze()
-            .rename("Ground Temperature (C)")
-        ),
-        "UnshadedDownTemperature": collection_from_series(
-            df.filter(regex="GROUND_ZONE_UP_UNSHADED")
-            .droplevel([0, 1, 2], axis=1)
-            .squeeze()
-            .rename("Ground Temperature (C)")
-        ),
-        "ShadedUpTemperature": collection_from_series(
-            df.filter(regex="SHADE_ZONE_DOWN")
-            .droplevel([0, 1, 2], axis=1)
-            .squeeze()
-            .rename("Sky Temperature (C)")
-        ),
-        "UnshadedUpTemperature": epw.sky_temperature,
-    }
-
-
-def surface_temperature_results_exist(model: Model, epw: EPW = None) -> bool:
-    """Check whether results already exist for this configuration of model and EPW.
-
-    Args:
-        model (Model): The model to check for.
-        epw (EPW): The EPW to check for.  Currently unused.
-
-    Returns:
-        bool: True if the model and EPW have already been simulated, False otherwise.
-    """
-    wd = working_directory(model, False)
-
-    # Try to load existing HBJSON file and check that it matches
-    try:
-        existing_model = Model.from_hbjson(
-            (wd / f"{model.identifier}.hbjson").as_posix()
-        )
-        if not model_eq(model, existing_model, include_identifier=True):
-            return False
-    except (FileNotFoundError, AssertionError):
-        return False
-
-    # Try to load existing EPW file and check that it matches
-    try:
-        existing_epw = EPW((wd / get_filename(epw, True)).as_posix())
-        if not epw_eq(epw, existing_epw, include_header=True):
-            return False
-    except (FileNotFoundError, AssertionError):
-        return False
-
-    # Check that the output files necessary to reload exist
-    if not (wd / "run" / "eplusout.sql").exists():
-        return False
-
-    return True
-
-
-def radiant_temperature(
-    collections: List[HourlyContinuousCollection], view_factors: List[float] = None
-) -> HourlyContinuousCollection:
-    """Calculate the MRT from a list of surface temperature collections, and view
-        factors to each of those surfaces.
-
-    Args:
-        collections (List[HourlyContinuousCollection]): A list of hourly continuous collections.
-        view_factors (List[float]): A list of view factors to each of the collections. If None, then all input collections are weighted equally.
-
-    Returns:
-        HourlyContinuousCollection: An HourlyContinuousCollection of the effective radiant
-            temperature.
-    """
-
-    if view_factors is None:
-        view_factors = [1 / len(collections)] * len(collections)
-    if len(collections) != len(view_factors):
-        raise ValueError("The number of collections and view factors must be the same.")
-    if sum(view_factors) != 1:
-        raise ValueError("The sum of view factors must be 1.")
-
-    mrt_series = (
-        np.power(
-            (
-                np.power(
-                    pd.concat([collection_to_series(i) for i in collections], axis=1)
-                    + 273.15,
-                    4,
-                )
-                * view_factors
-            ).sum(axis=1),
-            0.25,
-        )
-        - 273.15
-    )
-    mrt_series.name = "Radiant Temperature (C)"
-    return collection_from_series(mrt_series)
+from ..bhom.analytics import bhom_analytics
+from ..helpers import evaporative_cooling_effect
+from ..ladybug_extension.epw import AnalysisPeriod, collection_to_series
+from ._simulatebase import SimulationResult
+from .utci import utci
 
 
-def direct_sun_hours(
-    model: Model,
-    epw: EPW,
-    analysis_period: AnalysisPeriod,
-) -> pd.Series:
-    """Simulate the number of direct sun hours for a HB model for a given time.
+@bhom_analytics()
+def ranked_mitigations(
+    simulation_result: SimulationResult,
+    n_steps: int = 8,
+    analysis_period: AnalysisPeriod = None,
+    comfort_limits: tuple[float] = (9, 26),
+    evaporative_cooling_effectiveness: float = 0.7,
+) -> pd.DataFrame:
+    """Determine the relative impact of different measures to adjust UTCI.
 
     Args:
-        model (Model):
-            A Honeybee model containing sensor grid.
-        epw (EPW):
-            An epw file object.
+        simulation_result (SimulationResult):
+            A SimulationResult object containing the results of a simulation.
+        n_steps (int, optional):
+            The number of steps to calculate per variable
+            (n_steps**3 is the number of calculations run).
+            Defaults to 8.
         analysis_period (AnalysisPeriod, optional):
-            An AnalsysiPeriod, including timestep to simulate.
+            A period to apply to results. Defaults to None.
+        comfort_limits (tuple[float], optional):
+            Optional limits to set what is considered comfortable.
+            Defaults to (9, 26).
+        evaporative_cooling_effectiveness (float, optional):
+            The effectiveness of evaporative cooling to apply to all timesteps.
+            Defaults to 0.7.
 
     Returns:
-        pd.Series:
-            A series containing per-sensor sunlight hours.
+        pd.DataFrame:
+            A table of relative UTCI impact proportions.
     """
-
-    working_dir = working_directory(model, True)
-    res_dir = working_dir / "sunlight_hours"
-    res_dir.mkdir(parents=True, exist_ok=True)
-    results_file: Path = (
-        res_dir
-        / f"{describe_analysis_period(analysis_period, save_path=True, include_timestep=True)}.res"
+    warnings.warn(
+        "This method is not yet complete, or at least it requires some further "
+        "thought to make it faster, better, stronger!"
     )
 
-    if results_file.exists():
-        CONSOLE_LOGGER.info(
-            f"[{model.identifier}] - Loading Direct Sun Hours (hours) for {describe_analysis_period(analysis_period, save_path=False, include_timestep=True)}"
-        )
-        return load_res(results_file).squeeze()
+    # TODO - break method out into parts - namely basic inputs and single
+    # Series output, referenced from elsewhere
 
-    CONSOLE_LOGGER.info(
-        f"[{model.identifier}] - Simulating direct sun hours for {describe_analysis_period(analysis_period, save_path=False, include_timestep=True)}"
-    )
-    f = io.StringIO()
-    with contextlib.redirect_stdout(f):
-        wea = Wea.from_epw_file(
-            epw.file_path, timestep=analysis_period.timestep
-        ).filter_by_analysis_period(analysis_period)
-    grid_name = model.properties.radiance.sensor_grids[0].identifier
+    if analysis_period is None:
+        analysis_period = AnalysisPeriod()
 
-    # based on time of year being simulated - adjust tree/vegetation porosity
+    # get comfort limits as single values
+    comfort_low = min(comfort_limits)
+    comfort_mid = np.mean(comfort_limits)
+    comfort_high = max(comfort_limits)
 
-    recipe = Recipe("direct-sun-hours")
-    recipe.input_value_by_name("model", model)
-    recipe.input_value_by_name("wea", wea)
-    recipe.input_value_by_name("north", 0)
-    recipe.input_value_by_name("timestep", analysis_period.timestep)
-    recipe.input_value_by_name("grid-filter", grid_name)
-    recipe_settings = RecipeSettings()
-    _ = recipe.run(
-        settings=recipe_settings,
-        radiance_check=True,
-        queenbee_path=QUEENBEE_PATH,
+    # construct dataframe containing inputs to this process
+    epw = simulation_result.epw
+    atm = collection_to_series(epw.atmospheric_station_pressure, "atm")
+    dbt = collection_to_series(epw.dry_bulb_temperature, "dbt")
+    rh = collection_to_series(epw.relative_humidity, "rh")
+    ws = collection_to_series(epw.wind_speed, "ws")
+    mrt_unshaded = collection_to_series(
+        simulation_result.unshaded_mean_radiant_temperature, "mrt_unshaded"
     )
-
-    # move results to proper location
-    pts_file = working_dir / "direct_sun_hours" / "model" / "grid" / f"{grid_name}.pts"
-    res_file = (
-        working_dir / "direct_sun_hours" / "results" / "cumulative" / f"{grid_name}.res"
+    mrt_shaded = collection_to_series(
+        simulation_result.shaded_mean_radiant_temperature, "mrt_shaded"
     )
+    utci_unshaded = utci(dbt, rh, mrt_unshaded, ws).rename("utci_unshaded")
+    df = pd.concat([atm, dbt, rh, ws, mrt_unshaded, mrt_shaded, utci_unshaded], axis=1)
 
-    shutil.copy(pts_file, res_dir / "sensors.pts")
-    shutil.copy(res_file, results_file)
-
-    return load_res(results_file).squeeze()
-
-
-def solar_radiation(model: Model, epw: EPW) -> Dict[str, HourlyContinuousCollection]:
-    """Run Radiance on a model and return the results.
-
-    Args:
-        model (Model): A honeybee Model to be run through Radiance.
-        epw (EPW): An EPW object to be used for the simulation.
-
-    Returns:
-        Dict[str, LBTHourlyContinuousCollection]: A dictionary containing radiation-related
-            collections.
-    """
-
-    wd = working_directory(model, True)
-
-    if solar_radiation_results_exist(model, epw):
-        CONSOLE_LOGGER.info(f"[{model.identifier}] - Loading annual irradiance")
-        return solar_radiation_results_load(model)
-
-    epw.save((wd / get_filename(epw, True)).as_posix())
-
-    CONSOLE_LOGGER.info(f"[{model.identifier}] - Simulating annual irradiance")
-    wea = Wea.from_epw_file(epw.file_path)
+    # filter by analysis period
+    df = df.iloc[list(analysis_period.hoys_int)]
 
-    recipe = Recipe("annual-irradiance")
-    recipe.input_value_by_name("model", model)
-    recipe.input_value_by_name("wea", wea)
-    recipe.input_value_by_name("north", 0)
-    recipe.input_value_by_name("timestep", 1)
-    recipe.input_value_by_name("output-type", "solar")
-    recipe_settings = RecipeSettings()
-    _ = recipe.run(
-        settings=recipe_settings,
-        radiance_check=True,
-        queenbee_path=QUEENBEE_PATH,
+    # get comfort mask for baseline
+    df["utci_unshaded_comfortable"] = df.utci_unshaded.between(
+        comfort_low, comfort_high
     )
 
-    return solar_radiation_results_load(model)
-
-
-def solar_radiation_results_load(model: Model) -> Dict[str, HourlyContinuousCollection]:
-    """Load results from the solar radiation simulation.
-
-    Args:
-        model (Model): A honeybee Model to be run through Radiance.
-
-    Returns:
-        Dict[str, HourlyContinuousCollection]: A dictionary containing
-            radiation-related collections.
-    """
-
-    wd = working_directory(model, False)
-
-    shaded_down_direct_irradiance = collection_from_series(
-        make_annual(load_ill(wd / "annual_irradiance/results/direct/SHADED_DOWN.ill"))
-        .squeeze()
-        .fillna(0)
-        .rename("Irradiance (W/m2)")
-    )
-    shaded_up_direct_irradiance = collection_from_series(
-        make_annual(load_ill(wd / "annual_irradiance/results/direct/SHADED_UP.ill"))
-        .squeeze()
-        .fillna(0)
-        .rename("Irradiance (W/m2)")
-    )
-    unshaded_down_direct_irradiance = collection_from_series(
-        make_annual(load_ill(wd / "annual_irradiance/results/direct/UNSHADED_DOWN.ill"))
-        .squeeze()
-        .fillna(0)
-        .rename("Irradiance (W/m2)")
-    )
-    unshaded_up_direct_irradiance = collection_from_series(
-        make_annual(load_ill(wd / "annual_irradiance/results/direct/UNSHADED_UP.ill"))
-        .squeeze()
-        .fillna(0)
-        .rename("Irradiance (W/m2)")
-    )
-    shaded_down_total_irradiance = collection_from_series(
-        make_annual(load_ill(wd / "annual_irradiance/results/total/SHADED_DOWN.ill"))
-        .squeeze()
-        .fillna(0)
-        .rename("Irradiance (W/m2)")
-    )
-    shaded_up_total_irradiance = collection_from_series(
-        make_annual(load_ill(wd / "annual_irradiance/results/total/SHADED_UP.ill"))
-        .squeeze()
-        .fillna(0)
-        .rename("Irradiance (W/m2)")
-    )
-    unshaded_down_total_irradiance = collection_from_series(
-        make_annual(load_ill(wd / "annual_irradiance/results/total/UNSHADED_DOWN.ill"))
-        .squeeze()
-        .fillna(0)
-        .rename("Irradiance (W/m2)")
-    )
-    unshaded_up_total_irradiance = collection_from_series(
-        make_annual(load_ill(wd / "annual_irradiance/results/total/UNSHADED_UP.ill"))
-        .squeeze()
-        .fillna(0)
-        .rename("Irradiance (W/m2)")
-    )
-    shaded_down_diffuse_irradiance = (
-        shaded_down_total_irradiance - shaded_down_direct_irradiance
+    # get distance from comfortable (midpoint) for each timestep in baseline
+    df["utci_unshaded_distance_from_comfortable_midpoint"] = (
+        df.utci_unshaded - comfort_mid
     )
-    shaded_up_diffuse_irradiance = (
-        shaded_up_total_irradiance - shaded_up_direct_irradiance
+    df[
+        "utci_unshaded_distance_from_comfortable"
+    ] = df.utci_unshaded_distance_from_comfortable_midpoint.where(
+        ~df.utci_unshaded_comfortable, 0
     )
-    unshaded_down_diffuse_irradiance = (
-        unshaded_down_total_irradiance - unshaded_down_direct_irradiance
-    )
-    unshaded_up_diffuse_irradiance = (
-        unshaded_up_total_irradiance - unshaded_up_direct_irradiance
-    )
-
-    # load data and return in dict
-    return {
-        "ShadedDownDiffuseIrradiance": shaded_down_diffuse_irradiance,
-        "ShadedDownDirectIrradiance": shaded_down_direct_irradiance,
-        "ShadedDownTotalIrradiance": shaded_down_total_irradiance,
-        "ShadedUpDiffuseIrradiance": shaded_up_diffuse_irradiance,
-        "ShadedUpDirectIrradiance": shaded_up_direct_irradiance,
-        "ShadedUpTotalIrradiance": shaded_up_total_irradiance,
-        "UnshadedDownDiffuseIrradiance": unshaded_down_diffuse_irradiance,
-        "UnshadedDownDirectIrradiance": unshaded_down_direct_irradiance,
-        "UnshadedDownTotalIrradiance": unshaded_down_total_irradiance,
-        "UnshadedUpDiffuseIrradiance": unshaded_up_diffuse_irradiance,
-        "UnshadedUpDirectIrradiance": unshaded_up_direct_irradiance,
-        "UnshadedUpTotalIrradiance": unshaded_up_total_irradiance,
-    }
-
-
-def solar_radiation_results_exist(model: Model, epw: EPW = None) -> bool:
-    """Check whether results already exist for this configuration of model and EPW.
-
-    Args:
-        model (Model): The model to check for.
-        epw (EPW): The EPW to check for.
-
-    Returns:
-        bool: True if the model and EPW have already been simulated, False otherwise.
-    """
-    wd = working_directory(model, False)
-
-    # Try to load existing HBJSON file and check that it matches
-    try:
-        existing_model = Model.from_hbjson(
-            (wd / "annual_irradiance" / f"{model.identifier}.hbjson").as_posix()
-        )
-        if not model_eq(model, existing_model, include_identifier=True):
-            return False
-    except (FileNotFoundError, AssertionError):
-        return False
 
-    # Try to load existing EPW file and check that it matches
-    try:
-        existing_epw = EPW((wd / get_filename(epw, True)).as_posix())
-        if not epw_eq(epw, existing_epw, include_header=True):
-            return False
-    except (FileNotFoundError, AssertionError):
-        return False
+    # get possible values for shade/shelter/evapclg
+    shading_proportions = np.linspace(1, 0, n_steps)
+    wind_shelter_proportions = np.linspace(0, 1, n_steps)
+    evap_clg_proportions = np.linspace(0, 1, n_steps)
 
-    # Check that the output files necessary to reload exist
-    if not all(
-        [
-            (wd / "annual_irradiance/results/direct/SHADED_DOWN.ill").exists(),
-            (wd / "annual_irradiance/results/direct/SHADED_UP.ill").exists(),
-            (wd / "annual_irradiance/results/direct/UNSHADED_DOWN.ill").exists(),
-            (wd / "annual_irradiance/results/direct/UNSHADED_UP.ill").exists(),
-            (wd / "annual_irradiance/results/total/SHADED_DOWN.ill").exists(),
-            (wd / "annual_irradiance/results/total/SHADED_UP.ill").exists(),
-            (wd / "annual_irradiance/results/total/UNSHADED_DOWN.ill").exists(),
-            (wd / "annual_irradiance/results/total/UNSHADED_UP.ill").exists(),
-        ]
+    def _temp(
+        dbt,
+        rh,
+        atm,
+        ws,
+        mrt_unshaded,
+        mrt_shaded,
+        utci_unshaded_distance_from_comfortable_midpoint,
+        name,
     ):
-        return False
-
-    return True
-
-
-@dataclass(init=True, repr=True, eq=True)
-class SimulationResult(BHoMObject):
-    """An object containing all the results of a mean radiant temperature
-        simulation.
-
-    Args:
-        epw_file (str):
-            An epw file path.
-        ground_material (Union[LBTEnergyMaterialVegetation, LBTEnergyMaterial]):
-            A surface material for the ground zones topmost face.
-        shade_material (Union[LBTEnergyMaterialVegetation, LBTEnergyMaterial]):
-            A surface material for the shade zones faces.
-        identifier (str, optional):
-            A unique identifier for the model. Defaults to None which will
-            generate a unique identifier. This is useful for testing purposes!
-
-    Returns:
-        SimulationResult:
-            An object containing all the results of a mean radiant
-            temperature simulation.
-    """
-
-    EpwFile: Path = field(repr=False, compare=True)
-    GroundMaterial: Union[OpaqueVegetationMaterial, OpaqueMaterial] = field(
-        repr=False, compare=True
-    )
-    ShadeMaterial: Union[OpaqueVegetationMaterial, OpaqueMaterial] = field(
-        repr=False, compare=True
-    )
-    Identifier: str = field(repr=True, compare=True, default=None)
-
-    ShadedDownTemperature: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    ShadedUpTemperature: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    ShadedRadiantTemperature: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    ShadedLongwaveMeanRadiantTemperatureDelta: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    ShadedShortwaveMeanRadiantTemperatureDelta: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    ShadedMeanRadiantTemperature: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-
-    UnshadedDownTemperature: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    UnshadedUpTemperature: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    UnshadedRadiantTemperature: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    UnshadedLongwaveMeanRadiantTemperatureDelta: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    UnshadedShortwaveMeanRadiantTemperatureDelta: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-    UnshadedMeanRadiantTemperature: HourlyContinuousCollection = field(
-        init=True, repr=False, compare=False, default=None
-    )
-
-    _t: str = field(
-        init=False,
-        repr=False,
-        compare=True,
-        default="BH.oM.LadybugTools.SimulationResult",
-    )
-
-    def __post_init__(self):
-        self.EpwFile = Path(self.EpwFile).resolve()
-
-        if self.Identifier is None:
-            self.Identifier = simulation_id(
-                self.EpwFile,
-                self.GroundMaterial.to_lbt(),
-                self.ShadeMaterial.to_lbt(),
-            )
-
-        # wrap methods within this class
-        super().__post_init__()
-
-    @classmethod
-    def from_dict(cls, dictionary: Dict[str, Any]) -> SimulationResult:
-        """Create this object from a dictionary."""
-
-        if not isinstance(
-            dictionary["GroundMaterial"], (OpaqueMaterial, OpaqueVegetationMaterial)
-        ):
-            dictionary["GroundMaterial"] = material_from_dict(
-                dictionary["GroundMaterial"]
-            )
-
-        if not isinstance(
-            dictionary["ShadeMaterial"], (OpaqueMaterial, OpaqueVegetationMaterial)
-        ):
-            dictionary["ShadeMaterial"] = material_from_dict(
-                dictionary["ShadeMaterial"]
-            )
-
-        # handle objects
-        for simulated_result in [
-            "ShadedDownTemperature",
-            "ShadedUpTemperature",
-            "ShadedRadiantTemperature",
-            "ShadedLongwaveMeanRadiantTemperatureDelta",
-            "ShadedShortwaveMeanRadiantTemperatureDelta",
-            "ShadedMeanRadiantTemperature",
-            "UnshadedDownTemperature",
-            "UnshadedUpTemperature",
-            "UnshadedRadiantTemperature",
-            "UnshadedLongwaveMeanRadiantTemperatureDelta",
-            "UnshadedShortwaveMeanRadiantTemperatureDelta",
-            "UnshadedMeanRadiantTemperature",
-        ]:
-            if dictionary[simulated_result] is None:
-                continue
-            if isinstance(dictionary[simulated_result], HourlyContinuousCollection):
-                continue
-            if isinstance(dictionary[simulated_result], dict):
-                if "type" in dictionary[simulated_result].keys():
-                    dictionary[simulated_result] = HourlyContinuousCollection.from_dict(
-                        dictionary[simulated_result]
-                    )
-                else:
-                    dictionary[simulated_result] = None
-
-        return cls(
-            EpwFile=dictionary["EpwFile"],
-            GroundMaterial=dictionary["GroundMaterial"],
-            ShadeMaterial=dictionary["ShadeMaterial"],
-            Identifier=dictionary["Identifier"],
-            ShadedDownTemperature=dictionary["ShadedDownTemperature"],
-            ShadedUpTemperature=dictionary["ShadedUpTemperature"],
-            ShadedRadiantTemperature=dictionary["ShadedRadiantTemperature"],
-            ShadedLongwaveMeanRadiantTemperatureDelta=dictionary[
-                "ShadedLongwaveMeanRadiantTemperatureDelta"
-            ],
-            ShadedShortwaveMeanRadiantTemperatureDelta=dictionary[
-                "ShadedShortwaveMeanRadiantTemperatureDelta"
-            ],
-            ShadedMeanRadiantTemperature=dictionary["ShadedMeanRadiantTemperature"],
-            UnshadedDownTemperature=dictionary["UnshadedDownTemperature"],
-            UnshadedUpTemperature=dictionary["UnshadedUpTemperature"],
-            UnshadedRadiantTemperature=dictionary["UnshadedRadiantTemperature"],
-            UnshadedLongwaveMeanRadiantTemperatureDelta=dictionary[
-                "UnshadedLongwaveMeanRadiantTemperatureDelta"
-            ],
-            UnshadedShortwaveMeanRadiantTemperatureDelta=dictionary[
-                "UnshadedShortwaveMeanRadiantTemperatureDelta"
-            ],
-            UnshadedMeanRadiantTemperature=dictionary["UnshadedMeanRadiantTemperature"],
-        )
-
-    @classmethod
-    def from_json(cls, json_string: str) -> SimulationResult:
-        """Create this object from a JSON string."""
-
-        dictionary = inf_str_to_inf_dtype(
-            json.loads(json_string, object_hook=fix_bhom_jsondict)
-        )
-        return cls.from_dict(dictionary)
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Return this object as it's dictionary equivalent."""
-        dictionary = {}
-        for k, v in self.__dict__.items():
-            if isinstance(getattr(self, k), FunctionType):
-                continue
-            dictionary[k] = v
-        dictionary["_t"] = self._t
-        return dictionary
-
-    def to_json(self) -> str:
-        """Return this object as it's JSON string equivalent."""
-        return json.dumps(inf_dtype_to_inf_str(self.to_dict()), cls=BHoMEncoder)
-
-    @property
-    def epw(self) -> EPW:
-        """Return the EPW opject loaded from the associated EPW file."""
-        return EPW(self.EpwFile)
-
-    @property
-    def model(self) -> Model:
-        """Return the model object created from the associated materials."""
-        return create_model(
-            self.GroundMaterial.to_lbt(), self.ShadeMaterial.to_lbt(), self.Identifier
-        )
-
-    @property
-    def epw_file(self) -> Path:
-        """Handy accessor using proper Python naming style."""
-        return self.EpwFile
-
-    @property
-    def ground_material(self) -> Union[OpaqueMaterial, OpaqueVegetationMaterial]:
-        """Handy accessor using proper Python naming style."""
-        return self.GroundMaterial
-
-    @property
-    def shade_material(self) -> Union[OpaqueMaterial, OpaqueVegetationMaterial]:
-        """Handy accessor using proper Python naming style."""
-        return self.ShadeMaterial
-
-    @property
-    def identifier(self) -> str:
-        """Handy accessor using proper Python naming style."""
-        return self.Identifier
-
-    @property
-    def shaded_down_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.ShadedDownTemperature
-
-    @property
-    def shaded_up_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.ShadedUpTemperature
-
-    @property
-    def shaded_radiant_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.ShadedRadiantTemperature
-
-    @property
-    def shaded_longwave_mean_radiant_temperature_delta(
-        self,
-    ) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.ShadedLongwaveMeanRadiantTemperatureDelta
-
-    @property
-    def shaded_shortwave_mean_radiant_temperature_delta(
-        self,
-    ) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.ShadedShortwaveMeanRadiantTemperatureDelta
-
-    @property
-    def shaded_mean_radiant_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.ShadedMeanRadiantTemperature
-
-    @property
-    def unshaded_down_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.UnshadedDownTemperature
-
-    @property
-    def unshaded_up_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.UnshadedUpTemperature
-
-    @property
-    def unshaded_radiant_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.UnshadedRadiantTemperature
-
-    @property
-    def unshaded_longwave_mean_radiant_temperature_delta(
-        self,
-    ) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.UnshadedLongwaveMeanRadiantTemperatureDelta
-
-    @property
-    def unshaded_shortwave_mean_radiant_temperature_delta(
-        self,
-    ) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.UnshadedShortwaveMeanRadiantTemperatureDelta
-
-    @property
-    def unshaded_mean_radiant_temperature(self) -> HourlyContinuousCollection:
-        """Handy accessor using proper Python naming style."""
-        return self.UnshadedMeanRadiantTemperature
-
-    def is_run(self) -> bool:
-        """Helper method to determine whether this object is populated with simulation results."""
-        variables = [
-            "ShadedDownTemperature",
-            "ShadedUpTemperature",
-            "ShadedRadiantTemperature",
-            "ShadedLongwaveMeanRadiantTemperatureDelta",
-            "ShadedShortwaveMeanRadiantTemperatureDelta",
-            "ShadedMeanRadiantTemperature",
-            "UnshadedDownTemperature",
-            "UnshadedUpTemperature",
-            "UnshadedRadiantTemperature",
-            "UnshadedLongwaveMeanRadiantTemperatureDelta",
-            "UnshadedShortwaveMeanRadiantTemperatureDelta",
-            "UnshadedMeanRadiantTemperature",
-        ]
-        if any(getattr(self, i) is None for i in variables):
-            return False
-        return True
-
-    def run(self) -> SimulationResult:
-        """Run this SimulationResult and return the populated object."""
-
-        # create object to populate
-        sim_res = SimulationResult(
-            self.EpwFile, self.GroundMaterial, self.ShadeMaterial
-        )
-
-        # create referenced objects
-        epw = self.epw
-        model = self.model
-
-        # calculate surrounding surface temperatures
-        results = surface_temperature(model, epw)
-
-        # populate simulated variables
-        for k, v in results.items():
-            setattr(sim_res, k, v)
-
-        # calculate other variables
-        sim_res.ShadedRadiantTemperature = radiant_temperature(
+        # create feasible ranges of values
+        dbts, rhs = np.array(
             [
-                sim_res.ShadedDownTemperature,
-                sim_res.ShadedUpTemperature,
-            ],
-            [0.5, 0.5],
-        )
-        sim_res.UnshadedRadiantTemperature = radiant_temperature(
-            [
-                sim_res.UnshadedDownTemperature,
-                sim_res.UnshadedUpTemperature,
-            ],
-            [0.5, 0.5],
-        )
-
-        # calculate MRT
-        params = SolarCalParameter()
-
-        shaded_cal = OutdoorSolarCal(
-            location=epw.location,
-            direct_normal_solar=epw.direct_normal_radiation,
-            diffuse_horizontal_solar=epw.diffuse_horizontal_radiation,
-            horizontal_infrared=epw.horizontal_infrared_radiation_intensity,
-            surface_temperatures=sim_res.ShadedRadiantTemperature,
-            floor_reflectance=model.faces_by_identifier(["GROUND_ZONE_UP_SHADED"])[
-                0
-            ].properties.energy.construction.outside_solar_reflectance,
-            sky_exposure=0,
-            fraction_body_exposed=1,
-            solarcal_body_parameter=params,
-        )
-        sim_res.ShadedMeanRadiantTemperature = shaded_cal.mean_radiant_temperature
-        sim_res.ShadedShortwaveMeanRadiantTemperatureDelta = (
-            shaded_cal.shortwave_mrt_delta
-        )
-        sim_res.ShadedLongwaveMeanRadiantTemperatureDelta = (
-            shaded_cal.longwave_mrt_delta
-        )
-
-        unshaded_cal = OutdoorSolarCal(
-            location=epw.location,
-            direct_normal_solar=epw.direct_normal_radiation,
-            diffuse_horizontal_solar=epw.diffuse_horizontal_radiation,
-            horizontal_infrared=epw.horizontal_infrared_radiation_intensity,
-            surface_temperatures=sim_res.UnshadedRadiantTemperature,
-            floor_reflectance=model.faces_by_identifier(["GROUND_ZONE_UP_SHADED"])[
-                0
-            ].properties.energy.construction.outside_solar_reflectance,
-            sky_exposure=1,
-            fraction_body_exposed=1,
-            solarcal_body_parameter=params,
-        )
-        sim_res.UnshadedMeanRadiantTemperature = unshaded_cal.mean_radiant_temperature
-        sim_res.UnshadedShortwaveMeanRadiantTemperatureDelta = (
-            unshaded_cal.shortwave_mrt_delta
-        )
-        sim_res.UnshadedLongwaveMeanRadiantTemperatureDelta = (
-            unshaded_cal.longwave_mrt_delta
-        )
-
-        return sim_res
-
-    def to_dataframe(
-        self, include_epw: bool = False, include_epw_additional: bool = False
-    ) -> pd.DataFrame:
-        """Create a Pandas DataFrame from this object.
-
-        Args:
-            include_epw (bool, optional):
-                Set to True to include the dataframe for the EPW file also.
-            include_epw_additional (bool, optional): Set to True to also include calculated
-                values such as sun position along with EPW. Only includes if include_epw is
-                True also.
-
-        Returns:
-            pd.DataFrame: A Pandas DataFrame with this objects properties.
-        """
-
-        if not self.is_run():
-            raise AttributeError(f'This {self.__class__.__name__} has not been "run"!')
-
-        obj_series = []
-        for k, v in self.to_dict().items():
-            if isinstance(v, HourlyContinuousCollection):
-                series = collection_to_series(v)
-                category = "Shaded" if k.lower().startswith("shaded") else "Unshaded"
-                obj_series.append(
-                    series.rename((self.Identifier, category, f"{series.name} ({k})"))
+                evaporative_cooling_effect(
+                    dbt, rh, evap_x * evaporative_cooling_effectiveness, atm
                 )
-        obj_df = pd.concat(obj_series, axis=1)
-
-        if include_epw:
-            return pd.concat(
-                [
-                    epw_to_dataframe(self.epw, include_epw_additional),
-                    obj_df,
-                ],
-                axis=1,
-            )
-
-        return obj_df
-
-    def ranked_mitigations(
-        self,
-        n_steps: int = 8,
-        analysis_period: AnalysisPeriod = None,
-        comfort_limits: Tuple[float] = (9, 26),
-        evaporative_cooling_effectiveness: float = 0.7,
-    ) -> pd.DataFrame:
-        """Determine the relative impact of different measures to adjust UTCI.
-
-        Args:
-            n_steps (int, optional):
-                The number of steps to calculate per variable (n_steps**3 is the number of calculations run). Defaults to 8.
-            analysis_period (AnalysisPeriod, optional):
-                A period to apply to results. Defaults to None.
-            comfort_limits (Tuple[float], optional):
-                Optional limits to set what is considered comfortable. Defaults to (9, 26).
-
-        Returns:
-            pd.DataFrame:
-                A table of relative UTCI impact proportions.
-        """
-
-        # TODO - break method out into parts - namely basic inputs and single Series output, referenced from elsewhere
-
-        if analysis_period is None:
-            analysis_period = AnalysisPeriod()
-
-        # get comfort limits as single values
-        comfort_low = min(comfort_limits)
-        comfort_mid = np.mean(comfort_limits)
-        comfort_high = max(comfort_limits)
-
-        # construct dataframe containing inputs to this process
-        epw = self.epw
-        atm = collection_to_series(epw.atmospheric_station_pressure).rename("atm")
-        dbt = collection_to_series(epw.dry_bulb_temperature).rename("dbt")
-        rh = collection_to_series(epw.relative_humidity).rename("rh")
-        ws = collection_to_series(epw.wind_speed).rename("ws")
-        mrt_unshaded = collection_to_series(self.UnshadedMeanRadiantTemperature).rename(
-            "mrt_unshaded"
-        )
-        mrt_shaded = collection_to_series(self.ShadedMeanRadiantTemperature).rename(
-            "mrt_shaded"
-        )
-        utci_unshaded = utci(dbt, rh, mrt_unshaded, ws).rename("utci_unshaded")
-        df = pd.concat(
-            [atm, dbt, rh, ws, mrt_unshaded, mrt_shaded, utci_unshaded], axis=1
-        )
-
-        # filter by analysis period
-        df = df.iloc[list(analysis_period.hoys_int)]
-
-        # get comfort mask for baseline
-        df["utci_unshaded_comfortable"] = df.utci_unshaded.between(
-            comfort_low, comfort_high
-        )
-
-        # get distance from comfortable (midpoint) for each timestep in baseline
-        df["utci_unshaded_distance_from_comfortable_midpoint"] = (
-            df.utci_unshaded - comfort_mid
-        )
-        df[
-            "utci_unshaded_distance_from_comfortable"
-        ] = df.utci_unshaded_distance_from_comfortable_midpoint.where(
-            ~df.utci_unshaded_comfortable, 0
-        )
-
-        # get possible values for shade/shelter/evapclg
-        shading_proportions = np.linspace(1, 0, n_steps)
-        wind_shelter_proportions = np.linspace(0, 1, n_steps)
-        evap_clg_proportions = np.linspace(0, 1, n_steps)
-
-        def _temp(
-            dbt,
-            rh,
-            atm,
-            ws,
-            mrt_unshaded,
-            mrt_shaded,
-            utci_unshaded_distance_from_comfortable_midpoint,
-            name,
-        ):
-            # create feasible ranges of values
-            dbts, rhs = np.array(
-                [
-                    evaporative_cooling_effect(
-                        dbt, rh, evap_x * evaporative_cooling_effectiveness, atm
-                    )
-                    for evap_x in evap_clg_proportions
-                ]
-            ).T
-            wss = ws * wind_shelter_proportions
-            mrts = np.interp(shading_proportions, [0, 1], [mrt_unshaded, mrt_shaded])
-
-            # create all possible combinations of inputs
-            dbts, rhs, mrts, wss = np.array(
-                list(itertools.product(dbts, rhs, mrts, wss))
-            ).T
-            utcis = utci([dbts], [rhs], [mrts], [wss])[0]
-            shad_props, _, windshlt_props, evapclg_props = np.array(
-                list(
-                    itertools.product(
-                        shading_proportions,
-                        shading_proportions,
-                        wind_shelter_proportions,
-                        evap_clg_proportions,
-                    )
+                for evap_x in evap_clg_proportions
+            ]
+        ).T
+        wss = ws * wind_shelter_proportions
+        mrts = np.interp(shading_proportions, [0, 1], [mrt_unshaded, mrt_shaded])
+
+        # create all possible combinations of inputs
+        dbts, rhs, mrts, wss = np.array(list(itertools.product(dbts, rhs, mrts, wss))).T
+        utcis = utci([dbts], [rhs], [mrts], [wss])[0]
+        shad_props, _, windshlt_props, evapclg_props = np.array(
+            list(
+                itertools.product(
+                    shading_proportions,
+                    shading_proportions,
+                    wind_shelter_proportions,
+                    evap_clg_proportions,
                 )
-            ).T
-
-            # reshape matrix
-            mtx = pd.DataFrame(
-                [
-                    dbts,
-                    rhs,
-                    mrts,
-                    wss,
-                    utcis,
-                    shad_props,
-                    windshlt_props,
-                    evapclg_props,
-                ],
-                index=[
-                    "dbt",
-                    "rh",
-                    "mrt",
-                    "ws",
-                    "utci",
-                    "shade",
-                    "wind shelter",
-                    "evaporative cooling",
-                ],
-            ).T
-
-            # get comfort mask for current timestep
-            mtx["utci_comfortable"] = mtx.utci.between(comfort_low, comfort_high)
-
-            # get distance from comfortable (midpoint) for current timestep
-            mtx["utci_distance_from_comfortable_midpoint"] = mtx.utci - comfort_mid
-            mtx[
-                "utci_distance_from_comfortable"
-            ] = mtx.utci_distance_from_comfortable_midpoint.where(
-                ~mtx.utci_comfortable, 0
-            )
-
-            # determine whether comfort has improved, and drop rows where it hasnt
-            mtx["comfort_improved"] = abs(
-                mtx.utci_distance_from_comfortable_midpoint
-            ) < abs(utci_unshaded_distance_from_comfortable_midpoint)
-            mtx = mtx[mtx.comfort_improved]
-
-            # sort by distance to comfort midpoint, to get optimal conditions
-            mtx["utci_distance_from_comfortable_midpoint_absolute"] = abs(
-                mtx.utci_distance_from_comfortable_midpoint
             )
-            mtx = mtx.sort_values("utci_distance_from_comfortable_midpoint_absolute")
-
-            # normalise variables
-            temp = (
-                mtx[["shade", "wind shelter", "evaporative cooling"]]
-                / mtx[["shade", "wind shelter", "evaporative cooling"]].sum(axis=0)
-            ).reset_index(drop=True)
-
-            # get topmost 25%
-            ranks = temp.head(int(len(temp) / 4)).mean(axis=0)
-            ranks = ranks / ranks.sum()
-            ranks.name = name
-
-            # TODO - include "do nothing" as an option for ranking!
-
-            return ranks
+        ).T
 
-        tqdm.pandas(
-            desc=f"Calculating ranked beneficial impact of comfort mitigation measures for {epw}"
-        )
-        return df.progress_apply(
-            lambda row: _temp(
-                row.dbt,
-                row.rh,
-                row.atm,
-                row.ws,
-                row.mrt_unshaded,
-                row.mrt_shaded,
-                row.utci_unshaded_distance_from_comfortable_midpoint,
-                row.name,
-            ),
-            axis=1,
-        )
-
-    @staticmethod
-    def plot_ranked_mitigations(df: pd.DataFrame, ax: plt.Axes = None) -> plt.Axes:
-        raise NotImplementedError("Not yet done, also, should probably go elsewhere!")
+        # reshape matrix
+        mtx = pd.DataFrame(
+            [
+                dbts,
+                rhs,
+                mrts,
+                wss,
+                utcis,
+                shad_props,
+                windshlt_props,
+                evapclg_props,
+            ],
+            index=[
+                "dbt",
+                "rh",
+                "mrt",
+                "ws",
+                "utci",
+                "shade",
+                "wind shelter",
+                "evaporative cooling",
+            ],
+        ).T
+
+        # get comfort mask for current timestep
+        mtx["utci_comfortable"] = mtx.utci.between(comfort_low, comfort_high)
+
+        # get distance from comfortable (midpoint) for current timestep
+        mtx["utci_distance_from_comfortable_midpoint"] = mtx.utci - comfort_mid
+        mtx[
+            "utci_distance_from_comfortable"
+        ] = mtx.utci_distance_from_comfortable_midpoint.where(~mtx.utci_comfortable, 0)
+
+        # determine whether comfort has improved, and drop rows where it hasnt
+        mtx["comfort_improved"] = abs(
+            mtx.utci_distance_from_comfortable_midpoint
+        ) < abs(utci_unshaded_distance_from_comfortable_midpoint)
+        mtx = mtx[mtx.comfort_improved]
+
+        # sort by distance to comfort midpoint, to get optimal conditions
+        mtx["utci_distance_from_comfortable_midpoint_absolute"] = abs(
+            mtx.utci_distance_from_comfortable_midpoint
+        )
+        mtx = mtx.sort_values("utci_distance_from_comfortable_midpoint_absolute")
+
+        # normalise variables
+        temp = (
+            mtx[["shade", "wind shelter", "evaporative cooling"]]
+            / mtx[["shade", "wind shelter", "evaporative cooling"]].sum(axis=0)
+        ).reset_index(drop=True)
+
+        # get topmost 25%
+        ranks = temp.head(int(len(temp) / 4)).mean(axis=0)
+        ranks = ranks / ranks.sum()
+        ranks.name = name
+
+        # TODO - include "do nothing" as an option for ranking!
+
+        return ranks
+
+    tqdm.pandas(
+        desc=f"Calculating ranked beneficial impact of comfort mitigation measures for {epw}"
+    )
+    return df.progress_apply(
+        lambda row: _temp(
+            row.dbt,
+            row.rh,
+            row.atm,
+            row.ws,
+            row.mrt_unshaded,
+            row.mrt_shaded,
+            row.utci_unshaded_distance_from_comfortable_midpoint,
+            row.name,
+        ),
+        axis=1,
+    )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/calculate.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/calculate.py
index 84c4404b..bbf0ab2d 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/calculate.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/calculate.py
@@ -1,35 +1,34 @@
-from typing import Tuple
-
+"""Methods for calculating spatial comfort metrics."""
 import numpy as np
 import pandas as pd
 from scipy.interpolate import interp1d
 
-from ...ladybug_extension.analysis_period import (
+from ...ladybug_extension.analysisperiod import (
     AnalysisPeriod,
     analysis_period_to_datetimes,
 )
 
 
 def shaded_unshaded_interpolation(
-    unshaded_value: Tuple[float],
-    shaded_value: Tuple[float],
-    total_irradiance: Tuple[Tuple[float]],
-    sky_view: Tuple[float],
-    sun_up: Tuple[bool],
+    unshaded_value: tuple[float],
+    shaded_value: tuple[float],
+    total_irradiance: tuple[tuple[float]],
+    sky_view: tuple[float],
+    sun_up: tuple[bool],
 ) -> pd.DataFrame:
     """Interpolate between the unshaded and shaded input values, using the total irradiance and sky
         view as proportional values for each point.
 
     Args:
-        unshaded_value (Tuple[float]):
+        unshaded_value (tuple[float]):
             A list of hourly values for the unshaded case.
-        shaded_value (Tuple[float]):
+        shaded_value (tuple[float]):
             A collection of hourly values for the shaded case.
-        total_irradiance (Tuple[Tuple[float]]):
+        total_irradiance (tuple[tuple[float]]):
             An array with the total irradiance for each point for each hour.
-        sky_view (Tuple[float]):
+        sky_view (tuple[float]):
             A list with the sky view for each point.
-        sun_up (Tuple[bool]):
+        sun_up (tuple[bool]):
             A list of booleans stating whether the sun is up.
 
     Returns:
@@ -59,7 +58,8 @@ def shaded_unshaded_interpolation(
     # Check for shape alignment
     if not len(sky_view) == total_irradiance.shape[1]:
         raise ValueError(
-            f"Number of sky-view values must match length of total irradiance values ({len(sky_view)} != {total_irradiance.shape[1]})"
+            "Number of sky-view values must match length of total irradiance values "
+            f"({len(sky_view)} != {total_irradiance.shape[1]})"
         )
     if (
         len(unshaded_value)
@@ -68,7 +68,8 @@ def shaded_unshaded_interpolation(
         != len(sun_up)
     ):
         raise ValueError(
-            f"Number of points-in-time must match for unshaded, shaded, total_irradiance and sun_up values ({len(sky_view)} != {total_irradiance.shape[1]})"
+            "Number of points-in-time must match for unshaded, shaded, "
+            f"total_irradiance and sun_up values ({len(sky_view)} != {total_irradiance.shape[1]})"
         )
 
     # get the target range into which the value should fit following interpolation
@@ -115,8 +116,8 @@ def shaded_unshaded_interpolation(
 
 def rwdi_london_thermal_comfort_category(
     utci: pd.DataFrame,
-    comfort_limits: Tuple[float] = (0, 32),
-    hours: Tuple[float] = (8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20),
+    comfort_limits: tuple[float] = (0, 32),
+    hours: tuple[float] = (8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20),
 ) -> pd.Series:
     """From a dataframe containing annual hourly spatial UTCI values,
     categorise each column as one of the RWDI London Thermal Comfort
@@ -130,10 +131,10 @@ def rwdi_london_thermal_comfort_category(
     Args:
         utci (pd.DataFrame):
             A temporo-spatial UTCI data collection.
-        comfort_limits (Tuple[float], optional):
+        comfort_limits (tuple[float], optional):
             The UTCI values within which "comfortable" is defined.
             Defaults to (0, 32).
-        hours (Tuple[float], optional):
+        hours (tuple[float], optional):
             The hours to include in this assessment.
             Defaults to (8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20).
 
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/cfd.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/cfd.py
index bc83f790..8f9538cc 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/cfd.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/cfd.py
@@ -1,8 +1,12 @@
+"""Methods for handling CFD results for spatial comfort assessments."""
+# pylint: disable=E0401
 import json
 import warnings
 from io import StringIO
 from pathlib import Path
 
+# pylint: enable=E0401
+
 import numpy as np
 import pandas as pd
 from ladybug.epw import EPW
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/compare.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/compare.py
index 0b416f15..926a3183 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/compare.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/compare.py
@@ -1,4 +1,4 @@
-from typing import List, Tuple
+"""Methods for comparing spatial comfort results."""
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -7,15 +7,15 @@
 from matplotlib.tri.triangulation import Triangulation
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 
-from ...bhomutil.analytics import CONSOLE_LOGGER
-from ...helpers import create_triangulation
-from ...ladybug_extension.analysis_period import describe_analysis_period
+from ...bhom import CONSOLE_LOGGER
+from ...ladybug_extension.analysisperiod import describe_analysis_period
+from ...plot.utilities import create_triangulation
 from .spatial_comfort import SpatialComfort, SpatialMetric
 
 
 def get_common_pt_indices(
     spatial_result_1: SpatialComfort, spatial_result_2: SpatialComfort
-) -> List[List[int]]:
+) -> list[list[int]]:
     """Get the indices of the points that are common between two spatial comfort cases.
 
     Args:
@@ -24,7 +24,7 @@ def get_common_pt_indices(
         spatial_result_2 (SpatialComfort):
             A SpatialComfort object.
 
-    Returns (List[List[int]]):
+    Returns (list[list[int]]):
         A list of two lists of indices that correspond to the points in the two.
 
     """
@@ -48,7 +48,8 @@ def get_triangulation(
         spatial_result_2 (SpatialComfort):
             A SpatialComfort object.
         alpha (float, optional):
-            A number between 0 and 2 that controls the distance between vertices of the triangulation, above which cells are removed. Default is 1.9.
+            A number between 0 and 2 that controls the distance between vertices of the triangulation,
+            above which cells are removed. Default is 1.9.
 
     Returns (Triangulation):
         A matplotlib Triangulation object.
@@ -95,7 +96,8 @@ def compare_utci(
         raise ValueError("This type of plot is not possible for the requested metric.")
 
     CONSOLE_LOGGER.info(
-        f"[SpatialComfort - Comparison] - Plotting UTCI difference between {spatial_result_1} and {spatial_result_2} for {describe_analysis_period(analysis_period)}"
+        "[SpatialComfort - Comparison] - Plotting UTCI difference between "
+        f"{spatial_result_1} and {spatial_result_2} for {describe_analysis_period(analysis_period)}"
     )
     # get point indices
     indices = get_common_pt_indices(spatial_result_1, spatial_result_2)
@@ -150,7 +152,10 @@ def compare_utci(
 
     # add title
     ax.set_title(
-        f"{describe_analysis_period(analysis_period)}\n{metric.description()} - average difference\n{spatial_result_1} > {spatial_result_2}",
+        (
+            f"{describe_analysis_period(analysis_period)}\n{metric.description()} - "
+            f"average difference\n{spatial_result_1} > {spatial_result_2}"
+        ),
         ha="left",
         va="bottom",
         x=0,
@@ -167,7 +172,8 @@ def compare_mrt(
     analysis_period: AnalysisPeriod = AnalysisPeriod(),
     alpha: float = 1.9,
 ) -> plt.Figure:
-    """Compare two spatial comfort cases. WARNING: This can use A LOT of memory as it must load two sets of results to compare them.
+    """Compare two spatial comfort cases. WARNING: This can use A LOT of memory
+    as it must load two sets of results to compare them.
 
     Args:
         spatial_result_1 (SpatialComfort):
@@ -185,7 +191,10 @@ def compare_mrt(
     """
 
     CONSOLE_LOGGER.info(
-        f"[SpatialComfort - Comparison] - Plotting MRT difference between {spatial_result_1} and {spatial_result_2} for {describe_analysis_period(analysis_period)}"
+        (
+            f"[SpatialComfort - Comparison] - Plotting MRT difference between {spatial_result_1} and "
+            f"{spatial_result_2} for {describe_analysis_period(analysis_period)}"
+        )
     )
     # get point indices
     indices = get_common_pt_indices(spatial_result_1, spatial_result_2)
@@ -246,7 +255,10 @@ def compare_mrt(
 
     # add title
     ax.set_title(
-        f"{describe_analysis_period(analysis_period)}\n{metric.description()} - average difference\n{spatial_result_1} > {spatial_result_2}",
+        (
+            f"{describe_analysis_period(analysis_period)}\n{metric.description()} - "
+            f"average difference\n{spatial_result_1} > {spatial_result_2}"
+        ),
         ha="left",
         va="bottom",
         x=0,
@@ -263,7 +275,8 @@ def compare_sun_hours(
     analysis_period: AnalysisPeriod = AnalysisPeriod(),
     alpha: float = 1.9,
 ) -> plt.Figure:
-    """Compare two spatial comfort cases. WARNING: This can use A LOT of memory as it must load two sets of results to compare them.
+    """Compare two spatial comfort cases. WARNING: This can use A LOT of memory
+    as it must load two sets of results to compare them.
 
     Args:
         spatial_result_1 (SpatialComfort):
@@ -281,7 +294,10 @@ def compare_sun_hours(
     """
 
     CONSOLE_LOGGER.info(
-        f"[SpatialComfort - Comparison] - Plotting Sun-Hours difference between {spatial_result_1} and {spatial_result_2} for {describe_analysis_period(analysis_period)}"
+        (
+            f"[SpatialComfort - Comparison] - Plotting Sun-Hours difference between {spatial_result_1} and "
+            f"{spatial_result_2} for {describe_analysis_period(analysis_period)}"
+        )
     )
     # get point indices
     indices = get_common_pt_indices(spatial_result_1, spatial_result_2)
@@ -331,7 +347,10 @@ def compare_sun_hours(
 
     # add title
     ax.set_title(
-        f"{describe_analysis_period(analysis_period)}\n{metric.description()} - average difference\n{spatial_result_1} > {spatial_result_2}",
+        (
+            f"{describe_analysis_period(analysis_period)}\n{metric.description()} - "
+            f"average difference\n{spatial_result_1} > {spatial_result_2}"
+        ),
         ha="left",
         va="bottom",
         x=0,
@@ -349,7 +368,8 @@ def compare_ws(
     analysis_period: AnalysisPeriod = None,
     alpha: float = 1.9,
 ) -> plt.Figure:
-    """Compare two spatial comfort cases. WARNING: This can use A LOT of memory as it must load two sets of results to compare them.
+    """Compare two spatial comfort cases. WARNING: This can use A LOT of memory
+    as it must load two sets of results to compare them.
 
     Args:
         spatial_result_1 (SpatialComfort):
@@ -377,7 +397,10 @@ def compare_ws(
         raise ValueError("This type of plot is not possible for the requested metric.")
 
     CONSOLE_LOGGER.info(
-        f"[SpatialComfort - Comparison] - Plotting WS difference between {spatial_result_1} and {spatial_result_2} for {describe_analysis_period(analysis_period)}"
+        (
+            f"[SpatialComfort - Comparison] - Plotting WS difference between {spatial_result_1} "
+            f"and {spatial_result_2} for {describe_analysis_period(analysis_period)}"
+        )
     )
     # get point indices
     indices = get_common_pt_indices(spatial_result_1, spatial_result_2)
@@ -436,7 +459,10 @@ def compare_ws(
 
     # add title
     ax.set_title(
-        f"{describe_analysis_period(analysis_period)}\n{metric.description()} - average difference\n{spatial_result_1} > {spatial_result_2}",
+        (
+            f"{describe_analysis_period(analysis_period)}\n{metric.description()} - "
+            f"average difference\n{spatial_result_1} > {spatial_result_2}"
+        ),
         ha="left",
         va="bottom",
         x=0,
@@ -452,7 +478,7 @@ def compare_distance_to_comfortable(
     spatial_result_2: SpatialComfort,
     metric: SpatialMetric,
     analysis_period: AnalysisPeriod = None,
-    comfort_limits: Tuple[float] = (9, 26),
+    comfort_limits: tuple[float] = (9, 26),
     alpha: float = 1.9,
 ) -> plt.Figure:
     """For a given metric, plot the difference between two SpatialComfort objects.
@@ -549,7 +575,10 @@ def compare_distance_to_comfortable(
 
     # add title
     ax.set_title(
-        f"{describe_analysis_period(analysis_period)}\n{metric.description()} - average difference\n{spatial_result_1} > {spatial_result_2}",
+        (
+            f"{describe_analysis_period(analysis_period)}\n{metric.description()} - "
+            f"average difference\n{spatial_result_1} > {spatial_result_2}"
+        ),
         ha="left",
         va="bottom",
         x=0,
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/metric.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/metric.py
index 67848163..63239d52 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/metric.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/metric.py
@@ -1,6 +1,10 @@
+"""Methods for handling SpatialMetric results for spatial comfort assessments."""
+# pylint: disable=E0401
 from enum import Enum, auto
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
+
+# pylint: enable=E0401
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -48,7 +52,7 @@ def filepath(self, simulation_directory: Path) -> Path:
         """
         return simulation_directory / f"{self.name.lower()}.parquet"
 
-    def tricontourf_kwargs(self) -> Dict[str, Any]:
+    def tricontourf_kwargs(self) -> dict[str, Any]:
         """kwargs for tricontourf plot formatting of the given SpatialMetric."""
         cases = {
             SpatialMetric.DBT_EPW.value: {
@@ -109,7 +113,7 @@ def tricontourf_kwargs(self) -> Dict[str, Any]:
         except KeyError as exc:
             raise KeyError(f"tricontourf_kwargs not defined for {self}.") from exc
 
-    def tricontour_kwargs(self) -> Dict[str, Any]:
+    def tricontour_kwargs(self) -> dict[str, Any]:
         """kwargs for tricontour plot formatting of the given SpatialMetric."""
         cases = {
             SpatialMetric.DBT_EPW.value: {
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/moisture.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/moisture.py
index 3ae58064..01d52d6e 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/moisture.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/moisture.py
@@ -1,518 +1,319 @@
-from __future__ import annotations
+"""Methods for handling moisture."""
 
+# pylint: disable=E0401
 import json
 from pathlib import Path
-from typing import Any, Dict, List, Tuple
+from typing import Any
+from dataclasses import dataclass
+
+# pylint: enable=E0401
 
 import numpy as np
 import pandas as pd
-from ladybug.epw import EPW
-from scipy.spatial.distance import cdist
+from ladybug.epw import EPW, AnalysisPeriod
+
+from ladybug_geometry.geometry2d.pointvector import Point2D, Vector2D
+from tqdm import tqdm
 
-from ...bhomutil.analytics import CONSOLE_LOGGER
-from ...helpers import angle_from_north, proximity_decay
-from ...ladybug_extension.datacollection import collection_to_series
-from ...ladybug_extension.epw import unique_wind_speed_direction
+from ...helpers import (
+    DecayMethod,
+    angle_to_vector,
+    convert_keys_to_snake_case,
+    proximity_decay,
+)
+from ...ladybug_extension.datacollection import (
+    analysis_period_to_datetimes,
+)
+from ...ladybug_geometry_extension import pt_distances
 
 
+@dataclass(init=True, eq=True, repr=True)
 class MoistureSource:
-    # TODO - convert this object into a BHoMObject  # pylint: disable=fixme
-    """An object defining where moisture is present in a spatial thermal comfort simulation
+    """An object defining where moisture is present in a spatial thermal comfort simulation.
 
     Args:
         identifier (str):
             The ID of this moisture source.
         magnitude (float):
             The evaporative cooling effectiveness of this moisture source.
-        point_indices (List[int]):
+        point_indices (list[int]):
             The point indices (related to a list of points) where this moisture source is applied.
-        decay_function: (str):
+        decay_method: (str):
             The method with which moisture effects will "drop off" at distance from the emitter.
-        schedule (List[int]):
+        schedule (list[int]):
             A list of hours in the year where the moisture emitter will be active. If empty, then
             the moisture source will not be active for all hours of the year.
     """
 
-    def __init__(
-        self,
-        identifier: str,
-        magnitude: float,
-        point_indices: List[int],
-        decay_function: str,
-        schedule: List[int],
-    ) -> MoistureSource:
-        self.identifier = identifier
-        self.magnitude = magnitude
-        self.point_indices = np.array(point_indices)
-        self.decay_function = decay_function
-        if len(schedule) == 0:
-            schedule = np.arange(0, 8760, 1)
-        self.schedule = np.array(schedule)
+    identifier: str
+    evaporative_cooling_effect: list[float]
+    decay_method: DecayMethod = DecayMethod.LINEAR
+    max_decay_distance: float = 5
+    plume_angle: float = 25
 
-    @classmethod
-    def from_json(cls, json_file: Path) -> List[MoistureSource]:
-        """Create a set of MoistureSource objects from a json file.
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({self.identifier})"
 
-        Args:
-            json_file (Path): A file containing MoistureSource objects in json format.
+    def __post_init__(self):
+        """_"""
+        if isinstance(self.evaporative_cooling_effect, np.ndarray):
+            self.evaporative_cooling_effect = self.evaporative_cooling_effect.tolist()
 
-        Returns:
-            List[MoistureSource]: A list of MoistureSource objects.
-        """
+        if len(self.evaporative_cooling_effect) != 8760:
+            raise ValueError(
+                "evaporative_cooling_effect must be a list of 8760 values."
+            )
+        if any(
+            not isinstance(i, (float, int)) for i in self.evaporative_cooling_effect
+        ):
+            raise ValueError(
+                "evaporative_cooling_effect must be a list of numeric values."
+            )
 
-        with open(json_file, "r", encoding="utf-8") as fp:
-            moisture_sources_dict = json.load(fp)
+        if any(i < 0 for i in self.evaporative_cooling_effect):
+            raise ValueError(
+                "evaporative_cooling_effect must be a list of positive values."
+            )
+        if any(i > 1 for i in self.evaporative_cooling_effect):
+            raise ValueError(
+                "evaporative_cooling_effect must be a list of values less than or equal to 1."
+            )
 
-        objs = []
-        for moisture_source_dict in moisture_sources_dict:
-            objs.append(cls.from_dict(moisture_source_dict))
+        self.decay_method  # pylint: disable=W0104
+        # for some reason, if this is not called, the check below fails
 
-        return objs
+        if not isinstance(self.decay_method, DecayMethod):
+            raise ValueError("decay_method must be a DecayMethod enum value.")
 
-    @classmethod
-    def from_dict(cls, dictionary: Dict[str, Any]) -> MoistureSource:
-        """Create a MoistureSource object from a dict.
-
-        Args:
-            dictionary (Dict[str, Any]): _description_
+        if not isinstance(self.max_decay_distance, (float, int)):
+            raise ValueError("max_decay_distance must be a numeric value.")
+        if self.max_decay_distance <= 0:
+            raise ValueError(
+                "max_decay_distance must be a positive value greater than 0."
+            )
 
-        Returns:
-            MoistureSource: _description_
-        """
-        return cls(
-            identifier=dictionary["id"],
-            magnitude=dictionary["magnitude"],
-            point_indices=dictionary["point_indices"],
-            decay_function=dictionary["decay_function"],
-            schedule=dictionary["schedule"],
-        )
+    def to_dict(self) -> dict[str, Any]:
+        """Convert this object to a dictionary."""
+        d = {
+            "_t": "BH.oM.LadybugTools.MoistureSource",
+            "Identifier": self.identifier,
+            "DecayMethod": str(self.decay_method.name),
+            "EvaporativeCoolingEffect": self.evaporative_cooling_effect,
+            "MaxDecayDistance": self.max_decay_distance,
+        }
 
-    def __repr__(self) -> str:
-        return f"MoistureSource(id={self.identifier})"
+        return d
 
-    def to_dict(self) -> Dict[str, Any]:
-        """Return this object as a dictionary
+    @classmethod
+    def from_dict(cls, d: dict[str, Any]) -> "MoistureSource":
+        """Create this object from a dictionary."""
 
-        Returns:
-            Dict: The dict representation of this object.
-        """
+        d = convert_keys_to_snake_case(d)
 
-        return {
-            "id": self.identifier,
-            "magnitude": self.magnitude,
-            "point_indices": self.point_indices,
-            "decay_function": self.decay_function,
-            "schedule": self.schedule,
-        }
+        if isinstance(d["decay_method"], str):
+            d["decay_method"] = DecayMethod[d["decay_method"]]
 
-    def moisture_source_points(
-        self, spatial_points: List[List[float]]
-    ) -> List[List[float]]:
-        """Return a subset of a list, containing the point X,Y locations of moisture sources."""
-        return np.array(spatial_points[self.point_indices])
+        return cls(
+            identifier=d["identifier"],
+            evaporative_cooling_effect=d["evaporative_cooling_effect"],
+            decay_method=d["decay_method"],
+            max_decay_distance=d["max_decay_distance"],
+        )
 
-    def point_distances(self, spatial_points: List[List[float]]) -> List[List[float]]:
-        """Return the distance from each moisture_source pt to each other point in the input list"""
+    def to_json(self) -> str:
+        """Create a JSON string from this object."""
+        return json.dumps(self.to_dict())
 
-        # get the domain and source points
-        spatial_points = np.array(spatial_points)
-        emitter_points = self.moisture_source_points(spatial_points)
+    @classmethod
+    def from_json(cls, json_string: str) -> "MoistureSource":
+        """Create this object from a JSON string."""
 
-        # for each emitter, get the distance to all "receiving" points
-        distances = cdist(emitter_points, spatial_points)
+        return cls.from_dict(json.loads(json_string))
 
-        return distances
+    def to_file(self, path: Path) -> Path:
+        """Write this object to a JSON file."""
 
-    def point_angles(self, spatial_points: List[List[float]]) -> List[List[float]]:
-        """Return the relative north angle from each moisture_source pt to each other point in the
-        input list"""
+        if Path(path).suffix != ".json":
+            raise ValueError("path must be a JSON file.")
 
-        # get the domain and source points
-        spatial_points = np.array(spatial_points)
-        emitter_points = self.moisture_source_points(spatial_points)
+        with open(Path(path), "w") as fp:
+            fp.write(self.to_json())
 
-        # calculate the vector between each point
-        vectors = emitter_points[:, np.newaxis] - spatial_points
+        return Path(path)
 
-        # calculate angle to north for each vector
-        angles = np.array([angle_from_north(i.T).T for i in vectors])
+    @classmethod
+    def from_file(cls, path: Path) -> "MoistureSource":
+        """Create this object from a JSON file."""
 
-        return angles
+        with open(Path(path), "r") as fp:
+            return cls.from_json(fp.read())
 
-    def plume(
+    def spatial_evaporative_cooling_effect(
         self,
-        point_distances: List[List[float]],
-        point_angles: List[List[float]],
-        wind_speed: float,
-        wind_direction: float,
-        plume_width: float = 25,
-    ) -> List[float]:
-        """Calculate the spatial moisture plume from a single location to other locations under
-            given wind speed and direction.
+        epw: EPW,
+        source: Point2D | list[float],
+        points: list[Point2D] | list[list[float]],
+    ) -> pd.DataFrame:
+        """Return a value per points_xy, giving the effective
+        evaporative cooling amount per point from the source point.
 
         Args:
-            point_distances (List[List[float]]): An array of distances to other points.
-            point_angles (List[List[float]]): An array of angles to other points.
-            wind_speed (float): A value describing current wind speed.
-            wind_direction (float): A value describing current wind direction
-            plume_width (float, optional): The spread of the plume to be generated. Defaults to 25.
+            epw (EPW):
+                An EPW object.
+            source (Point2D | list[float]):
+                The source point. Can be a Point2D or an [X, Y] coordinate list.
+            points (list[Point2D]):
+                A list of points to check for downwindedness.
+                Can be a list of Point2D or [[X, Y], ...] coordinate lists.
 
         Returns:
-            List[float]: _description_
+            pd.DataFrame:
+                A dataframe containing the evaporative cooling effect for each point,
+                for each hour of the year
         """
 
-        # get downwind angles
-        if (wind_direction > 360 - (plume_width / 2)) or (
-            wind_direction < (0 + (plume_width / 2))
-        ):
-            downwind = (point_angles > 360 - (plume_width / 2)) | (
-                point_angles < (0 + (plume_width / 2))
-            )
-        else:
-            downwind = (point_angles > wind_direction - (plume_width / 2)) & (
-                point_angles < wind_direction + (plume_width / 2)
-            )
+        idx = analysis_period_to_datetimes(AnalysisPeriod())
 
-        # calculate proximity decayed values
-        mag_dist_values = proximity_decay(
-            self.magnitude, point_distances, 2 * wind_speed, self.decay_function
+        distances = pt_distances(source, points)
+        downwinds = is_point_downwind(
+            epw=epw, source=source, points=points, plume_angle=self.plume_angle
         )
-
-        # get overall magnitude based on down-winded-ness
-        return np.where(downwind, mag_dist_values, 0).max(axis=0)
-
-    def spatial_moisture(
-        self,
-        spatial_points: List[List[float]],
-        epw: EPW,
-        plume_width: float = 25,
-        simulation_directory: Path = None,
-    ) -> Dict[Tuple(float), List[List[float]]]:
-        """Calculate the spatial moisture thingy."""
-        if simulation_directory is not None:
-            output_dir = simulation_directory / "moisture"
-            if (output_dir / f"{self.pathsafe_id}_matrix.parquet").exists():
-                CONSOLE_LOGGER.info(
-                    f"[{simulation_directory.stem}] - Loading moisture effectiveness data for {self.identifier}"
-                )
-                moisture_df = pd.read_parquet(
-                    output_dir / f"{self.pathsafe_id}_matrix.parquet"
-                )
-                moisture_df.columns = moisture_df.columns.astype(int)
-                return moisture_df
-
-        # get point distances
-        pt_distances = self.point_distances(spatial_points)
-
-        # get point angles
-        pt_angles = self.point_angles(spatial_points)
-
-        # get unique ws/wd
-        unique_ws_wd = unique_wind_speed_direction(epw, self.schedule)
-
-        # construct a dict ready for results
-        d = {}
-        for ws, wd in unique_ws_wd:
-            d[(ws, wd)] = self.plume(pt_distances, pt_angles, ws, wd, plume_width)
-
-        # for each time period in the year construct the resultant moisture matrix
-        moisture_matrix = []
-        for n, (ws, wd) in enumerate(list(zip(*[epw.wind_speed, epw.wind_direction]))):
-            CONSOLE_LOGGER.info(
-                f"[{simulation_directory.stem}] - Calculating moisture effectiveness data for {self.identifier} ({n/8760:03.2%})",
-                end="\r",
-            )
-            if n not in self.schedule:
-                moisture_matrix.append(np.zeros_like(pt_distances[0]))
-            else:
-                moisture_matrix.append(d[(ws, wd)])
-
-        moisture_df = pd.DataFrame(
-            moisture_matrix, index=collection_to_series(epw.dry_bulb_temperature).index
+        ws_adj = np.interp(
+            epw.wind_speed, [epw.wind_speed.min, epw.wind_speed.max], [0, 1]
         )
 
-        if simulation_directory is not None:
-            output_dir = Path(simulation_directory) / "moisture"
-            moisture_df.columns = moisture_df.columns.astype(str)
-            moisture_df.to_parquet(output_dir / f"{self.pathsafe_id}_matrix.parquet")
-
-        return moisture_df
-
-    @property
-    def pathsafe_id(self) -> str:
-        """Create a path-safe ID for this object"""
-        return "".join(
-            [c for c in self.identifier if c.isalpha() or c.isdigit() or c == " "]
-        ).rstrip()
+        all_vals = []
+        pbar = tqdm(list(enumerate(zip(epw.wind_direction, ws_adj))))
+        for n, (angle, speed) in pbar:
+            pbar.set_description(f"Processing {self} {idx[n]:%b %d %H:%M}")
+            dist = np.where(downwinds[angle], distances, np.nan)
+            all_vals.append(
+                proximity_decay(
+                    value=self.evaporative_cooling_effect[n],
+                    decay_method=self.decay_method,
+                    distance_to_value=dist,
+                    max_distance=self.max_decay_distance * speed,
+                )[0]
+            )
+        return pd.DataFrame(all_vals, index=idx).fillna(0)
 
 
-def moisture_directory(simulation_directory: Path) -> Path:
-    """Get the moisture directory for a spatial simulation.
+def is_point_downwind(
+    epw: EPW,
+    source: Point2D | list[float],
+    points: list[Point2D] | list[list[float]],
+    plume_angle: float = 25,
+) -> dict[float, list[bool]]:
+    """Create a dictionary containing a list of booleans for each unique wind direction, defining whether each point
+    is downwind of the source point.
 
     Args:
-        simulation_directory (Path):
-            The associated simulation directory
+        epw (EPW):
+            An EPW object.
+        source (Point2D | list[float]):
+            The source point. Can be a Point2D or an [X, Y] coordinate list.
+        points (list[Point2D]):
+            A list of points to check for downwindedness. Can be a list of Point2D or [[X, Y], ...] coordinate lists.
+        plume_angle (float, optional):
+            The angle of the plume defining the spread of moisture. Defaults to 25 degrees.
 
     Returns:
-        Path:
-            The path to the moisture directory
+        dict[float, list[bool]]:
+            A dictionary of wind direction keys and a list of booleans defining whether each point is downwind of the
+            source point.
     """
 
-    if not (simulation_directory / "moisture").exists():
-        raise FileNotFoundError(
-            f'No "moisture" directory exists in {simulation_directory}. For this method to work, '
-            + 'you need a moisture directory containing a JSON file named "moisture_sources.json". The file contains a list of '
-            + 'MoistureSource objects in the format [{"id": "name_of_moisture_source", '
-            + '"magnitude": 0.3, "point_indices": [0, 1, 2], "decay_function": "linear", '
-            + '"schedule": []}]'
-        )
+    if isinstance(source, list):
+        if all(isinstance(i, (int, float)) for i in source):
+            source = Point2D(*source)
+        else:
+            raise ValueError("source must be a Point2D or [X, Y] coordinate list.")
+
+    if isinstance(points, list):
+        if all(isinstance(i, Point2D) for i in points):
+            pass
+        elif all(isinstance(i, list) for i in points):
+            if all(isinstance(i, (int, float)) for i in points[0]):
+                points = [Point2D(*i) for i in points]
+        else:
+            raise ValueError(
+                "points must be a list of Point2D or [[X, Y], ...] coordinate lists."
+            )
 
-    return simulation_directory / "moisture"
+    if (plume_angle <= 0) or (plume_angle > 180):
+        raise ValueError("plume_angle must be a value between 0 and 180.")
 
+    angles = np.unique(epw.wind_direction)
+    vectors = [-Vector2D(*i) for i in np.array(angle_to_vector(angles)).T]
+    pts_vectors = [Vector2D(*i) for i in (np.array(source) - np.array(points))]
 
-def load_moisture_sources(simulation_directory: Path) -> List[MoistureSource]:
-    """Get/create the moisture directory for a spatial simulation.
+    in_plume = {}
+    pbar = tqdm(list(zip(angles, vectors)))
+    for angle, vector in pbar:
+        pbar.set_description(
+            f"Calculating downwindedness for {angle}° wind from {source}"
+        )
+        temp = []
+        for pts_vector in pts_vectors:
+            try:
+                temp.append(vector.angle(pts_vector) < np.radians(plume_angle / 2))
+            except ZeroDivisionError:
+                temp.append(True)
+        in_plume[angle] = temp
+
+    return in_plume
+
+    # def spatial_evaporative_cooling_effect(self, spatial_points: list[list[float]]) -> list[float]:
+    #     """Return a subset of a list, containing the point X,Y locations of moisture sources."""
+    #     return np.array(self.evaporative_cooling_effect)
+
+
+def spatial_evaporative_cooling_effect(
+    moisture_sources: list[MoistureSource],
+    epw: EPW,
+    sources: list[Point2D] | list[list[float]],
+    points: list[Point2D] | list[list[float]],
+    save_path: Path = None,
+) -> pd.DataFrame:
+    """Return a value per points, giving the effective
+    evaporative cooling amount per point from the source point.
 
     Args:
-        simulation_directory (Path):
-            The associated simulation directory.
+        moisture_sources (list[MoistureSource]):
+            A list of moisture source objects.
+        epw (EPW):
+            An EPW object.
+        sources (list[Point2D] | list[list[float]]):
+            The source points. Can be a list of Point2D or [[X, Y], ...] coordinate lists.
+        points (list[Point2D] | list[list[float]]):
+            A list of points to check for downwindedness. Can be a list of Point2D or [[X, Y], ...] coordinate lists.
+        save_path (Path, optional):
+            A path to save the results to. Defaults to None.
 
     Returns:
-        List[MoistureSource]:
-            A list of moisture sources in the simulation directory.
+        pd.DataFrame:
+            A dataframe containing the evaporative cooling effect for each point, for each hour of the year
     """
-
-    return MoistureSource.from_json(
-        moisture_directory(simulation_directory) / "moisture_sources.json"
+    if len(moisture_sources) != len(sources):
+        raise ValueError(
+            "The number of moisture sources must match the number of source points."
+        )
+    if save_path is not None:
+        if save_path.suffix != ".parquet":
+            raise ValueError("save_path must be a parquet file.")
+        if save_path.exists():
+            return pd.read_parquet(save_path)
+
+    df = (
+        sum(
+            ms.spatial_evaporative_cooling_effect(epw=epw, source=src, points=points)
+            for src, ms in zip(sources, moisture_sources)
+        )
+        .clip(upper=0.99)
+        .astype(np.float32)
     )
 
+    if save_path is not None:
+        df.to_parquet(save_path, compression="gzip")
 
-EXAMPLE_MOISTURE_CONFIG = """
-[
-    {
-        "id": "MISTING",
-        "magnitude": 0.37,
-        "point_indices": [0, 100, 200, 300],
-        "decay_function": "sigmoid",
-        "schedule": [
-            3634,
-            3635,
-            3636,
-            3637,
-            3638,
-            3639,
-            3640,
-            3658,
-            3659,
-            3660,
-            3661,
-            3662,
-            3663,
-            3664,
-            3682,
-            3683,
-            3684,
-            3685,
-            3686,
-            3687,
-            3688,
-            3706,
-            3707,
-            3708,
-            3709,
-            3710,
-            3711,
-            3712,
-            3730,
-            3731,
-            3732,
-            3733,
-            3734,
-            3735,
-            3736,
-            3754,
-            3755,
-            3756,
-            3757,
-            3758,
-            3759,
-            3760,
-            3778,
-            3779,
-            3780,
-            3781,
-            3782,
-            3783,
-            3784,
-            3802,
-            3803,
-            3804,
-            3805,
-            3806,
-            3807,
-            3808,
-            3826,
-            3827,
-            3828,
-            3829,
-            3830,
-            3831,
-            3832,
-            3850,
-            3851,
-            3852,
-            3853,
-            3854,
-            3855,
-            3856,
-            3874,
-            3875,
-            3876,
-            3877,
-            3878,
-            3879,
-            3880,
-            3898,
-            3899,
-            3900,
-            3901,
-            3902,
-            3903,
-            3904,
-            3922,
-            3923,
-            3924,
-            3925,
-            3926,
-            3927,
-            3928,
-            3946,
-            3947,
-            3948,
-            3949,
-            3950,
-            3951,
-            3952,
-            3970,
-            3971,
-            3972,
-            3973,
-            3974,
-            3975,
-            3976,
-            3994,
-            3995,
-            3996,
-            3997,
-            3998,
-            3999,
-            4000,
-            4018,
-            4019,
-            4020,
-            4021,
-            4022,
-            4023,
-            4024,
-            4042,
-            4043,
-            4044,
-            4045,
-            4046,
-            4047,
-            4048,
-            4066,
-            4067,
-            4068,
-            4069,
-            4070,
-            4071,
-            4072,
-            4090,
-            4091,
-            4092,
-            4093,
-            4094,
-            4095,
-            4096,
-            4114,
-            4115,
-            4116,
-            4117,
-            4118,
-            4119,
-            4120,
-            4138,
-            4139,
-            4140,
-            4141,
-            4142,
-            4143,
-            4144,
-            4162,
-            4163,
-            4164,
-            4165,
-            4166,
-            4167,
-            4168,
-            4186,
-            4187,
-            4188,
-            4189,
-            4190,
-            4191,
-            4192,
-            4210,
-            4211,
-            4212,
-            4213,
-            4214,
-            4215,
-            4216,
-            4234,
-            4235,
-            4236,
-            4237,
-            4238,
-            4239,
-            4240,
-            4258,
-            4259,
-            4260,
-            4261,
-            4262,
-            4263,
-            4264,
-            4282,
-            4283,
-            4284,
-            4285,
-            4286,
-            4287,
-            4288,
-            4306,
-            4307,
-            4308,
-            4309,
-            4310,
-            4311,
-            4312,
-            4330,
-            4331,
-            4332,
-            4333,
-            4334,
-            4335,
-            4336
-        ],
-    },
-    {
-        "id": "POND",
-        "magnitude": 0.2,
-        "point_indices": [50, 150, 250, 350],
-        "decay_function": "linear",
-        "schedule": [],
-    }
-]
-"""
+    return df
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/point_mitigation.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/point_mitigation.py
index 29de9c0b..d77cc61e 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/point_mitigation.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/point_mitigation.py
@@ -1,28 +1,28 @@
-from __future__ import annotations
-
-from copy import copy
-from typing import Union
+"""Methods for modifying single points in-situ."""
+from copy import copy  # pylint: disable=E0401
 
 import pandas as pd
 from ladybug.datacollection import HourlyContinuousCollection
 
 from ...ladybug_extension.datacollection import collection_from_series
-from ..external_comfort import ExternalComfort, SimulationResult, Typology
+from ..externalcomfort import ExternalComfort, Typology
+from ..simulate import SimulationResult
 
 
 class PointMitigation:
-    """Adjust point-location-based UTCI using adjustments to the composite DBT, MRT, RH and WS values in-situ.
+    """Adjust point-location-based UTCI using adjustments to the composite
+    DBT, MRT, RH and WS values in-situ.
 
     Args:
         simulation_result (SimulationResult):
             The simulation result to get point-values from.
-        point_dbt (Union[pd.Series, HourlyContinuousCollection]):
+        point_dbt (pd.Series | HourlyContinuousCollection):
             The DBT values to set for the theoretical point.
-        point_mrt (Union[pd.Series, HourlyContinuousCollection]):
+        point_mrt (pd.Series | HourlyContinuousCollection):
             The MRT values to set for the theoretical point.
-        point_rh (Union[pd.Series, HourlyContinuousCollection]):
+        point_rh (pd.Series | HourlyContinuousCollection):
             The RH values to set for the theoretical point.
-        point_ws (Union[pd.Series, HourlyContinuousCollection]):
+        point_ws (pd.Series | HourlyContinuousCollection):
             The WS values to set for the theoretical point.
 
     Returns:
@@ -33,11 +33,11 @@ class PointMitigation:
     def __init__(
         self,
         simulation_result: SimulationResult,
-        point_dbt: Union[pd.Series, HourlyContinuousCollection],
-        point_mrt: Union[pd.Series, HourlyContinuousCollection],
-        point_rh: Union[pd.Series, HourlyContinuousCollection],
-        point_ws: Union[pd.Series, HourlyContinuousCollection],
-    ) -> PointMitigation:
+        point_dbt: pd.Series | HourlyContinuousCollection,
+        point_mrt: pd.Series | HourlyContinuousCollection,
+        point_rh: pd.Series | HourlyContinuousCollection,
+        point_ws: pd.Series | HourlyContinuousCollection,
+    ) -> "PointMitigation":
         self._point_dbt = (
             point_dbt
             if isinstance(point_dbt, HourlyContinuousCollection)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/sky_view_pov.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/sky_view_pov.py
index aae7281e..b7b20b90 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/sky_view_pov.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/sky_view_pov.py
@@ -1,4 +1,4 @@
-from typing import Union
+"""Methods for visualising sky-view at a given location."""
 
 from honeybee.model import Model
 from ladybug.datacollection import HourlyContinuousCollection
@@ -8,9 +8,10 @@
 from matplotlib.colors import BoundaryNorm, Colormap
 from PIL import Image, ImageDraw, ImageFont, ImageOps
 
-from ...helpers import figure_to_image
-from ...plot import skymatrix, sp
+from ...plot._skymatrix import skymatrix
+from ...plot._sunpath import sunpath as sp
 from ...plot.fisheye_sky import fisheye_sky
+from ...plot.utilities import figure_to_image
 
 
 def sky_view_pov(
@@ -18,7 +19,7 @@ def sky_view_pov(
     sensor: Point3D,
     epw: EPW,
     analysis_period: AnalysisPeriod = AnalysisPeriod(),
-    cmap: Union[Colormap, str] = "viridis",
+    cmap: Colormap | str = "viridis",
     norm: BoundaryNorm = None,
     data_collection: HourlyContinuousCollection = None,
     density: int = 1,
@@ -69,12 +70,11 @@ def sky_view_pov(
         sunpath_img = ImageOps.mirror(
             figure_to_image(
                 sp(
-                    epw=epw,
+                    location=epw.location,
                     analysis_period=analysis_period,
                     data_collection=data_collection,
                     cmap=cmap,
                     norm=norm,
-                    show_title=False,
                     show_grid=False,
                     show_legend=False,
                 )
@@ -120,7 +120,7 @@ def sky_view_pov(
                 (255, 255, 255),
                 font=font,
             )
-        except Exception:
+        except Exception:  # pylint: disable=broad-except
             draw.text(
                 (5, 5),
                 title,
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/spatial_comfort.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/spatial_comfort.py
index 4c31bfd2..1e9ab7c3 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/spatial_comfort.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/spatial/spatial_comfort.py
@@ -1,33 +1,31 @@
-# pylint: disable=broad-exception-caught
-from __future__ import annotations
-
+"""Methods for handling SpatialMetric results for spatial comfort assessments."""
+# pylint: disable=broad-exception-caught,W0212
+# pylint: disable=line-too-long
+# pylint: disable=E0401
 import calendar
 import contextlib
 import copy
 import io
-import shutil
 import warnings
 from dataclasses import dataclass, field
 from datetime import datetime
 from pathlib import Path
-from typing import List, Tuple
+
+# pyling: enable=E0401
 
 import numpy as np
 import pandas as pd
 from honeybee.model import Model
 from ladybug.analysisperiod import AnalysisPeriod
 from matplotlib import pyplot as plt
-from matplotlib.colors import ListedColormap
 from matplotlib.dates import DateFormatter
 from matplotlib.ticker import PercentFormatter, StrMethodFormatter
+from matplotlib.colors import ListedColormap
 from mpl_toolkits.axes_grid1 import make_axes_locatable
-from PIL import Image
 
-from ...bhomutil.analytics import CONSOLE_LOGGER
-from ...bhomutil.bhom_object import BHoMObject
-from ...helpers import sanitise_string
+from ...helpers import sanitise_string, wind_speed_at_height
 from ...honeybee_extension.results import load_ill, load_pts, load_res, make_annual
-from ...ladybug_extension.analysis_period import (
+from ...ladybug_extension.analysisperiod import (
     analysis_period_to_boolean,
     analysis_period_to_datetimes,
     describe_analysis_period,
@@ -38,15 +36,16 @@
 )
 from ...ladybug_extension.epw import seasonality_from_month, sun_position_list
 from ...plot._utci import (
-    utci_distance_to_comfortable,
     utci_heatmap_difference,
     utci_heatmap_histogram,
 )
 from ...plot.utilities import create_triangulation
-from ..simulate import SimulationResult
-from ..simulate import direct_sun_hours as dsh
-from ..simulate import working_directory
-from ..utci.calculate import utci, utci_parallel
+from ...plot._sunpath import sunpath
+from .._simulatebase import SimulationResult
+from ...bhom.logging import CONSOLE_LOGGER
+
+# from ..simulate import direct_sun_hours as dsh
+from ..utci import utci, utci_parallel
 from .calculate import (
     rwdi_london_thermal_comfort_category,
     shaded_unshaded_interpolation,
@@ -56,7 +55,7 @@
 
 
 @dataclass(init=True, repr=True, eq=True)
-class SpatialComfort(BHoMObject):
+class SpatialComfort:
     """A SpatialComfort object, used to calculate spatial UTCI.
 
     Args:
@@ -72,13 +71,6 @@ class SpatialComfort(BHoMObject):
     spatial_simulation_directory: Path = field(init=True, repr=True, compare=False)
     simulation_result: SimulationResult = field(init=True, repr=True, compare=False)
 
-    _t: str = field(
-        init=False,
-        compare=True,
-        repr=False,
-        default="BH.oM.LadybugTools.SpatialComfort",
-    )
-
     _model: Model = field(init=False, compare=False, repr=False, default=None)
     _points: pd.DataFrame = field(init=False, compare=False, repr=False, default=None)
     _dry_bulb_temperature_epw: pd.DataFrame = field(
@@ -124,20 +116,17 @@ def __post_init__(self):
                 "The spatial_simulation_directory given cannot be processed to create a SpatialComfort object."
             )
 
-        if not self.simulation_result.is_run():
-            self.simulation_result = self.simulation_result.run()
-
         # calculate baseline UTCI for shaded/unshaded
         self._unshaded_utci = utci(
             self.simulation_result.epw.dry_bulb_temperature,
             self.simulation_result.epw.relative_humidity,
-            self.simulation_result.UnshadedMeanRadiantTemperature,
+            self.simulation_result.unshaded_mean_radiant_temperature,
             self.simulation_result.epw.wind_speed,
         )
         self._shaded_utci = utci(
             self.simulation_result.epw.dry_bulb_temperature,
             self.simulation_result.epw.relative_humidity,
-            self.simulation_result.ShadedMeanRadiantTemperature,
+            self.simulation_result.shaded_mean_radiant_temperature,
             self.simulation_result.epw.wind_speed,
         )
 
@@ -157,7 +146,7 @@ def __post_init__(self):
         # add default analysis periods
         f = io.StringIO()
         with contextlib.redirect_stdout(f):
-            self.analysis_periods: List[AnalysisPeriod] = []
+            self.analysis_periods: list[AnalysisPeriod] = []
             for month, day in [[3, 21], [6, 21], [9, 21], [12, 21]]:
                 for start_hour, end_hour in [[8, 20], [0, 23]]:
                     for timestep in [1, 12]:
@@ -214,10 +203,6 @@ def __post_init__(self):
                             ),
                         )
 
-        # wrap methods within this class
-        # commented out here to avoid BHoMObject running all methods upon instantiation!
-        # super().__post_init__()
-
     def __repr__(self) -> str:
         return f"{self.spatial_simulation_directory.name}"
 
@@ -499,8 +484,8 @@ def mean_radiant_temperature_interpolated(self) -> pd.DataFrame:
 
             CONSOLE_LOGGER.info(f"[{self}] - Generating {metric.description()}")
             df = shaded_unshaded_interpolation(
-                unshaded_value=self.simulation_result.UnshadedMeanRadiantTemperature.values,
-                shaded_value=self.simulation_result.ShadedMeanRadiantTemperature.values,
+                unshaded_value=self.simulation_result.unshaded_mean_radiant_temperature.values,
+                shaded_value=self.simulation_result.shaded_mean_radiant_temperature.values,
                 total_irradiance=self.irradiance_total.values,
                 sky_view=self.sky_view.squeeze().values,
                 sun_up=[
@@ -531,6 +516,9 @@ def wind_speed_cfd(self) -> pd.DataFrame:
             df = spatial_wind_speed(
                 self.spatial_simulation_directory, self.simulation_result.epw
             ).round(2)
+            df = wind_speed_at_height(
+                df, 1.5, 10
+            )  # TODO - this part assumes wind speed extracted from CFD results at 1.5m above ground, to translate to 10m above ground. This may not be correct in certain cases.
             df.columns = df.columns.astype(str)
             df.to_parquet(save_path)
             self._wind_speed_cfd = df
@@ -586,7 +574,7 @@ def universal_thermal_climate_index_calculated(self) -> pd.DataFrame:
 
             try:
                 ws = self.wind_speed_cfd
-            except Exception as exc:
+            except Exception as _:
                 try:
                     ws = self.wind_speed_epw
                 except Exception as exc_inner:
@@ -620,7 +608,8 @@ def points_xyz(self) -> np.ndarray:
 
     @property
     def coldest_day(self) -> datetime:
-        """The coldest day in the year associated with this case (average for all hours in day), within the winter period."""
+        """The coldest day in the year associated with this case (average for all hours in day),
+        within the winter period."""
         seasons = seasonality_from_month(self.simulation_result.epw)
         return (
             collection_to_series(self.simulation_result.epw.dry_bulb_temperature)
@@ -633,7 +622,8 @@ def coldest_day(self) -> datetime:
 
     @property
     def hottest_day(self) -> datetime:
-        """The hottest day in the year associated with this case (average for all hours in day), within the summer period."""
+        """The hottest day in the year associated with this case (average for all hours in day),
+        within the summer period."""
         seasons = seasonality_from_month(self.simulation_result.epw)
         return (
             collection_to_series(self.simulation_result.epw.dry_bulb_temperature)
@@ -644,7 +634,7 @@ def hottest_day(self) -> datetime:
             .to_pydatetime()
         )
 
-    def filter_by_point_indices(self, indices: List[int]) -> SpatialComfort:
+    def filter_by_point_indices(self, indices: list[int]) -> "SpatialComfort":
         """Filter this object by a list of indices."""
 
         new_obj = copy.copy(self)
@@ -702,8 +692,8 @@ def filter_by_point_indices(self, indices: List[int]) -> SpatialComfort:
 
     def calculate_comfortable_time_percentage(
         self,
-        analysis_periods: List[AnalysisPeriod] = [AnalysisPeriod()],
-        comfort_limits: Tuple[float] = (9, 26),
+        analysis_periods: tuple[AnalysisPeriod] = (AnalysisPeriod(),),
+        comfort_limits: tuple[float] = (9, 26),
     ) -> pd.Series:
         """Calculate the proportion of comfortable hours for each point in the Spatial case."""
         _bool = analysis_period_to_boolean(analysis_periods)
@@ -718,9 +708,10 @@ def calculate_comfortable_time_percentage(
     def calculate_comfortable_area_percentage(
         self,
         time_proportion_threshold: float,
-        analysis_periods: List[AnalysisPeriod] = [AnalysisPeriod()],
-        comfort_limits: Tuple[float] = (9, 26),
+        analysis_periods: tuple[AnalysisPeriod] = (AnalysisPeriod(),),
+        comfort_limits: tuple[float] = (9, 26),
     ) -> float:
+        """_"""
         if time_proportion_threshold >= 1 or time_proportion_threshold <= 0:
             raise ValueError("Time proportion threshold must be between 0 and 1.")
 
@@ -733,10 +724,12 @@ def calculate_comfortable_area_percentage(
 
     def calculate_annual_comfortable_hours(
         self,
-        analysis_period_groups: List[List[AnalysisPeriod]] = [[AnalysisPeriod()]],
-        comfort_limit_groups: List[Tuple[float]] = [(9, 26)],
+        analysis_period_groups: tuple[tuple[AnalysisPeriod]] = ((AnalysisPeriod(),),),
+        comfort_limit_groups: tuple[tuple[float]] = ((9, 26),),
     ) -> float:
-        # given a list of analysis periods and comfort limits, calculate the annual comfortable hours across all AP hours within teh comofrt limts per AP period, in the year
+        """_"""
+        # given a list of analysis periods and comfort limits, calculate the annual comfortable hours
+        # across all AP hours within teh comofrt limts per AP period, in the year
         if len(analysis_period_groups) != len(comfort_limit_groups):
             raise ValueError(
                 "The number of analysis period groups must match the number of comfort limit groups."
@@ -756,8 +749,8 @@ def calculate_annual_comfortable_hours(
     def london_comfort_category(
         self,
         metric: SpatialMetric,
-        comfort_limits: Tuple[float] = (0, 32),
-        hours: List[float] = range(8, 21, 1),
+        comfort_limits: tuple[float] = (0, 32),
+        hours: list[float] = range(8, 21, 1),
     ) -> pd.Series:
         """Calculate the London Thermal Comfort category for this Spatial case."""
         if metric.value == SpatialMetric.UTCI_INTERPOLATED.value:
@@ -772,40 +765,40 @@ def london_comfort_category(
             metric_values, comfort_limits, hours
         )
 
-    def direct_sun_hours(
-        self,
-        analysis_period: AnalysisPeriod,
-    ) -> pd.Series:
-        """Calculate a set of direct sun hours datasets.
-
-        Args:
-            analysis_period (AnalysisPeriod, optional):
-                An AnalysisPeriod, including timestep to simulate.
-
-        Returns:
-            pd.Series:
-                A series containing results.
-        """
-        # move results files into spatial directory once completed
-        working_dir = working_directory(self.model, True)
-        res_dir = working_dir / "sunlight_hours"
-        res_dir.mkdir(parents=True, exist_ok=True)
-        results_file: Path = (
-            res_dir
-            / f"{describe_analysis_period(analysis_period, save_path=True, include_timestep=True)}.res"
-        )
-        if results_file.exists():
-            return load_res(results_file).squeeze()
-
-        result = dsh(self.model, self.simulation_result.epw, analysis_period)
-
-        # make a directory in the current case to contain results files
-        target_dir = self.spatial_simulation_directory / "sunlight_hours"
-        target_dir.mkdir(parents=True, exist_ok=True)
-
-        shutil.copy2(results_file.as_posix(), target_dir.as_posix())
-
-        return result
+    # def direct_sun_hours(
+    #     self,
+    #     analysis_period: AnalysisPeriod,
+    # ) -> pd.Series:
+    #     """Calculate a set of direct sun hours datasets.
+
+    #     Args:
+    #         analysis_period (AnalysisPeriod, optional):
+    #             An AnalysisPeriod, including timestep to simulate.
+
+    #     Returns:
+    #         pd.Series:
+    #             A series containing results.
+    #     """
+    #     # move results files into spatial directory once completed
+    #     working_dir = simulation_directory(self.model)
+    #     res_dir = working_dir / "sunlight_hours"
+    #     res_dir.mkdir(parents=True, exist_ok=True)
+    #     results_file: Path = (
+    #         res_dir
+    #         / f"{describe_analysis_period(analysis_period, save_path=True, include_timestep=True)}.res"
+    #     )
+    #     if results_file.exists():
+    #         return load_res(results_file).squeeze()
+
+    #     result = dsh(self.model, self.simulation_result.epw, analysis_period)
+
+    #     # make a directory in the current case to contain results files
+    #     target_dir = self.spatial_simulation_directory / "sunlight_hours"
+    #     target_dir.mkdir(parents=True, exist_ok=True)
+
+    #     shutil.copy2(results_file.as_posix(), target_dir.as_posix())
+
+    #     return result
 
     def get_spatial_metric(self, metric: SpatialMetric) -> pd.DataFrame:
         """Return the dataframe associated with the given metric."""
@@ -846,7 +839,7 @@ def get_spatial_metric(self, metric: SpatialMetric) -> pd.DataFrame:
         raise ValueError(f"{metric} cannot be obtained!")
 
     def plot_typical_point_in_time(
-        self, metric: SpatialMetric, month: int, hour: int, levels: List[float] = None
+        self, metric: SpatialMetric, month: int, hour: int, levels: list[float] = None
     ) -> plt.Figure:
         """Create a typical point-in-time plot of the given metric."""
 
@@ -966,8 +959,8 @@ def plot_sky_view(self) -> plt.Figure:
     def plot_london_comfort_category(
         self,
         metric: SpatialMetric,
-        comfort_limits: Tuple[float] = (0, 32),
-        hours: List[float] = range(8, 21, 1),
+        comfort_limits: tuple[float] = (0, 32),
+        hours: list[float] = range(8, 21, 1),
     ) -> plt.Figure:
         """Return a plot showing the RWDI London thermal comfort category."""
 
@@ -1141,50 +1134,50 @@ def plot_mrt_average(self, analysis_period: AnalysisPeriod) -> plt.Figure:
 
         return fig
 
-    def plot_direct_sun_hours(self, analysis_period: AnalysisPeriod) -> plt.Figure:
-        """Plot the  direct-sun-hours for the given analysis period."""
-
-        metric = SpatialMetric.DIRECT_SUN_HOURS
-        CONSOLE_LOGGER.info(
-            f"[{self}] - Plotting {metric.description()} for {describe_analysis_period(analysis_period, include_timestep=True)}"
-        )
-        series = self.direct_sun_hours(analysis_period)
-        tcf_properties = metric.tricontourf_kwargs()
-        tc_properties = metric.tricontour_kwargs()
-
-        fig, ax = plt.subplots(1, 1, figsize=(8, 8))
-        ax.set_aspect("equal")
-        ax.axis("off")
-        ax.set_xlim([min(self.points.x.values), max(self.points.x.values)])
-        ax.set_ylim([min(self.points.y.values), max(self.points.y.values)])
-
-        # add contour-fill
-        tcf = ax.tricontourf(self._triangulation, series.values, **tcf_properties)
-
-        # plot colorbar
-        divider = make_axes_locatable(ax)
-        cax = divider.append_axes("right", size="5%", pad=0.1, aspect=20)
-        cbar = plt.colorbar(tcf, cax=cax)
-        cbar.outline.set_visible(False)
-        cbar.set_label(metric.description())
-
-        # add contour lines if present
-        if len(tc_properties["levels"]) != 0:
-            tcs = ax.tricontour(self._triangulation, series.values, **tc_properties)
-            ax.clabel(tcs, inline=1, fontsize="x-small", colors=["k"])
-            cbar.add_lines(tcs)
-
-        # add title
-        ax.set_title(
-            f"{describe_analysis_period(analysis_period, include_timestep=False)}\n{metric.description()}\nAverage: {np.mean(series.values):0.1f} hours",
-            ha="left",
-            va="bottom",
-            x=0,
-        )
-
-        plt.tight_layout()
-
-        return fig
+    # def plot_direct_sun_hours(self, analysis_period: AnalysisPeriod) -> plt.Figure:
+    #     """Plot the  direct-sun-hours for the given analysis period."""
+
+    #     metric = SpatialMetric.DIRECT_SUN_HOURS
+    #     CONSOLE_LOGGER.info(
+    #         f"[{self}] - Plotting {metric.description()} for {describe_analysis_period(analysis_period, include_timestep=True)}"
+    #     )
+    #     series = self.direct_sun_hours(analysis_period)
+    #     tcf_properties = metric.tricontourf_kwargs()
+    #     tc_properties = metric.tricontour_kwargs()
+
+    #     fig, ax = plt.subplots(1, 1, figsize=(8, 8))
+    #     ax.set_aspect("equal")
+    #     ax.axis("off")
+    #     ax.set_xlim([min(self.points.x.values), max(self.points.x.values)])
+    #     ax.set_ylim([min(self.points.y.values), max(self.points.y.values)])
+
+    #     # add contour-fill
+    #     tcf = ax.tricontourf(self._triangulation, series.values, **tcf_properties)
+
+    #     # plot colorbar
+    #     divider = make_axes_locatable(ax)
+    #     cax = divider.append_axes("right", size="5%", pad=0.1, aspect=20)
+    #     cbar = plt.colorbar(tcf, cax=cax)
+    #     cbar.outline.set_visible(False)
+    #     cbar.set_label(metric.description())
+
+    #     # add contour lines if present
+    #     if len(tc_properties["levels"]) != 0:
+    #         tcs = ax.tricontour(self._triangulation, series.values, **tc_properties)
+    #         ax.clabel(tcs, inline=1, fontsize="x-small", colors=["k"])
+    #         cbar.add_lines(tcs)
+
+    #     # add title
+    #     ax.set_title(
+    #         f"{describe_analysis_period(analysis_period, include_timestep=False)}\n{metric.description()}\nAverage: {np.mean(series.values):0.1f} hours",
+    #         ha="left",
+    #         va="bottom",
+    #         x=0,
+    #     )
+
+    #     plt.tight_layout()
+
+    #     return fig
 
     def plot_sunpath(self, analysis_period: AnalysisPeriod) -> plt.Figure:
         """Return a figure showing the sunpath for this simulation at the given time."""
@@ -1204,7 +1197,7 @@ def plot_hours_comfortable(
         self,
         analysis_period: AnalysisPeriod,
         metric: SpatialMetric,
-        comfort_thresholds: Tuple[float] = (9, 26),
+        comfort_thresholds: tuple[float] = (9, 26),
     ) -> plt.Figure:
         """Return a figure showing an "hours comfortable" plot."""
 
@@ -1487,7 +1480,7 @@ def plot_hours_combined(
         self,
         analysis_period: AnalysisPeriod,
         metric: SpatialMetric,
-        comfort_thresholds: Tuple[float] = (0, 28),
+        comfort_thresholds: tuple[float] = (0, 28),
     ) -> plt.Figure:
         """Return an Image object containing a combination of "hours cold/hot/comfortable"."""
 
@@ -1650,8 +1643,7 @@ def summarise_point(
         point_index: int,
         point_identifier: str,
         metric: SpatialMetric,
-        comfort_limits: Tuple = (9, 26),
-        analysis_period: AnalysisPeriod = AnalysisPeriod(),
+        comfort_limits: tuple = (9, 26),
     ) -> None:
         """Return the figure object showing the location of a specific point."""
 
@@ -1710,14 +1702,14 @@ def summarise_point(
             fig = utci_heatmap_histogram(self._unshaded_utci, "Openfield")
             fig.savefig(save_path, transparent=True, bbox_inches="tight")
 
-        # create openfield UTCI distance to comfortable plot
-        save_path = self._plot_directory / "point_Openfield_distance_to_comfortable.png"
-        if not save_path.exists():
-            CONSOLE_LOGGER.info(
-                f"[{self}] - Plotting Openfield UTCI distance to comfortable"
-            )
-            fig = utci_distance_to_comfortable(self._unshaded_utci, "Openfield")
-            fig.savefig(save_path, transparent=True, bbox_inches="tight")
+        # # create openfield UTCI distance to comfortable plot
+        # save_path = self._plot_directory / "point_Openfield_distance_to_comfortable.png"
+        # if not save_path.exists():
+        #     CONSOLE_LOGGER.info(
+        #         f"[{self}] - Plotting Openfield UTCI distance to comfortable"
+        #     )
+        #     fig = utci_distance_to_comfortable(self._unshaded_utci, "Openfield")
+        #     fig.savefig(save_path, transparent=True, bbox_inches="tight")
 
         # create point location UTCI plot
         save_path = (
@@ -1728,17 +1720,17 @@ def summarise_point(
             f = utci_heatmap_histogram(point_utci, f"{self} - {point_identifier}")
             f.savefig(save_path, transparent=True, bbox_inches="tight")
 
-        # create pt location UTCI distance to comfortable plot
-        save_path = (
-            self._plot_directory
-            / f"point_{sanitise_string(point_identifier)}_distance_to_comfortable.png"
-        )
-        if not save_path.exists():
-            CONSOLE_LOGGER.info(
-                f"[{self}] - Plotting {point_identifier} UTCI distance to comfortable"
-            )
-            f = utci_distance_to_comfortable(point_utci, f"{self} - {point_identifier}")
-            f.savefig(save_path, transparent=True, bbox_inches="tight")
+        # # create pt location UTCI distance to comfortable plot
+        # save_path = (
+        #     self._plot_directory
+        #     / f"point_{sanitise_string(point_identifier)}_distance_to_comfortable.png"
+        # )
+        # if not save_path.exists():
+        #     CONSOLE_LOGGER.info(
+        #         f"[{self}] - Plotting {point_identifier} UTCI distance to comfortable"
+        #     )
+        #     f = utci_distance_to_comfortable(point_utci, f"{self} - {point_identifier}")
+        #     f.savefig(save_path, transparent=True, bbox_inches="tight")
 
         # create pt location UTCI difference
         save_path = (
@@ -1756,20 +1748,19 @@ def summarise_point(
             )
             f.savefig(save_path, transparent=True, bbox_inches="tight")
 
-        # create pt location CSV UTCI simplified
-        save_path = (
-            self._plot_directory
-            / f"point_{sanitise_string(point_identifier)}_monthlysummary.png"
-        )
-        if not save_path.exists():
-            pt_monthly_utci = describe_utci_monthly(
-                utci_collection=point_utci,
-                density=True,
-                simplified=True,
-                comfort_limits=comfort_limits,
-                analysis_periods=analysis_period,
-            )
-            pt_monthly_utci.to_csv(save_path)
+        # # create pt location CSV UTCI simplified
+        # save_path = (
+        #     self._plot_directory
+        #     / f"point_{sanitise_string(point_identifier)}_monthlysummary.png"
+        # )
+        # if not save_path.exists():
+        #     pt_monthly_utci = describe_utci_monthly(
+        #         utci_collection=point_utci,
+        #         density=True,
+        #         comfort_limits=comfort_limits,
+        #         analysis_periods=analysis_period,
+        #     )
+        #     pt_monthly_utci.to_csv(save_path)
 
         # create pt location sky view
         # self.plot_sky_view_pov(point_index, point_identifier, Vector3D(0, 0, 0.5))
@@ -1779,7 +1770,7 @@ def summarise_point(
     def summarise_point_from_json(
         self,
         metric: SpatialMetric,
-        comfort_limits: Tuple = (9, 26),
+        comfort_limits: tuple = (9, 26),
         analysis_period: AnalysisPeriod = AnalysisPeriod(),
     ) -> None:
         """For each point given in a JSON file, summarise the point."""
@@ -1811,7 +1802,7 @@ def run_all(
         comfort_percentages: bool = False,
         sunlight_hours: bool = False,
         london_comfort: bool = False,
-        comfort_limits: Tuple[float] = (9, 26),
+        comfort_limits: tuple[float] = (9, 26),
     ) -> None:
         """
         Run all plotting methods
@@ -1833,7 +1824,7 @@ def run_all(
                 Plot sunlight hours. Defaults to False.
             london_comfort (bool, optional):
                 Plot London comfort. Defaults to False.
-            comfort_limits (Tuple[float], optional):
+            comfort_limits (tuple[float], optional):
                 Comfort limits. Defaults to (9, 26).
 
         """
@@ -1960,15 +1951,16 @@ def run_all(
             time_delta = datetimes.max() - datetimes.min()
 
             if time_delta <= pd.Timedelta(days=1) and analysis_period.timestep > 1:
-                if sunlight_hours:
-                    save_path = (
-                        self._plot_directory
-                        / f"{describe_analysis_period(analysis_period, save_path=True, include_timestep=True)}_direct_sun_hours.png"
-                    )
-                    if not save_path.exists():
-                        fig = self.plot_direct_sun_hours(analysis_period)
-                        fig.savefig(save_path, transparent=True, bbox_inches="tight")
-                        plt.close(fig)
+                pass
+                # if sunlight_hours:
+                #     save_path = (
+                #         self._plot_directory
+                #         / f"{describe_analysis_period(analysis_period, save_path=True, include_timestep=True)}_direct_sun_hours.png"
+                #     )
+                #     if not save_path.exists():
+                #         fig = self.plot_direct_sun_hours(analysis_period)
+                #         fig.savefig(save_path, transparent=True, bbox_inches="tight")
+                #         plt.close(fig)
 
         if london_comfort:
             try:
@@ -1983,11 +1975,11 @@ def run_all(
                 pass
 
 
-def spatial_comfort_possible(simulation_directory: Path) -> bool:
+def spatial_comfort_possible(sim_dir: Path) -> bool:
     """Checks whether spatial_comfort processing is possible for a given simulation_directory.
 
     Args:
-        simulation_directory (Path):
+        sim_dir (Path):
             A folder containing Honeybee-Radiance Sky-View and Annual Irradiance results.
 
     Returns:
@@ -1995,19 +1987,19 @@ def spatial_comfort_possible(simulation_directory: Path) -> bool:
             True if possible. If impossible, then an error is raised instead.
     """
 
-    simulation_directory = Path(simulation_directory)
+    sim_dir = Path(sim_dir)
 
     # if process already run (and output files already exist), then return true
     if all(
         [
-            SpatialMetric.RAD_TOTAL.filepath(simulation_directory).exists(),
-            SpatialMetric.SKY_VIEW.filepath(simulation_directory).exists(),
+            SpatialMetric.RAD_TOTAL.filepath(sim_dir).exists(),
+            SpatialMetric.SKY_VIEW.filepath(sim_dir).exists(),
         ]
     ):
         return True
 
     # Check for annual irradiance data
-    annual_irradiance_directory = simulation_directory / "annual_irradiance"
+    annual_irradiance_directory = sim_dir / "annual_irradiance"
     if (
         not annual_irradiance_directory.exists()
         or len(list((annual_irradiance_directory / "results").glob("**/*.ill"))) == 0
@@ -2017,7 +2009,7 @@ def spatial_comfort_possible(simulation_directory: Path) -> bool:
         )
 
     # Check for sky-view data
-    sky_view_directory = simulation_directory / "sky_view"
+    sky_view_directory = sim_dir / "sky_view"
     if not (sky_view_directory).exists():
         raise FileNotFoundError(
             f"Sky-view data is not available in {sky_view_directory}."
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/typology.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/typology.py
index f0660695..da9b4e3a 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/typology.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/typology.py
@@ -1,460 +1,105 @@
-from __future__ import annotations
-
-import json
-import warnings
-from dataclasses import dataclass, field
+"""Module for creating typology objects."""
+# pylint: disable=E0401,E1101
 from enum import Enum
-from types import FunctionType
-from typing import Any, Dict, List, Tuple, Union
-
-import numpy as np
-import pandas as pd
-from ladybug.datacollection import HourlyContinuousCollection
-from ladybug.epw import EPW
-from ladybug_comfort.collection.pmv import PMV
-from ladybug_comfort.collection.utci import UTCI
 
-from ..bhomutil.analytics import CONSOLE_LOGGER
-from ..bhomutil.bhom_object import BHoMObject
-from ..bhomutil.encoder import BHoMEncoder
-from ..helpers import decay_rate_smoother, evaporative_cooling_effect
-from ..ladybug_extension.datacollection import (
-    collection_from_series,
-    collection_to_series,
-)
-from ..plot import utci_heatmap_histogram
-from .shelter import (
-    Shelter,
-    Shelters,
-    annual_effective_wind_speed,
-    annual_sun_exposure,
-    sky_exposure,
-)
-from .simulate import SimulationResult
+# pylint: enable=E0401
 
+import numpy as np
 
-@dataclass(init=True, repr=True, eq=True)
-class Typology(BHoMObject):
-    """An external comfort typology, describing the context in which thermal comfort will be calculated.
+from ..bhom.analytics import bhom_analytics
+from ._typologybase import Typology
+from ._shelterbase import Shelter
 
-    Args:
-        name (str):
-            The name of the external comfort typology.
-        shelters (List[Shelter], optional):
-            A list of shelters modifying exposure to the elements.
-            Defaults to None.
-        evaporative_cooling_effect (Union[float, List[float]), optional):
-            An amount of evaporative cooling to add to results calculated by
-            this typology. Defaults to 0. Can also be a list of 8760 values.
-        wind_speed_multiplier (float, optional):
-            A factor to multiply wind speed by. Defaults to 1. Can be used to
-            account for wind speed reduction due to sheltering not accounted
-            for by shelter objects, or to approximate effects of acceleration.
-        radiant_temperature_adjustment (Union[float, List[float]], optional):
-            A change in MRT to be applied. Defaults to 0. Can also be a
-            list of 8760 values. A positive value will increase the
-            MRT and a negative value will decrease it.
 
-    Returns:
-        Typology: An external comfort typology.
-    """
+class Typologies(Enum):
+    """_"""
 
-    Name: str = field(init=True, compare=True, repr=True)
-    Shelters: List[Shelter] = field(
-        init=True, compare=True, repr=False, default_factory=list
+    OPENFIELD = Typology(identifier="Openfield")
+    ENCLOSED = Typology(
+        identifier="Fully enclosed",
+        shelters=[
+            Shelter.from_adjacent_wall(angle=0),
+            Shelter.from_adjacent_wall(angle=90),
+            Shelter.from_adjacent_wall(angle=180),
+            Shelter.from_adjacent_wall(angle=270),
+            Shelter.from_overhead_circle(),
+        ],
     )
-    EvaporativeCoolingEffect: Union[float, List[float]] = field(
-        init=True, compare=True, repr=False, default=0
+    ENCLOSED_POROUS = Typology(
+        identifier="Fully enclosed 50% porous",
+        shelters=[
+            Shelter.from_adjacent_wall(angle=0).set_porosity(0.5),
+            Shelter.from_adjacent_wall(angle=90).set_porosity(0.5),
+            Shelter.from_adjacent_wall(angle=180).set_porosity(0.5),
+            Shelter.from_adjacent_wall(angle=270).set_porosity(0.5),
+            Shelter.from_overhead_circle().set_porosity(0.5),
+        ],
     )
-    WindSpeedMultiplier: Union[float, List[float]] = field(
-        init=True, compare=True, repr=False, default=1
+    SKY_SHELTER = Typology(
+        identifier="Sky shelter",
+        shelters=[
+            Shelter.from_overhead_circle(radius=5),
+        ],
     )
-    RadiantTemperatureAdjustment: Union[float, List[float]] = field(
-        init=True, compare=True, repr=False, default=0
+    NEAR_WATER = Typology(
+        identifier="Near water",
+        evaporative_cooling_effect=(0.15,) * 8760,
     )
-
-    _t: str = field(
-        init=False, compare=True, repr=False, default="BH.oM.LadybugTools.Typology"
+    MISTING = Typology(
+        identifier="Misting",
+        evaporative_cooling_effect=(0.3,) * 8760,
+    )
+    PASSIVE_DOWNDRAUGHT_EVAPORATIVE_COOLING_TOWER = Typology(
+        identifier="Passive downdraught evaporative cooling tower",
+        evaporative_cooling_effect=(0.7,) * 8760,
+        shelters=[Shelter.from_overhead_circle(height_above_ground=5, radius=4)],
+    )
+    NORTH_SHELTER = Typology(
+        identifier="Shelter from north",
+        shelters=[Shelter.from_adjacent_wall(angle=0)],
+    )
+    EAST_SHELTER = Typology(
+        identifier="Shelter from east",
+        shelters=[Shelter.from_adjacent_wall(angle=90)],
+    )
+    SOUTH_SHELTER = Typology(
+        identifier="Shelter from south",
+        shelters=[Shelter.from_adjacent_wall(angle=180)],
+    )
+    WEST_SHELTER = Typology(
+        identifier="Shelter from west",
+        shelters=[Shelter.from_adjacent_wall(angle=270)],
+    )
+    LINEAR_EAST_WEST_SHELTER = Typology(
+        identifier="Linear overhead shelter from east to west",
+        shelters=[Shelter.from_overhead_linear(angle=90)],
+    )
+    LINEAR_NORTH_SOUTH_SHELTER = Typology(
+        identifier="Linear overhead shelter from north to south",
+        shelters=[Shelter.from_overhead_linear(angle=0)],
     )
-
-    def __post_init__(self):
-        if isinstance(self.WindSpeedMultiplier, (float, int)):
-            self.WindSpeedMultiplier = np.ones(8760) * self.WindSpeedMultiplier
-        else:
-            self.WindSpeedMultiplier = np.array(self.WindSpeedMultiplier)
-        if min(self.WindSpeedMultiplier) < 0:
-            raise ValueError("The wind_speed_adjustment factor cannot be less than 0.")
-        if len(self.WindSpeedMultiplier) != 8760:
-            raise ValueError(
-                "Wind speed multiplier can only currently be either a single value applied across the entire year, or a list of 8760 values."
-            )
-
-        if isinstance(self.EvaporativeCoolingEffect, (float, int)):
-            self.EvaporativeCoolingEffect = (
-                np.ones(8760) * self.EvaporativeCoolingEffect
-            )
-        else:
-            self.EvaporativeCoolingEffect = np.array(self.EvaporativeCoolingEffect)
-        if (
-            min(self.EvaporativeCoolingEffect) < 0
-            or max(self.EvaporativeCoolingEffect) > 1
-        ):
-            raise ValueError("Evaporative cooling effect must be between 0 and 1.")
-        if len(self.EvaporativeCoolingEffect) != 8760:
-            raise ValueError(
-                "Evaporative cooling effect can only currently be either a single value applied across the entire year, or a list of 8760 values."
-            )
-
-        if isinstance(self.RadiantTemperatureAdjustment, (float, int)):
-            self.RadiantTemperatureAdjustment = (
-                np.ones(8760) * self.RadiantTemperatureAdjustment
-            )
-        else:
-            self.RadiantTemperatureAdjustment = np.array(
-                self.RadiantTemperatureAdjustment
-            )
-        if len(self.RadiantTemperatureAdjustment) != 8760:
-            raise ValueError(
-                "Radiant temperature adjustment can only currently be either a single value applied across the entire year, or a list of 8760 values."
-            )
-
-        # wrap methods within this class
-        super().__post_init__()
-
-    @classmethod
-    def from_dict(cls, dictionary: Dict[str, Any]) -> Typology:
-        """Create this object from a dictionary."""
-
-        # handle shelter object conversions
-        for n, shelter in enumerate(dictionary["Shelters"]):
-            if not isinstance(shelter, Shelter):
-                dictionary["Shelters"][n] = Shelter.from_dict(shelter)
-
-        return cls(
-            Name=dictionary["Name"],
-            Shelters=dictionary["Shelters"],
-            EvaporativeCoolingEffect=dictionary["EvaporativeCoolingEffect"],
-            WindSpeedMultiplier=dictionary["WindSpeedMultiplier"],
-            RadiantTemperatureAdjustment=dictionary["RadiantTemperatureAdjustment"],
-        )
-
-    @classmethod
-    def from_json(cls, json_string: str) -> Typology:
-        """Create this object from a JSON string."""
-        return cls.from_dict(json.loads(json_string))
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Return this object as it's dictionary equivalent."""
-        dictionary = {}
-        for k, v in self.__dict__.items():
-            if isinstance(getattr(self, k), FunctionType):
-                continue
-            dictionary[k] = v
-        dictionary["_t"] = self._t
-        return dictionary
-
-    def to_json(self) -> str:
-        """Return this object as it's JSON string equivalent."""
-        return json.dumps(self.to_dict(), cls=BHoMEncoder)
-
-    @property
-    def evaporative_cooling_effect(self) -> List[float]:
-        """Handy alias for the evaporative cooling effect."""
-        return self.EvaporativeCoolingEffect
-
-    @property
-    def shelters(self) -> List[Shelter]:
-        """Handy alias for the shelters."""
-        return self.Shelters
-
-    @property
-    def wind_speed_multiplier(self) -> List[float]:
-        """Handy alias for the wind speed multiplier."""
-        return self.WindSpeedMultiplier
-
-    @property
-    def radiant_temperature_adjustment(self) -> List[float]:
-        """Handy alias for the radiant temperature adjustment."""
-        return self.RadiantTemperatureAdjustment
-
-    @property
-    def name(self) -> str:
-        """Handy alias for the name."""
-        return self.Name
-
-    def sky_exposure(self) -> float:
-        """Direct access to "sky_exposure" method for this typology object."""
-        return sky_exposure(self.Shelters, include_radiation_porosity=True)
-
-    def sun_exposure(self, epw: EPW) -> List[float]:
-        """Direct access to "sun_exposure" method for this typology object."""
-        return annual_sun_exposure(self.Shelters, epw, include_radiation_porosity=True)
-
-    def dry_bulb_temperature(self, epw: EPW) -> HourlyContinuousCollection:
-        """Get the effective DBT for the given EPW file for this Typology.
-
-        Args:
-            epw (EPW): A ladybug EPW object.
-
-        Returns:
-            HourlyContinuousCollection: The effective DBT following application of any evaporative
-                cooling effects.
-        """
-        # if there is no evaporative cooling effect, return the original DBT
-        if sum(self.EvaporativeCoolingEffect) == 0:
-            return epw.dry_bulb_temperature
-
-        # if there is evaporative cooling effect, return the adjusted DBT
-        dbt_evap = []
-        for dbt, rh, ece, atm in list(
-            zip(
-                *[
-                    epw.dry_bulb_temperature,
-                    epw.relative_humidity,
-                    self.EvaporativeCoolingEffect,
-                    epw.atmospheric_station_pressure,
-                ]
-            )
-        ):
-            _dbt, _ = evaporative_cooling_effect(
-                dry_bulb_temperature=dbt,
-                relative_humidity=rh,
-                evaporative_cooling_effectiveness=ece,
-                atmospheric_pressure=atm,
-            )
-            dbt_evap.append(_dbt)
-
-        return epw.dry_bulb_temperature.get_aligned_collection(dbt_evap)
-
-    def relative_humidity(self, epw: EPW) -> HourlyContinuousCollection:
-        """Get the effective RH for the given EPW file for this Typology.
-
-        Args:
-            epw (EPW): A ladybug EPW object.
-
-        Returns:
-            HourlyContinuousCollection: The effective RH following application of any evaporative
-                cooling effects.
-        """
-
-        # if there is no evaporative cooling effect, return the original RH
-        if sum(self.EvaporativeCoolingEffect) == 0:
-            return epw.relative_humidity
-
-        # if there is evaporative cooling effect, return the adjusted RH
-        rh_evap = []
-        for dbt, rh, ece, atm in list(
-            zip(
-                *[
-                    epw.dry_bulb_temperature,
-                    epw.relative_humidity,
-                    self.EvaporativeCoolingEffect,
-                    epw.atmospheric_station_pressure,
-                ]
-            )
-        ):
-            _, _rh = evaporative_cooling_effect(
-                dry_bulb_temperature=dbt,
-                relative_humidity=rh,
-                evaporative_cooling_effectiveness=ece,
-                atmospheric_pressure=atm,
-            )
-            rh_evap.append(_rh)
-
-        return epw.relative_humidity.get_aligned_collection(rh_evap)
-
-    def wind_speed(self, epw: EPW) -> HourlyContinuousCollection:
-        """Calculate wind speed when subjected to a set of shelters.
-
-        Args:
-            epw (EPW): The input EPW.
-
-        Returns:
-            HourlyContinuousCollection: The resultant wind-speed.
-        """
-
-        ws = epw.wind_speed
-
-        if len(self.Shelters) == 0:
-            return ws.get_aligned_collection(
-                np.array(ws.values) * self.WindSpeedMultiplier
-            )
-
-        # adjust to 0 if multiplier is 0
-        if sum(self.WindSpeedMultiplier) == 0:
-            return ws.get_aligned_collection(0)
-
-        # adjust ws based on shelter configuration
-        wind_speed_pre_multiplier = epw.wind_speed.get_aligned_collection(
-            annual_effective_wind_speed(self.Shelters, epw)
-        )
-
-        # adjust ws based on multiplier (collection)
-        return ws.get_aligned_collection(
-            np.array(wind_speed_pre_multiplier.values) * self.WindSpeedMultiplier
-        )
-
-    def mean_radiant_temperature(
-        self,
-        simulation_result: SimulationResult,
-    ) -> HourlyContinuousCollection:
-        """Return the effective mean radiant temperature for the given typology following a
-            simulation of the collections necessary to calculate this.
-
-        Args:
-            simulation_result (SimulationResult): An object containing all collections describing
-                shaded and unshaded mean-radiant-temperature.
-
-        Returns:
-            HourlyContinuousCollection: An calculated mean radiant temperature based on the shelter
-                configuration for the given typology.
-        """
-
-        shaded_mrt = collection_to_series(
-            simulation_result.ShadedMeanRadiantTemperature
-        )
-        unshaded_mrt = collection_to_series(
-            simulation_result.UnshadedMeanRadiantTemperature
-        )
-
-        daytime = np.array(
-            [i > 0 for i in simulation_result.epw.global_horizontal_radiation]
-        )
-        _sun_exposure = self.sun_exposure(simulation_result.epw)
-        _sky_exposure = self.sky_exposure()
-        mrts = []
-        for sun_exp, sun_up, shaded, unshaded in list(
-            zip(*[_sun_exposure, daytime, shaded_mrt.values, unshaded_mrt.values])
-        ):
-            if sun_up:
-                mrts.append(np.interp(sun_exp, [0, 1], [shaded, unshaded]))
-            else:
-                mrts.append(np.interp(_sky_exposure, [0, 1], [shaded, unshaded]))
-
-        # Fill any gaps where sun-visible/sun-occluded values are missing
-        mrt_series = pd.Series(
-            mrts, index=shaded_mrt.index, name=shaded_mrt.name
-        ).interpolate()
-
-        # apply an exponentially weighted moving average to account for
-        # transition between shaded/unshaded periods on surrounding surface
-        # temperatures
-        mrt_series = decay_rate_smoother(
-            mrt_series, difference_threshold=-10, transition_window=4, ewm_span=1.25
-        )
-
-        # apply radiant temperature adjustment if given
-        return collection_from_series(mrt_series + self.RadiantTemperatureAdjustment)
-
-    def universal_thermal_climate_index(
-        self, simulation_result: SimulationResult, return_comfort_obj: bool = False
-    ) -> Union[UTCI, HourlyContinuousCollection]:
-        """Return the universal thermal climate index for the given typology
-            following a simulation of the collections necessary to calculate
-            this.
-
-        Args:
-            simulation_result (SimulationResult):
-                An object containing all collections describing shaded and
-                unshaded mean-radiant-temperature.
-            return_comfort_obj (bool, optional):
-                Set to True to return the UTCI comfort object instead of the
-                data collection.
-
-        Returns:
-            Union[UTCI, HourlyContinuousCollection]:
-                The resultant UTCI for this Typology, in the given
-                SimulationResult location.
-        """
-        epw = simulation_result.epw
-        utci = UTCI(
-            air_temperature=self.dry_bulb_temperature(epw),
-            rel_humidity=self.relative_humidity(epw),
-            rad_temperature=self.mean_radiant_temperature(simulation_result),
-            wind_speed=self.wind_speed(epw),
-        )
-
-        CONSOLE_LOGGER.info(
-            f"[{simulation_result.Identifier} - {self.Name}] - Calculating universal thermal climate index"
-        )
-
-        if return_comfort_obj:
-            return utci
-
-        return utci.universal_thermal_climate_index
-
-    def standard_effective_temperature(
-        self,
-        simulation_result: SimulationResult,
-        met_rate: float = 1.1,
-        clo_value: float = 0.7,
-        return_comfort_obj: bool = False,
-    ) -> Union[PMV, HourlyContinuousCollection]:
-        """Return the predicted mean vote for the given typology
-            following a simulation of the collections necessary to calculate
-            this.
-
-        Args:
-            simulation_result (SimulationResult):
-                An object containing all collections describing shaded and
-                unshaded mean-radiant-temperature.
-            met_rate (float, optional):
-                Metabolic rate value.
-            clo_value (float, optional):
-                Clothing value.
-            return_comfort_obj (bool, optional):
-                Set to True to return the PMV comfort object instead of the
-                data collection.
-
-        Returns:
-            Union[PMV, HourlyContinuousCollection]:
-                The resultant PMV for this Typology, in the given
-                SimulationResult location.
-        """
-        epw = simulation_result.epw
-        pmv = PMV(
-            air_temperature=self.dry_bulb_temperature(epw),
-            rel_humidity=self.relative_humidity(epw),
-            rad_temperature=self.mean_radiant_temperature(simulation_result),
-            air_speed=self.wind_speed(epw),
-            met_rate=met_rate,
-            clo_value=clo_value,
-        )
-
-        if return_comfort_obj:
-            return pmv
-
-        return pmv.standard_effective_temperature
-
-    def plot_utci_hist(self, res: SimulationResult) -> None:
-        """Convenience method to plot UTCI histogram directly from a typology."""
-        warnings.warn(
-            "While it is possible to call from a Typology object, the recommended method of calling the UTCI histogram is from an ExternalComfort object."
-        )
-        return utci_heatmap_histogram(
-            self.universal_thermal_climate_index(res), title=self.Name
-        )
 
 
+@bhom_analytics()
 def combine_typologies(
-    typologies: Tuple[Typology],
-    evaporative_cooling_effect_weights: Tuple[float] = None,
-    wind_speed_multiplier_weights: Tuple[float] = None,
-    radiant_temperature_adjustment_weights: Tuple[float] = None,
+    typologies: list[Typology],
+    evaporative_cooling_effect_weights: list[float] = None,
+    target_wind_speed_weights: list[float] = None,
+    radiant_temperature_adjustment_weights: list[float] = None,
 ) -> Typology:
     """Combine multiple typologies into a single typology.
 
     Args:
-        typologies (Tuple[Typology]):
-            A tuple of typologies to combine.
-        evaporative_cooling_effect_weights (Tuple[float], optional):
-            A tuple of weights to apply to the evaporative cooling effect
+        typologies (list[Typology]):
+            A list of typologies to combine.
+        evaporative_cooling_effect_weights (list[float], optional):
+            A list of weights to apply to the evaporative cooling effect
             of each typology. Defaults to None.
-        wind_speed_multiplier_weights (Tuple[float], optional):
-            A tuple of weights to apply to the wind speed multiplier
+        target_wind_speed_weights (list[float], optional):
+            A list of weights to apply to the wind speed multiplier
             of each typology. Defaults to None.
-        radiant_temperature_adjustment_weights (Tuple[float], optional):
-            A tuple of weights to apply to the radiant temperature adjustment
+        radiant_temperature_adjustment_weights (list[float], optional):
+            A list of weights to apply to the radiant temperature adjustment
             of each typology. Defaults to None.
 
     Raises:
@@ -464,215 +109,40 @@ def combine_typologies(
         Typology: A combined typology.
     """
 
-    if evaporative_cooling_effect_weights is None:
-        evaporative_cooling_effect_weights = np.ones_like(typologies) * (
-            1 / len(typologies)
-        )
-    else:
-        if sum(evaporative_cooling_effect_weights) != 1:
-            raise ValueError("evaporative_cooling_effect_weights must sum to 1.")
-
-    if wind_speed_multiplier_weights is None:
-        wind_speed_multiplier_weights = np.ones_like(typologies) * (1 / len(typologies))
-    else:
-        if sum(wind_speed_multiplier_weights) != 1:
-            raise ValueError("wind_speed_multiplier_weights must sum to 1.")
-
-    if radiant_temperature_adjustment_weights is None:
-        radiant_temperature_adjustment_weights = np.ones_like(typologies) * (
-            1 / len(typologies)
-        )
-    else:
-        if sum(radiant_temperature_adjustment_weights) != 1:
-            raise ValueError("radiant_temperature_adjustment_weights must sum to 1.")
-
     all_shelters = []
     for typ in typologies:
-        all_shelters.extend(typ.Shelters)
+        all_shelters.extend(typ.shelters)
 
-    ec_effect = np.average(
-        [i.EvaporativeCoolingEffect for i in typologies],
-        weights=evaporative_cooling_effect_weights,
-        axis=0,
-    )
-    ws_multiplier = np.average(
-        [i.WindSpeedMultiplier for i in typologies],
-        weights=wind_speed_multiplier_weights,
+    target_wind_speed_avg = np.stack(
+        [i.target_wind_speed for i in typologies],
         axis=0,
-    )
-    tr_adjustment = np.average(
-        [i.RadiantTemperatureAdjustment for i in typologies],
+    ).T
+    target_wind_speed_avg[target_wind_speed_avg is None] = np.nan
+    target_wind_speed_avg = target_wind_speed_avg.astype(float)
+    masked_arr = np.ma.masked_invalid(target_wind_speed_avg)
+    target_wind_speed_avg = (
+        np.ma.average(masked_arr, axis=1, weights=target_wind_speed_weights)
+        .filled(np.nan)
+        .tolist()
+    )
+    target_wind_speed_avg = [None if np.isnan(i) else i for i in target_wind_speed_avg]
+
+    radiant_temperature_adjustment_avg = np.average(
+        [i.radiant_temperature_adjustment for i in typologies],
         weights=radiant_temperature_adjustment_weights,
         axis=0,
-    )
-
-    return Typology(
-        Name=" + ".join([i.Name for i in typologies]),
-        Shelters=all_shelters,
-        EvaporativeCoolingEffect=ec_effect,
-        WindSpeedMultiplier=ws_multiplier,
-        RadiantTemperatureAdjustment=tr_adjustment,
-    )
-
+    ).tolist()
 
-class Typologies(Enum):
-    """A list of pre-defined Typology objects."""
-
-    OPENFIELD = Typology(
-        Name="Openfield",
-    )
-    ENCLOSED = Typology(
-        Name="Enclosed",
-        Shelters=[
-            Shelters.NORTH.value,
-            Shelters.EAST.value,
-            Shelters.SOUTH.value,
-            Shelters.WEST.value,
-            Shelters.OVERHEAD_LARGE.value,
-        ],
-    )
-    POROUS_ENCLOSURE = Typology(
-        Name="Porous enclosure",
-        Shelters=[
-            Shelters.NORTH.value.set_porosity(0.5),
-            Shelters.EAST.value.set_porosity(0.5),
-            Shelters.SOUTH.value.set_porosity(0.5),
-            Shelters.WEST.value.set_porosity(0.5),
-            Shelters.OVERHEAD_LARGE.value.set_porosity(0.5),
-        ],
-    )
-    SKY_SHELTER = Typology(
-        Name="Sky-shelter",
-        Shelters=[
-            Shelters.OVERHEAD_LARGE.value,
-        ],
-    )
-    FRITTED_SKY_SHELTER = Typology(
-        Name="Fritted sky-shelter",
-        Shelters=[
-            Shelters.OVERHEAD_LARGE.value.set_porosity(0.5),
-        ],
-    )
-    NEAR_WATER = Typology(
-        Name="Near water",
-        EvaporativeCoolingEffect=0.15,
-    )
-    MISTING = Typology(
-        Name="Misting",
-        EvaporativeCoolingEffect=0.3,
-    )
-    PDEC = Typology(
-        Name="PDEC",
-        EvaporativeCoolingEffect=0.7,
-    )
-    NORTH_SHELTER = Typology(
-        Name="North shelter",
-        Shelters=[
-            Shelters.NORTH.value,
-        ],
-    )
-    NORTHEAST_SHELTER = Typology(
-        Name="Northeast shelter", Shelters=[Shelters.NORTHEAST.value]
-    )
-    EAST_SHELTER = Typology(Name="East shelter", Shelters=[Shelters.EAST.value])
-    SOUTHEAST_SHELTER = Typology(
-        Name="Southeast shelter", Shelters=[Shelters.SOUTHEAST.value]
-    )
-    SOUTH_SHELTER = Typology(
-        Name="South shelter",
-        Shelters=[
-            Shelters.SOUTH.value,
-        ],
-    )
-    SOUTHWEST_SHELTER = Typology(
-        Name="Southwest shelter", Shelters=[Shelters.SOUTHWEST.value]
-    )
-    WEST_SHELTER = Typology(Name="West shelter", Shelters=[Shelters.WEST.value])
-    NORTHWEST_SHELTER = Typology(
-        Name="Northwest shelter", Shelters=[Shelters.NORTHWEST.value]
-    )
-    NORTH_SHELTER_WITH_CANOPY = Typology(
-        Name="North shelter with canopy",
-        Shelters=[
-            Shelters.NORTH.value,
-            Shelters.CANOPY_N_E_S_W.value,
-        ],
-    )
-    NORTHEAST_SHELTER_WITH_CANOPY = Typology(
-        Name="Northeast shelter with canopy",
-        Shelters=[
-            Shelters.NORTHEAST.value,
-            Shelters.CANOPY_NE_SE_SW_NW.value,
-        ],
-    )
-    EAST_SHELTER_WITH_CANOPY = Typology(
-        Name="East shelter with canopy",
-        Shelters=[
-            Shelters.EAST.value,
-            Shelters.CANOPY_N_E_S_W.value,
-        ],
-    )
-    SOUTHEAST_SHELTER_WITH_CANOPY = Typology(
-        Name="Southeast shelter with canopy",
-        Shelters=[
-            Shelters.SOUTHEAST.value,
-            Shelters.CANOPY_NE_SE_SW_NW.value,
-        ],
-    )
-    SOUTH_SHELTER_WITH_CANOPY = Typology(
-        Name="South shelter with canopy",
-        Shelters=[
-            Shelters.SOUTH.value,
-            Shelters.CANOPY_N_E_S_W.value,
-        ],
-    )
-    SOUTHWEST_SHELTER_WITH_CANOPY = Typology(
-        Name="Southwest shelter with canopy",
-        Shelters=[
-            Shelters.SOUTHWEST.value,
-            Shelters.CANOPY_NE_SE_SW_NW.value,
-        ],
-    )
-    WEST_SHELTER_WITH_CANOPY = Typology(
-        Name="West shelter with canopy",
-        Shelters=[
-            Shelters.WEST.value,
-            Shelters.CANOPY_N_E_S_W.value,
-        ],
-    )
-    NORTHWEST_SHELTER_WITH_CANOPY = Typology(
-        Name="Northwest shelter with canopy",
-        Shelters=[
-            Shelters.NORTHWEST.value,
-            Shelters.CANOPY_NE_SE_SW_NW.value,
-        ],
-    )
+    evaporative_cooling_effect_avg = np.average(
+        [i.evaporative_cooling_effect for i in typologies],
+        weights=evaporative_cooling_effect_weights,
+        axis=0,
+    ).tolist()
 
-    NORTHSOUTH_LINEAR_SHELTER = Typology(
-        Name="North-south linear overhead shelter",
-        Shelters=[
-            Shelters.NORTH_SOUTH_LINEAR.value,
-        ],
-    )
-    NORTHEAST_SOUTHWEST_LINEAR_SHELTER = Typology(
-        Name="Northeast-southwest linear overhead shelter",
-        Shelters=[
-            Shelters.NORTHEAST_SOUTHWEST_LINEAR.value,
-        ],
-    )
-    EAST_WEST_LINEAR_SHELTER = Typology(
-        Name="East-west linear overhead shelter",
-        Shelters=[
-            Shelters.EAST_WEST_LINEAR.value,
-        ],
-    )
-    NORTHWEST_SOUTHEAST_LINEAR_SHELTER = Typology(
-        Name="Northwest-southeast linear overhead shelter",
-        Shelters=[
-            Shelters.NORTHWEST_SOUTHEAST_LINEAR.value,
-        ],
+    return Typology(
+        identifier=" + ".join([i.identifier for i in typologies]),
+        shelters=all_shelters,
+        evaporative_cooling_effect=evaporative_cooling_effect_avg,
+        target_wind_speed=target_wind_speed_avg,
+        radiant_temperature_adjustment=radiant_temperature_adjustment_avg,
     )
-
-    def to_json(self) -> str:
-        """Convert the current typology into its BHoM JSON string format."""
-        return self.value.to_json()
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci.py
new file mode 100644
index 00000000..f724497c
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci.py
@@ -0,0 +1,1559 @@
+"""Methods for calculating UTCI."""
+# pylint: disable=C0302
+
+# pylint: disable=E0401
+import warnings
+from calendar import month_abbr
+from concurrent.futures import ProcessPoolExecutor
+
+# pylint: enable=E0401
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+from ladybug.datacollection import HourlyContinuousCollection
+from ladybug.datatype.temperature import (
+    UniversalThermalClimateIndex as LB_UniversalThermalClimateIndex,
+)
+from ladybug.epw import EPW
+from ladybug_comfort.collection.solarcal import OutdoorSolarCal
+from ladybug_comfort.collection.utci import UTCI
+from scipy.interpolate import interp1d, interp2d
+from tqdm import tqdm
+
+from ..bhom.analytics import bhom_analytics
+from ..categorical.categories import UTCI_DEFAULT_CATEGORIES, CategoricalComfort
+from ..helpers import evaporative_cooling_effect, month_hour_binned_series
+from ..ladybug_extension.datacollection import (
+    collection_from_series,
+    collection_to_series,
+)
+
+
+def _saturated_vapor_pressure_hpa(dry_bulb_temperature: np.ndarray):
+    """Calculate saturated vapor pressure (hPa) at temperature (C).
+
+    Args:
+        dry_bulb_temperature (np.ndarray):
+            Dry bulb temperature [C]
+
+    Returns:
+        np.ndarray:
+            Saturated vapor pressure [hPa]
+
+    Note:
+        This equation of saturation vapor pressure is specific to the UTCI model.
+
+    """
+    dry_bulb_temperature = np.atleast_1d(dry_bulb_temperature)
+    g = (
+        -2836.5744,
+        -6028.076559,
+        19.54263612,
+        -0.02737830188,
+        0.000016261698,
+        7.0229056e-10,
+        -1.8680009e-13,
+    )
+    tk = dry_bulb_temperature + 273.15  # air temp in K
+    es = 2.7150305 * np.log(tk)
+    for i, x in enumerate(g):
+        es += x * (tk ** (i - 2))
+
+    return np.exp(es) * 0.01
+
+
+def _utci_ndarray(
+    air_temperature: np.ndarray,
+    mean_radiant_temperature: np.ndarray,
+    wind_speed: np.ndarray,
+    relative_humidity: np.ndarray,
+) -> np.ndarray:
+    """This method is a vectorised version of the universal_thermal_climate_index method defined in ladybug-tools
+    https://github.com/ladybug-tools/ladybug-comfort/blob/master/ladybug_comfort/utci.py
+
+    Args:
+        air_temperature (np.ndarray):
+            Air temperature [C]
+        mean_radiant_temperature (np.ndarray):
+            Mean radiant temperature [C]
+        wind_speed (np.ndarray):
+            Wind speed 10 m above ground level [m/s]
+        relative_humidity (np.ndarray):
+            Relative humidity [%]
+
+    Returns:
+        np.ndarray:
+            The Universal Thermal Climate Index (UTCI) for the input conditions as approximated by a 4-D polynomial
+    """
+
+    g = (
+        -2836.5744,
+        -6028.076559,
+        19.54263612,
+        -0.02737830188,
+        0.000016261698,
+        7.0229056e-10,
+        -1.8680009e-13,
+    )
+    tk = air_temperature + 273.15  # air temp in K
+    es = 2.7150305 * np.log(tk)
+    for i, x in enumerate(g):
+        es = es + (x * (tk ** (i - 2)))
+    es = np.exp(es) * 0.01
+    eh_pa = es * (relative_humidity / 100.0)  # partial vapor pressure
+    pa_pr = eh_pa / 10.0  # convert vapour pressure to kPa
+    d_tr = (
+        mean_radiant_temperature - air_temperature
+    )  # difference between radiant and air temperature
+
+    utci_approx = (
+        air_temperature
+        + 0.607562052
+        + -0.0227712343 * air_temperature
+        + 8.06470249e-4 * air_temperature * air_temperature
+        + -1.54271372e-4 * air_temperature * air_temperature * air_temperature
+        + -3.24651735e-6
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        + 7.32602852e-8
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        + 1.35959073e-9
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        + -2.25836520 * wind_speed
+        + 0.0880326035 * air_temperature * wind_speed
+        + 0.00216844454 * air_temperature * air_temperature * wind_speed
+        + -1.53347087e-5
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        + -5.72983704e-7
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        + -2.55090145e-9
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        + -0.751269505 * wind_speed * wind_speed
+        + -0.00408350271 * air_temperature * wind_speed * wind_speed
+        + -5.21670675e-5 * air_temperature * air_temperature * wind_speed * wind_speed
+        + 1.94544667e-6
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        + 1.14099531e-8
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        + 0.158137256 * wind_speed * wind_speed * wind_speed
+        + -6.57263143e-5 * air_temperature * wind_speed * wind_speed * wind_speed
+        + 2.22697524e-7
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        + -4.16117031e-8
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        + -0.0127762753 * wind_speed * wind_speed * wind_speed * wind_speed
+        + 9.66891875e-6
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        + 2.52785852e-9
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        + 4.56306672e-4 * wind_speed * wind_speed * wind_speed * wind_speed * wind_speed
+        + -1.74202546e-7
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        + -5.91491269e-6
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        + 0.398374029 * d_tr
+        + 1.83945314e-4 * air_temperature * d_tr
+        + -1.73754510e-4 * air_temperature * air_temperature * d_tr
+        + -7.60781159e-7 * air_temperature * air_temperature * air_temperature * d_tr
+        + 3.77830287e-8
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * d_tr
+        + 5.43079673e-10
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * d_tr
+        + -0.0200518269 * wind_speed * d_tr
+        + 8.92859837e-4 * air_temperature * wind_speed * d_tr
+        + 3.45433048e-6 * air_temperature * air_temperature * wind_speed * d_tr
+        + -3.77925774e-7
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * d_tr
+        + -1.69699377e-9
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * d_tr
+        + 1.69992415e-4 * wind_speed * wind_speed * d_tr
+        + -4.99204314e-5 * air_temperature * wind_speed * wind_speed * d_tr
+        + 2.47417178e-7
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * d_tr
+        + 1.07596466e-8
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * d_tr
+        + 8.49242932e-5 * wind_speed * wind_speed * wind_speed * d_tr
+        + 1.35191328e-6 * air_temperature * wind_speed * wind_speed * wind_speed * d_tr
+        + -6.21531254e-9
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * d_tr
+        + -4.99410301e-6 * wind_speed * wind_speed * wind_speed * wind_speed * d_tr
+        + -1.89489258e-8
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * d_tr
+        + 8.15300114e-8
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * d_tr
+        + 7.55043090e-4 * d_tr * d_tr
+        + -5.65095215e-5 * air_temperature * d_tr * d_tr
+        + -4.52166564e-7 * air_temperature * air_temperature * d_tr * d_tr
+        + 2.46688878e-8
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * d_tr
+        + 2.42674348e-10
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * d_tr
+        + 1.54547250e-4 * wind_speed * d_tr * d_tr
+        + 5.24110970e-6 * air_temperature * wind_speed * d_tr * d_tr
+        + -8.75874982e-8 * air_temperature * air_temperature * wind_speed * d_tr * d_tr
+        + -1.50743064e-9
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * d_tr
+        * d_tr
+        + -1.56236307e-5 * wind_speed * wind_speed * d_tr * d_tr
+        + -1.33895614e-7 * air_temperature * wind_speed * wind_speed * d_tr * d_tr
+        + 2.49709824e-9
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * d_tr
+        * d_tr
+        + 6.51711721e-7 * wind_speed * wind_speed * wind_speed * d_tr * d_tr
+        + 1.94960053e-9
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * d_tr
+        * d_tr
+        + -1.00361113e-8
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * d_tr
+        * d_tr
+        + -1.21206673e-5 * d_tr * d_tr * d_tr
+        + -2.18203660e-7 * air_temperature * d_tr * d_tr * d_tr
+        + 7.51269482e-9 * air_temperature * air_temperature * d_tr * d_tr * d_tr
+        + 9.79063848e-11
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * d_tr
+        * d_tr
+        + 1.25006734e-6 * wind_speed * d_tr * d_tr * d_tr
+        + -1.81584736e-9 * air_temperature * wind_speed * d_tr * d_tr * d_tr
+        + -3.52197671e-10
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * d_tr
+        * d_tr
+        * d_tr
+        + -3.36514630e-8 * wind_speed * wind_speed * d_tr * d_tr * d_tr
+        + 1.35908359e-10
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * d_tr
+        * d_tr
+        * d_tr
+        + 4.17032620e-10 * wind_speed * wind_speed * wind_speed * d_tr * d_tr * d_tr
+        + -1.30369025e-9 * d_tr * d_tr * d_tr * d_tr
+        + 4.13908461e-10 * air_temperature * d_tr * d_tr * d_tr * d_tr
+        + 9.22652254e-12 * air_temperature * air_temperature * d_tr * d_tr * d_tr * d_tr
+        + -5.08220384e-9 * wind_speed * d_tr * d_tr * d_tr * d_tr
+        + -2.24730961e-11 * air_temperature * wind_speed * d_tr * d_tr * d_tr * d_tr
+        + 1.17139133e-10 * wind_speed * wind_speed * d_tr * d_tr * d_tr * d_tr
+        + 6.62154879e-10 * d_tr * d_tr * d_tr * d_tr * d_tr
+        + 4.03863260e-13 * air_temperature * d_tr * d_tr * d_tr * d_tr * d_tr
+        + 1.95087203e-12 * wind_speed * d_tr * d_tr * d_tr * d_tr * d_tr
+        + -4.73602469e-12 * d_tr * d_tr * d_tr * d_tr * d_tr * d_tr
+        + 5.12733497 * pa_pr
+        + -0.312788561 * air_temperature * pa_pr
+        + -0.0196701861 * air_temperature * air_temperature * pa_pr
+        + 9.99690870e-4 * air_temperature * air_temperature * air_temperature * pa_pr
+        + 9.51738512e-6
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * pa_pr
+        + -4.66426341e-7
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * pa_pr
+        + 0.548050612 * wind_speed * pa_pr
+        + -0.00330552823 * air_temperature * wind_speed * pa_pr
+        + -0.00164119440 * air_temperature * air_temperature * wind_speed * pa_pr
+        + -5.16670694e-6
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * pa_pr
+        + 9.52692432e-7
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * pa_pr
+        + -0.0429223622 * wind_speed * wind_speed * pa_pr
+        + 0.00500845667 * air_temperature * wind_speed * wind_speed * pa_pr
+        + 1.00601257e-6
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        + -1.81748644e-6
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        + -1.25813502e-3 * wind_speed * wind_speed * wind_speed * pa_pr
+        + -1.79330391e-4
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        + 2.34994441e-6
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        + 1.29735808e-4 * wind_speed * wind_speed * wind_speed * wind_speed * pa_pr
+        + 1.29064870e-6
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        + -2.28558686e-6
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        + -0.0369476348 * d_tr * pa_pr
+        + 0.00162325322 * air_temperature * d_tr * pa_pr
+        + -3.14279680e-5 * air_temperature * air_temperature * d_tr * pa_pr
+        + 2.59835559e-6
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * pa_pr
+        + -4.77136523e-8
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * pa_pr
+        + 8.64203390e-3 * wind_speed * d_tr * pa_pr
+        + -6.87405181e-4 * air_temperature * wind_speed * d_tr * pa_pr
+        + -9.13863872e-6 * air_temperature * air_temperature * wind_speed * d_tr * pa_pr
+        + 5.15916806e-7
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * d_tr
+        * pa_pr
+        + -3.59217476e-5 * wind_speed * wind_speed * d_tr * pa_pr
+        + 3.28696511e-5 * air_temperature * wind_speed * wind_speed * d_tr * pa_pr
+        + -7.10542454e-7
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * d_tr
+        * pa_pr
+        + -1.24382300e-5 * wind_speed * wind_speed * wind_speed * d_tr * pa_pr
+        + -7.38584400e-9
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * d_tr
+        * pa_pr
+        + 2.20609296e-7
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * d_tr
+        * pa_pr
+        + -7.32469180e-4 * d_tr * d_tr * pa_pr
+        + -1.87381964e-5 * air_temperature * d_tr * d_tr * pa_pr
+        + 4.80925239e-6 * air_temperature * air_temperature * d_tr * d_tr * pa_pr
+        + -8.75492040e-8
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * d_tr
+        * pa_pr
+        + 2.77862930e-5 * wind_speed * d_tr * d_tr * pa_pr
+        + -5.06004592e-6 * air_temperature * wind_speed * d_tr * d_tr * pa_pr
+        + 1.14325367e-7
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * d_tr
+        * d_tr
+        * pa_pr
+        + 2.53016723e-6 * wind_speed * wind_speed * d_tr * d_tr * pa_pr
+        + -1.72857035e-8
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * d_tr
+        * d_tr
+        * pa_pr
+        + -3.95079398e-8 * wind_speed * wind_speed * wind_speed * d_tr * d_tr * pa_pr
+        + -3.59413173e-7 * d_tr * d_tr * d_tr * pa_pr
+        + 7.04388046e-7 * air_temperature * d_tr * d_tr * d_tr * pa_pr
+        + -1.89309167e-8
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * d_tr
+        * d_tr
+        * pa_pr
+        + -4.79768731e-7 * wind_speed * d_tr * d_tr * d_tr * pa_pr
+        + 7.96079978e-9 * air_temperature * wind_speed * d_tr * d_tr * d_tr * pa_pr
+        + 1.62897058e-9 * wind_speed * wind_speed * d_tr * d_tr * d_tr * pa_pr
+        + 3.94367674e-8 * d_tr * d_tr * d_tr * d_tr * pa_pr
+        + -1.18566247e-9 * air_temperature * d_tr * d_tr * d_tr * d_tr * pa_pr
+        + 3.34678041e-10 * wind_speed * d_tr * d_tr * d_tr * d_tr * pa_pr
+        + -1.15606447e-10 * d_tr * d_tr * d_tr * d_tr * d_tr * pa_pr
+        + -2.80626406 * pa_pr * pa_pr
+        + 0.548712484 * air_temperature * pa_pr * pa_pr
+        + -0.00399428410 * air_temperature * air_temperature * pa_pr * pa_pr
+        + -9.54009191e-4
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * pa_pr
+        * pa_pr
+        + 1.93090978e-5
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * pa_pr
+        * pa_pr
+        + -0.308806365 * wind_speed * pa_pr * pa_pr
+        + 0.0116952364 * air_temperature * wind_speed * pa_pr * pa_pr
+        + 4.95271903e-4 * air_temperature * air_temperature * wind_speed * pa_pr * pa_pr
+        + -1.90710882e-5
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * pa_pr
+        * pa_pr
+        + 0.00210787756 * wind_speed * wind_speed * pa_pr * pa_pr
+        + -6.98445738e-4 * air_temperature * wind_speed * wind_speed * pa_pr * pa_pr
+        + 2.30109073e-5
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        * pa_pr
+        + 4.17856590e-4 * wind_speed * wind_speed * wind_speed * pa_pr * pa_pr
+        + -1.27043871e-5
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        * pa_pr
+        + -3.04620472e-6
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        * pa_pr
+        + 0.0514507424 * d_tr * pa_pr * pa_pr
+        + -0.00432510997 * air_temperature * d_tr * pa_pr * pa_pr
+        + 8.99281156e-5 * air_temperature * air_temperature * d_tr * pa_pr * pa_pr
+        + -7.14663943e-7
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * pa_pr
+        * pa_pr
+        + -2.66016305e-4 * wind_speed * d_tr * pa_pr * pa_pr
+        + 2.63789586e-4 * air_temperature * wind_speed * d_tr * pa_pr * pa_pr
+        + -7.01199003e-6
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * d_tr
+        * pa_pr
+        * pa_pr
+        + -1.06823306e-4 * wind_speed * wind_speed * d_tr * pa_pr * pa_pr
+        + 3.61341136e-6
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * d_tr
+        * pa_pr
+        * pa_pr
+        + 2.29748967e-7 * wind_speed * wind_speed * wind_speed * d_tr * pa_pr * pa_pr
+        + 3.04788893e-4 * d_tr * d_tr * pa_pr * pa_pr
+        + -6.42070836e-5 * air_temperature * d_tr * d_tr * pa_pr * pa_pr
+        + 1.16257971e-6
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * d_tr
+        * pa_pr
+        * pa_pr
+        + 7.68023384e-6 * wind_speed * d_tr * d_tr * pa_pr * pa_pr
+        + -5.47446896e-7 * air_temperature * wind_speed * d_tr * d_tr * pa_pr * pa_pr
+        + -3.59937910e-8 * wind_speed * wind_speed * d_tr * d_tr * pa_pr * pa_pr
+        + -4.36497725e-6 * d_tr * d_tr * d_tr * pa_pr * pa_pr
+        + 1.68737969e-7 * air_temperature * d_tr * d_tr * d_tr * pa_pr * pa_pr
+        + 2.67489271e-8 * wind_speed * d_tr * d_tr * d_tr * pa_pr * pa_pr
+        + 3.23926897e-9 * d_tr * d_tr * d_tr * d_tr * pa_pr * pa_pr
+        + -0.0353874123 * pa_pr * pa_pr * pa_pr
+        + -0.221201190 * air_temperature * pa_pr * pa_pr * pa_pr
+        + 0.0155126038 * air_temperature * air_temperature * pa_pr * pa_pr * pa_pr
+        + -2.63917279e-4
+        * air_temperature
+        * air_temperature
+        * air_temperature
+        * pa_pr
+        * pa_pr
+        * pa_pr
+        + 0.0453433455 * wind_speed * pa_pr * pa_pr * pa_pr
+        + -0.00432943862 * air_temperature * wind_speed * pa_pr * pa_pr * pa_pr
+        + 1.45389826e-4
+        * air_temperature
+        * air_temperature
+        * wind_speed
+        * pa_pr
+        * pa_pr
+        * pa_pr
+        + 2.17508610e-4 * wind_speed * wind_speed * pa_pr * pa_pr * pa_pr
+        + -6.66724702e-5
+        * air_temperature
+        * wind_speed
+        * wind_speed
+        * pa_pr
+        * pa_pr
+        * pa_pr
+        + 3.33217140e-5 * wind_speed * wind_speed * wind_speed * pa_pr * pa_pr * pa_pr
+        + -0.00226921615 * d_tr * pa_pr * pa_pr * pa_pr
+        + 3.80261982e-4 * air_temperature * d_tr * pa_pr * pa_pr * pa_pr
+        + -5.45314314e-9
+        * air_temperature
+        * air_temperature
+        * d_tr
+        * pa_pr
+        * pa_pr
+        * pa_pr
+        + -7.96355448e-4 * wind_speed * d_tr * pa_pr * pa_pr * pa_pr
+        + 2.53458034e-5 * air_temperature * wind_speed * d_tr * pa_pr * pa_pr * pa_pr
+        + -6.31223658e-6 * wind_speed * wind_speed * d_tr * pa_pr * pa_pr * pa_pr
+        + 3.02122035e-4 * d_tr * d_tr * pa_pr * pa_pr * pa_pr
+        + -4.77403547e-6 * air_temperature * d_tr * d_tr * pa_pr * pa_pr * pa_pr
+        + 1.73825715e-6 * wind_speed * d_tr * d_tr * pa_pr * pa_pr * pa_pr
+        + -4.09087898e-7 * d_tr * d_tr * d_tr * pa_pr * pa_pr * pa_pr
+        + 0.614155345 * pa_pr * pa_pr * pa_pr * pa_pr
+        + -0.0616755931 * air_temperature * pa_pr * pa_pr * pa_pr * pa_pr
+        + 0.00133374846
+        * air_temperature
+        * air_temperature
+        * pa_pr
+        * pa_pr
+        * pa_pr
+        * pa_pr
+        + 0.00355375387 * wind_speed * pa_pr * pa_pr * pa_pr * pa_pr
+        + -5.13027851e-4 * air_temperature * wind_speed * pa_pr * pa_pr * pa_pr * pa_pr
+        + 1.02449757e-4 * wind_speed * wind_speed * pa_pr * pa_pr * pa_pr * pa_pr
+        + -0.00148526421 * d_tr * pa_pr * pa_pr * pa_pr * pa_pr
+        + -4.11469183e-5 * air_temperature * d_tr * pa_pr * pa_pr * pa_pr * pa_pr
+        + -6.80434415e-6 * wind_speed * d_tr * pa_pr * pa_pr * pa_pr * pa_pr
+        + -9.77675906e-6 * d_tr * d_tr * pa_pr * pa_pr * pa_pr * pa_pr
+        + 0.0882773108 * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
+        + -0.00301859306 * air_temperature * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
+        + 0.00104452989 * wind_speed * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
+        + 2.47090539e-4 * d_tr * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
+        + 0.00148348065 * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
+    )
+
+    return utci_approx
+
+
+def _utci_collection(
+    air_temperature: HourlyContinuousCollection,
+    mean_radiant_temperature: HourlyContinuousCollection,
+    wind_speed: HourlyContinuousCollection,
+    relative_humidity: HourlyContinuousCollection,
+) -> HourlyContinuousCollection:
+    """Calculate UTCI for a Ladybug HourlyContinuousCollection inputs.
+
+    Args:
+        air_temperature (HourlyContinuousCollection):
+            A ladybug HourlyContinuousCollection describing air temperature in C.
+        mean_radiant_temperature (HourlyContinuousCollection):
+            A ladybug HourlyContinuousCollection describing mean radiant temperature in C.
+        wind_speed (HourlyContinuousCollection):
+            A ladybug HourlyContinuousCollection describing wind speed at 10m above ground in m/s.
+        relative_humidity (HourlyContinuousCollection):
+            A ladybug HourlyContinuousCollection describing relative humidity [0-100].
+
+    Returns:
+        HourlyContinuousCollection:
+            The calculated UTCI for the given inputs.
+    """
+
+    return UTCI(
+        air_temperature=air_temperature,
+        rel_humidity=relative_humidity,
+        rad_temperature=mean_radiant_temperature,
+        wind_speed=wind_speed,
+    ).universal_thermal_climate_index
+
+
+@bhom_analytics()
+def utci_parallel(
+    ta: np.ndarray, tr: np.ndarray, vel: np.ndarray, rh: np.ndarray
+) -> np.ndarray:
+    """Calculate UTCI a bit faster!
+
+    Args:
+        ta (np.ndarray):
+            Air temperature [C]
+        tr (np.ndarray):
+            Mean radiant temperature [C]
+        vel (np.ndarray):
+            Wind speed 10m above ground level [m/s]
+        rh (np.ndarray):
+            Relative humidity [%]
+
+    Returns:
+        np.ndarray:
+            UTCI values.
+    """
+
+    if ta.shape != tr.shape != vel.shape != rh.shape:
+        raise ValueError("Input arrays must be of the same shape.")
+    if any(
+        [
+            len(ta.shape) != 2,
+            len(tr.shape) != 2,
+            len(vel.shape) != 2,
+            len(rh.shape) != 2,
+        ]
+    ):
+        raise ValueError("Input arrays must be of shape (n, m).")
+
+    with ProcessPoolExecutor() as executor:
+        results = list(
+            tqdm(
+                executor.map(_utci_ndarray, ta, tr, vel, rh),
+                total=len(ta),
+                desc="Calculating UTCI: ",
+            )
+        )
+
+    return np.concatenate(results).reshape(ta.shape)
+
+
+@bhom_analytics()
+def utci(
+    air_temperature: HourlyContinuousCollection
+    | pd.DataFrame
+    | pd.Series
+    | npt.NDArray[np.float64],
+    relative_humidity: HourlyContinuousCollection
+    | pd.DataFrame
+    | pd.Series
+    | npt.NDArray[np.float64],
+    mean_radiant_temperature: HourlyContinuousCollection
+    | pd.DataFrame
+    | pd.Series
+    | npt.NDArray[np.float64],
+    wind_speed: HourlyContinuousCollection
+    | pd.DataFrame
+    | pd.Series
+    | npt.NDArray[np.float64],
+) -> HourlyContinuousCollection | pd.DataFrame | npt.NDArray[np.float64]:
+    """Return the UTCI for the given inputs.
+
+    Arguments:
+        air_temperature: HourlyContinuousCollection | pd.DataFrame | pd.Series | NDArray[np.float64]:
+            The air temperature in Celsius.
+        relative_humidity: HourlyContinuousCollection | pd.DataFrame | pd.Series | NDArray[np.float64]:
+            The relative humidity in percentage.
+        mean_radiant_temperature: HourlyContinuousCollection | pd.DataFrame | pd.Series | NDArray[np.float64]:
+            The mean radiant temperature in Celsius.
+        wind_speed: HourlyContinuousCollection | pd.DataFrame | pd.Series | NDArray[np.float64]:
+            The wind speed in m/s.
+
+    Returns:
+        HourlyContinuousCollection:
+            The calculated UTCI based on the shelter configuration for the given typology.
+    """
+    _inputs = [
+        air_temperature,
+        relative_humidity,
+        mean_radiant_temperature,
+        wind_speed,
+    ]
+
+    if all((isinstance(i, HourlyContinuousCollection) for i in _inputs)):
+        # print("Calculating UTCI - Ladybug HourlyContinuousCollection")
+        return UTCI(
+            air_temperature=air_temperature,
+            rel_humidity=relative_humidity,
+            rad_temperature=mean_radiant_temperature,
+            wind_speed=wind_speed,
+        ).universal_thermal_climate_index
+
+    if all((isinstance(i, (float, int)) for i in _inputs)):
+        # print("Calculating UTCI - float/int")
+        return _utci_ndarray(
+            air_temperature=air_temperature,
+            relative_humidity=relative_humidity,
+            mean_radiant_temperature=mean_radiant_temperature,
+            wind_speed=np.clip([wind_speed], 0, 17)[0],
+        )
+
+    if all((isinstance(i, pd.DataFrame) for i in _inputs)):
+        # print("Calculating UTCI - pandas DataFrame")
+        return pd.DataFrame(
+            _utci_ndarray(
+                air_temperature=air_temperature.values,
+                relative_humidity=relative_humidity.values,
+                mean_radiant_temperature=mean_radiant_temperature.values,
+                wind_speed=wind_speed.clip(lower=0, upper=17).values,
+            ),
+            columns=_inputs[0].columns
+            if len(_inputs[0].columns) > 1
+            else ["Universal Thermal Climate Index (C)"],
+            index=_inputs[0].index,
+        )
+
+    if all((isinstance(i, pd.Series) for i in _inputs)):
+        # print("Calculating UTCI - pandas Series")
+        return pd.Series(
+            _utci_ndarray(
+                air_temperature=air_temperature,
+                relative_humidity=relative_humidity,
+                mean_radiant_temperature=mean_radiant_temperature,
+                wind_speed=wind_speed.clip(lower=0, upper=17),
+            ),
+            name="Universal Thermal Climate Index (C)",
+            index=_inputs[0].index,
+        )
+
+    if all((isinstance(i, (list, tuple)) for i in _inputs)):
+        # print("Calculating UTCI - List/Tuple")
+        return _utci_ndarray(
+            air_temperature=np.array(air_temperature),
+            relative_humidity=np.array(relative_humidity),
+            mean_radiant_temperature=np.array(mean_radiant_temperature),
+            wind_speed=np.clip(np.array(wind_speed), 0, 17),
+        )
+
+    try:
+        # print("Calculating UTCI - np.array")
+        # assume numpy array
+        return _utci_ndarray(
+            air_temperature=air_temperature,
+            relative_humidity=relative_humidity,
+            mean_radiant_temperature=mean_radiant_temperature,
+            wind_speed=np.clip(wind_speed, 0, 17),
+        )
+    except Exception as e:
+        raise ValueError(
+            "No possible means of calculating UTCI from that combination of inputs was found."
+        ) from e
+
+
+@bhom_analytics()
+def compare_monthly_utci(
+    utci_collections: list[HourlyContinuousCollection],
+    utci_categories: CategoricalComfort = UTCI_DEFAULT_CATEGORIES,
+    identifiers: tuple[str] = None,
+    density: bool = False,
+) -> pd.DataFrame:
+    """Create a summary of a comparison between a "baseline" UTCI collection,
+    and another.
+
+    Args:
+        utci_collections (list[HourlyContinuousCollection]):
+            A list of UTCI collections to compare.
+        utci_categories (Categories, optional):
+            A set of categories to use for the comparison.
+        identifiers (tuple[str]):
+            A tuple of identifiers for the baseline and comparable collections.
+        density (bool, optional):
+            Return the proportion of time rather than the number of hours.
+            Defaults to False.
+
+    Returns:
+        pd.DataFrame:
+            A table showing the number of hours in each category for each
+            collection.
+    """
+
+    # ensure each collection given it a UTCI collection
+    if len(utci_collections) < 2:
+        raise ValueError("At least two UTCI collections must be given to compare them.")
+
+    if any(
+        not isinstance(i.header.data_type, LB_UniversalThermalClimateIndex)
+        for i in utci_collections
+    ):
+        raise ValueError("Input collection is not a UTCI collection.")
+
+    if identifiers is None:
+        identifiers = range(len(utci_collections))
+    else:
+        assert len(identifiers) == len(
+            utci_collections
+        ), "The identifiers given must be the same length as the collections given."
+
+    dd = []
+    for i in utci_collections:
+        _df = utci_categories.timeseries_summary_monthly(
+            collection_to_series(i), density=density
+        )
+        _df.columns = [utci_categories.interval_from_bin_name(i) for i in _df.columns]
+        dd.append(_df)
+    df = pd.concat(dd, axis=1, keys=identifiers)
+    df = df.reorder_levels([1, 0], axis=1).sort_index(axis=1)
+    df = df.rename(
+        columns=utci_categories._interval_bin_name  # pylint: disable=protected-access
+    )
+
+    return df
+
+
+@bhom_analytics()
+def shade_benefit_category(
+    unshaded_utci: HourlyContinuousCollection | pd.Series,
+    shaded_utci: HourlyContinuousCollection | pd.Series,
+    comfort_limits: tuple[float] = (9, 26),
+) -> pd.Series:
+    """Determine shade-gap analysis category, indicating where shade is not
+    beneificial.
+
+    Args:
+        unshaded_utci (HourlyContinuousCollection | pd.Series):
+            A dataset containing unshaded UTCI values.
+        shaded_utci (HourlyContinuousCollection | pd.Series):
+            A dataset containing shaded UTCI values.
+        comfort_limits (tuple[float], optional):
+            The range within which "comfort" is achieved. Defaults to (9, 26).
+
+    Returns:
+        pd.Series:
+            A catgorical series indicating shade-benefit.
+
+    """
+
+    # convert to series if not already
+    if isinstance(unshaded_utci, HourlyContinuousCollection):
+        unshaded_utci = collection_to_series(unshaded_utci)
+    if isinstance(shaded_utci, HourlyContinuousCollection):
+        shaded_utci = collection_to_series(shaded_utci)
+
+    if len(unshaded_utci) != len(shaded_utci):
+        raise ValueError(
+            f"Input sizes do not match ({len(unshaded_utci)} != {len(shaded_utci)})"
+        )
+
+    if sum(unshaded_utci == shaded_utci) == len(unshaded_utci):
+        raise ValueError("Input series are identical.")
+
+    # get limits
+    low, high = min(comfort_limits), max(comfort_limits)
+
+    # get distance to comfort (degrees from edge of "comfortable")
+    distance_from_comfort_unshaded = abs(
+        np.where(
+            unshaded_utci < low,
+            unshaded_utci - low,
+            np.where(unshaded_utci > high, unshaded_utci - high, 0),
+        )
+    )
+    distance_from_comfort_shaded = abs(
+        np.where(
+            shaded_utci < low,
+            shaded_utci - low,
+            np.where(shaded_utci > high, shaded_utci - high, 0),
+        )
+    )
+
+    # get boolean mask where comfortable
+    comfortable_unshaded = unshaded_utci.between(low, high)
+    comfortable_shaded = shaded_utci.between(low, high)
+
+    # get masks for each category
+    comfortable_without_shade = comfortable_unshaded
+    comfortable_with_shade = ~comfortable_unshaded & comfortable_shaded
+    shade_has_negative_impact = (
+        distance_from_comfort_unshaded < distance_from_comfort_shaded
+    )
+    shade_has_positive_impact = (
+        distance_from_comfort_unshaded > distance_from_comfort_shaded
+    )
+
+    # construct categorical series
+    shade_categories = np.where(
+        comfortable_without_shade,
+        "Comfortable without shade",
+        np.where(
+            comfortable_with_shade,
+            "Comfortable with shade",
+            np.where(
+                shade_has_negative_impact,
+                "Shade is detrimental",
+                np.where(shade_has_positive_impact, "Shade is beneficial", "Undefined"),
+            ),
+        ),
+    )
+
+    return pd.Series(shade_categories, index=unshaded_utci.index)
+
+
+@bhom_analytics()
+def distance_to_comfortable(
+    utci_value: int
+    | float
+    | list
+    | tuple
+    | pd.Series
+    | pd.DataFrame
+    | HourlyContinuousCollection,
+    comfort_thresholds: tuple[float] = (9, 26),
+    distance_to_comfort_band_centroid: bool = True,
+) -> int | float | list | tuple | pd.Series | pd.DataFrame | HourlyContinuousCollection:
+    """
+    Get the distance between the given value/s and the "comfortable" category.
+
+    Args:
+        utci_value (int | float | list | tuple | pd.Series | pd.DataFrame | HourlyContinuousCollection):
+            A value or set of values representing UTCI temperature.
+        comfort_thresholds (list[float], optional):
+            The comfortable band of UTCI temperatures. Defaults to [9, 26].
+        distance_to_comfort_band_centroid (bool, optional):
+            If True, the distance to the centroid of the comfort band is
+            plotted. If False, the distance to the edge of the comfort band is
+            plotted. Defaults to False.
+
+    Returns:
+        int | float | list | tuple | pd.Series | pd.DataFrame | HourlyContinuousCollection:
+            The original value/s distance from "comfortable"
+
+    """
+
+    if len(comfort_thresholds) != 2:
+        raise ValueError("comfort_thresholds must be a list of length 2.")
+
+    if len(set(comfort_thresholds)) != 2:
+        raise ValueError("comfort_thresholds must contain two unique values.")
+
+    if comfort_thresholds[0] > comfort_thresholds[1]:
+        warnings.warn("comfort_thresholds are not increasing. Swapping the values.")
+
+    low_limit = min(comfort_thresholds)
+    high_limit = max(comfort_thresholds)
+    midpoint = np.mean(comfort_thresholds)
+
+    # get raw values as numpy array for ease of processing
+    original_value = np.atleast_1d(utci_value)
+    name = "Distance to comfortable (C)"
+
+    # calculate distance from "comfort"
+    if not distance_to_comfort_band_centroid:
+        distance = np.where(
+            original_value < low_limit,
+            original_value - low_limit,
+            np.where(original_value > high_limit, original_value - high_limit, 0),
+        )
+    else:
+        distance = np.where(
+            original_value < midpoint,
+            -(midpoint - original_value),
+            original_value - midpoint,
+        )
+
+    if isinstance(utci_value, pd.Series):
+        return pd.Series(distance, index=utci_value.index, name=name)
+
+    if isinstance(utci_value, pd.DataFrame):
+        return pd.DataFrame(
+            distance, index=utci_value.index, columns=utci_value.columns
+        )
+
+    if isinstance(utci_value, HourlyContinuousCollection):
+        if not isinstance(utci_value.header.data_type, LB_UniversalThermalClimateIndex):
+            raise ValueError("Input collection is not a UTCI collection.")
+        return collection_from_series(
+            pd.Series(
+                original_value,
+                index=collection_to_series(utci_value).index,
+                name=name,
+            )
+        )
+
+    if isinstance(utci_value, list):
+        return distance.tolist()
+
+    if isinstance(utci_value, tuple):
+        return tuple(distance.tolist())
+
+    return distance
+
+
+@bhom_analytics()
+def feasible_utci_limits(
+    epw: EPW, include_additional_moisture: float = 0, as_dataframe: bool = False
+) -> tuple[HourlyContinuousCollection] | pd.DataFrame:
+    """Calculate the absolute min/max collections of UTCI based on possible
+    shade, wind and moisture conditions.
+
+    Args:
+        epw (EPW):
+            The EPW object for which limits will be calculated.
+        include_additional_moisture (float, optional):
+            Include the effect of evaporative cooling on the UTCI limits.
+            Default is 0, for no evaporative cooling.
+        as_dataframe (bool):
+            Return the output as a dataframe with two columns, instread of two
+            separate collections.
+
+    Returns:
+        tuple[HourlyContinuousCollection] | pd.DataFrame:
+            The lowest possible UTCI and highest UTCI temperatures for each
+            hour of the year.
+    """
+    mrt_unshaded = OutdoorSolarCal(
+        epw.location,
+        epw.direct_normal_radiation,
+        epw.diffuse_horizontal_radiation,
+        epw.horizontal_infrared_radiation_intensity,
+        epw.dry_bulb_temperature,
+    ).mean_radiant_temperature
+    dbt_evap, rh_evap = np.array(
+        [
+            evaporative_cooling_effect(
+                dry_bulb_temperature=_dbt,
+                relative_humidity=_rh,
+                evaporative_cooling_effectiveness=include_additional_moisture,
+                atmospheric_pressure=_atm,
+            )
+            for _dbt, _rh, _atm in list(
+                zip(
+                    *[
+                        epw.dry_bulb_temperature,
+                        epw.relative_humidity,
+                        epw.atmospheric_station_pressure,
+                    ]
+                )
+            )
+        ]
+    ).T
+    dbt_evap = epw.dry_bulb_temperature.get_aligned_collection(dbt_evap)
+    rh_evap = epw.relative_humidity.get_aligned_collection(rh_evap)
+
+    dbt_rh_options = (
+        [[dbt_evap, rh_evap], [epw.dry_bulb_temperature, epw.relative_humidity]]
+        if include_additional_moisture != 0
+        else [[epw.dry_bulb_temperature, epw.relative_humidity]]
+    )
+
+    utcis = []
+    utcis = []
+    for _dbt, _rh in dbt_rh_options:
+        for _ws in [
+            epw.wind_speed,
+            epw.wind_speed.get_aligned_collection(0),
+            epw.wind_speed * 1.1,
+        ]:
+            for _mrt in [epw.dry_bulb_temperature, mrt_unshaded]:
+                utcis.append(
+                    UTCI(
+                        air_temperature=_dbt,
+                        rad_temperature=_mrt,
+                        rel_humidity=_rh,
+                        wind_speed=_ws,
+                    ).universal_thermal_climate_index,
+                )
+    df = pd.concat([collection_to_series(i) for i in utcis], axis=1)
+    min_utci = collection_from_series(
+        df.min(axis=1).rename("Universal Thermal Climate Index (C)")
+    )
+    max_utci = collection_from_series(
+        df.max(axis=1).rename("Universal Thermal Climate Index (C)")
+    )
+
+    if as_dataframe:
+        return pd.concat(
+            [
+                collection_to_series(min_utci),
+                collection_to_series(max_utci),
+            ],
+            axis=1,
+            keys=["lowest", "highest"],
+        )
+
+    return min_utci, max_utci
+
+
+@bhom_analytics()
+def feasible_utci_category_limits(
+    epw: EPW,
+    include_additional_moisture: float = 0,
+    utci_categories: CategoricalComfort = UTCI_DEFAULT_CATEGORIES,
+    density: bool = False,
+    mask: list[bool] = None,
+):
+    """Calculate the upper and lower proportional limits of UTCI categories
+    based on possible shade, wind and moisture conditions.
+
+    Args:
+        epw (EPW):
+            The EPW object for which limits will be calculated.
+        include_additional_moisture (float, optional):
+            Include the effect of evaporative cooling on the UTCI limits.
+            Default is 0, for no evaporative cooling.
+        utci_categories (Categorical, optional):
+            A set of categories to use for the comparison.
+        density (bool, optional):
+            Return the proportion of time rather than the number of hours.
+            Defaults to False.
+        mask (list[bool], optional):
+            A list of booleans to mask the data. Defaults to None.
+
+    Returns:
+        pd.DataFrame:
+            A table with monthly time binned UTCI data.
+    """
+
+    lims = feasible_utci_limits(
+        epw, include_additional_moisture=include_additional_moisture, as_dataframe=True
+    )
+
+    if mask is not None:
+        lims = lims[mask]
+        if lims.index.month.nunique() != 12:
+            raise ValueError("Masked data include at least one value per month.")
+
+    df = (
+        pd.concat(
+            [
+                utci_categories.timeseries_summary_monthly(
+                    lims.lowest, density=density
+                ),
+                utci_categories.timeseries_summary_monthly(
+                    lims.highest, density=density
+                ),
+            ],
+            axis=1,
+            keys=lims.columns,
+        )
+        .reorder_levels([1, 0], axis=1)
+        .sort_index(axis=1, ascending=[True, False])
+    )
+
+    df.index = [month_abbr[i] for i in df.index]
+
+    return df
+
+
+@bhom_analytics()
+def month_hour_binned(
+    utci_data: pd.Series | HourlyContinuousCollection,
+    month_bins: tuple[tuple[int]] = None,
+    hour_bins: tuple[tuple[int]] = None,
+    month_labels: tuple[str] = None,
+    hour_labels: tuple[str] = None,
+    agg: str = "mean",
+) -> pd.DataFrame:
+    """Create a table with monthly hour binned UTCI data.
+
+    Args:
+        utci_data (pd.Series | HourlyContinuousCollection):
+            A collection of UTCI values.
+        month_bins (tuple[list[int]]):
+            A list of lists of months to group data into.
+        hour_bins (tuple[list[int]]):
+            A list of lists of hours to group data into.
+        month_labels (list[str], optional):
+            A list of labels for the month bins. Defaults to None.
+        hour_labels (list[str], optional):
+            A list of labels for the hour bins. Defaults to None.
+        agg (str, optional):
+            The aggregation method to use for the data within each bin.
+            Defaults to "mean".
+
+    Returns:
+        pd.DataFrame:
+            A table with monthly time binned UTCI data.
+    """
+
+    # check the utci_data is either a pd.Series or a HourlyContinuousCollection
+    if not isinstance(utci_data, (pd.Series, HourlyContinuousCollection)):
+        raise TypeError(
+            "utci_data must be either a pandas.Series or a ladybug.datacollection.HourlyContinuousCollection"
+        )
+
+    if isinstance(utci_data, HourlyContinuousCollection):
+        utci_data = collection_to_series(utci_data)
+
+    df = month_hour_binned_series(
+        series=utci_data,
+        month_bins=month_bins,
+        hour_bins=hour_bins,
+        month_labels=month_labels,
+        hour_labels=hour_labels,
+        agg=agg,
+    )
+
+    return df
+
+
+@bhom_analytics()
+def met_rate_adjustment(
+    utci_collection: HourlyContinuousCollection, met: float
+) -> HourlyContinuousCollection:
+    """Adjust a UTCI data collection using a target met-rate.
+
+    References:
+        This method uses the relationshp between UTCI and MET rate described in
+        LINDNER-CENDROWSKA, Katarzyna and BRÖDE, Peter, 2021. The evaluation of
+        biothermal conditions for various forms of climatic therapy based on
+        UTCI adjusted for activity [online]. 2021. IGiPZ PAN.
+        [Accessed 27 October 2022]. Available from:
+        http://rcin.org.pl/igipz/Content/194924/PDF/WA51_229409_r2021-t94-no2_G-Polonica-Linder.pdf.
+
+    +---------------------------------------------------------------+----------------------+
+    | Activity                                                      | Metabolic rate (MET) |
+    +---------------------------------------------------------------+----------------------+
+    | Neutral (resting in sitting or standing position)             | 1.1                  |
+    +---------------------------------------------------------------+----------------------+
+    | Slow walk (on even path, without load, at 3-4 km·h-1)         | 2.3                  |
+    | (default for UTCI calculation)                                |                      |
+    +---------------------------------------------------------------+----------------------+
+    | Fast walk (on even path, without load, at ~5 km·h-1)          | 3.4                  |
+    +---------------------------------------------------------------+----------------------+
+    | Marching (on even path, without load, at ~5.5 km·h-1)         | 4.0                  |
+    +---------------------------------------------------------------+----------------------+
+    | Bicycling (for pleasure, on flat terrain, at < 16 km·h-1)     | 4.0                  |
+    +---------------------------------------------------------------+----------------------+
+    | Nordic walking (for exercise, on flat terrain, at 5-6 km·h-1) | 4.8                  |
+    +---------------------------------------------------------------+----------------------+
+
+    Args:
+        utci_collection (HourlyContinuousCollection):
+            A UTCI data collection.
+        met (float, optional):
+            The metabolic rate to apply to this data collection.
+
+    Returns:
+        HourlyContinuousCollection:
+            An adjusted UTCI data collection.
+    """
+
+    if met < 1.1:
+        raise ValueError(
+            "met_rate must be >= 1.1 (representative of a human body at rest)."
+        )
+    if met > 4.8:
+        raise ValueError(
+            "met_rate must be <= 4.8 (representative of a exercise at 5-6km·h-1)."
+        )
+
+    # data below extracted from https://doi.org/10.7163/GPol.0199 in the format {MET: [UTCI, ΔUTCI]}
+    data = {
+        4.8: [
+            [-50, 71.81581439393939],
+            [-46.47239263803681, 71.5127840909091],
+            [-42.94478527607362, 71.36126893939394],
+            [-38.95705521472392, 70.14914772727273],
+            [-34.96932515337423, 67.421875],
+            [-30.521472392638035, 64.69460227272728],
+            [-26.993865030674847, 61.66429924242425],
+            [-23.006134969325153, 58.33096590909091],
+            [-17.944785276073617, 52.87642045454545],
+            [-12.576687116564415, 48.63399621212122],
+            [-6.441717791411044, 43.63399621212122],
+            [-1.380368098159508, 40.300662878787875],
+            [4.29447852760736, 35.755208333333336],
+            [9.815950920245399, 30.906723484848484],
+            [15.490797546012274, 26.512784090909093],
+            [23.00613496932516, 20.755208333333336],
+            [28.06748466257669, 16.967329545454547],
+            [30.98159509202455, 15.452178030303031],
+            [35.889570552147234, 12.573390151515156],
+            [42.63803680981596, 9.846117424242422],
+            [50, 6.967329545454547],
+        ],
+        4.0: [
+            [-50, 46.05823863636364],
+            [-47.69938650306749, 47.421875],
+            [-45.0920245398773, 48.63399621212122],
+            [-43.86503067484662, 50.60369318181819],
+            [-40.1840490797546, 51.96732954545455],
+            [-36.04294478527608, 51.36126893939394],
+            [-33.74233128834356, 50.906723484848484],
+            [-30.67484662576687, 49.543087121212125],
+            [-27.300613496932513, 47.87642045454545],
+            [-24.846625766871163, 46.36126893939394],
+            [-23.159509202453986, 44.99763257575758],
+            [-20.39877300613497, 42.87642045454545],
+            [-16.871165644171782, 39.69460227272728],
+            [-14.11042944785276, 37.573390151515156],
+            [-11.04294478527607, 35.60369318181819],
+            [-7.515337423312886, 34.24005681818182],
+            [-3.374233128834355, 32.27035984848485],
+            [1.0736196319018418, 29.84611742424243],
+            [4.141104294478531, 28.02793560606061],
+            [7.055214723926383, 25.45217803030303],
+            [9.969325153374236, 23.482481060606062],
+            [13.036809815950924, 21.664299242424242],
+            [16.41104294478528, 19.543087121212125],
+            [19.631901840490798, 17.27035984848485],
+            [22.852760736196316, 14.846117424242422],
+            [26.22699386503068, 12.724905303030305],
+            [29.601226993865026, 11.664299242424242],
+            [33.28220858895706, 9.846117424242422],
+            [36.65644171779141, 8.785511363636367],
+            [39.87730061349693, 8.330965909090907],
+            [42.63803680981596, 7.421875],
+            [46.012269938650306, 6.664299242424242],
+            [50, 5.452178030303031],
+        ],
+        3.4: [
+            [-50, 27.724905303030305],
+            [-47.239263803680984, 28.785511363636367],
+            [-44.47852760736196, 29.543087121212125],
+            [-41.717791411042946, 30.45217803030303],
+            [-38.34355828220859, 31.05823863636364],
+            [-35.2760736196319, 31.05823863636364],
+            [-33.43558282208589, 31.361268939393938],
+            [-30.98159509202454, 31.815814393939398],
+            [-28.220858895705522, 31.967329545454547],
+            [-26.380368098159508, 31.05823863636364],
+            [-24.846625766871163, 30.300662878787882],
+            [-23.006134969325153, 29.694602272727273],
+            [-21.165644171779142, 28.482481060606062],
+            [-18.558282208588956, 26.967329545454547],
+            [-16.411042944785272, 25.755208333333336],
+            [-14.11042944785276, 24.39157196969697],
+            [-11.65644171779141, 23.02793560606061],
+            [-8.742331288343557, 23.179450757575758],
+            [-5.828220858895705, 23.330965909090914],
+            [-2.4539877300613497, 22.573390151515156],
+            [0.6134969325153392, 21.361268939393938],
+            [3.374233128834355, 19.846117424242422],
+            [6.74846625766871, 17.876420454545453],
+            [9.662576687116562, 16.512784090909093],
+            [12.883435582822088, 14.846117424242422],
+            [16.104294478527606, 14.088541666666664],
+            [19.631901840490798, 11.815814393939398],
+            [22.085889570552155, 10.300662878787882],
+            [24.84662576687117, 8.785511363636367],
+            [28.52760736196319, 8.027935606060609],
+            [31.44171779141105, 7.1188446969696955],
+            [34.20245398773007, 6.058238636363637],
+            [37.269938650306756, 5.755208333333336],
+            [40.03067484662577, 5.452178030303031],
+            [42.94478527607362, 4.846117424242426],
+            [46.012269938650306, 4.088541666666668],
+            [50, 3.9370265151515156],
+        ],
+        2.3: [
+            [-50, -0.15151515151515227],
+            [50, 0],
+        ],
+        1.1: [
+            [-50, -27.87878787878788],
+            [-48.15950920245399, -26.96969696969697],
+            [-45.858895705521476, -26.21212121212121],
+            [-44.6319018404908, -24.09090909090909],
+            [-42.484662576687114, -21.96969696969697],
+            [-39.57055214723926, -19.848484848484848],
+            [-36.04294478527608, -19.242424242424242],
+            [-32.20858895705521, -20],
+            [-29.447852760736197, -21.363636363636363],
+            [-25.460122699386503, -23.03030303030303],
+            [-22.54601226993865, -24.848484848484848],
+            [-19.32515337423313, -26.666666666666668],
+            [-16.257668711656443, -28.484848484848484],
+            [-13.34355828220859, -30.151515151515152],
+            [-9.509202453987726, -30.90909090909091],
+            [-5.981595092024541, -31.060606060606062],
+            [-3.067484662576689, -29.393939393939394],
+            [-1.380368098159508, -27.272727272727273],
+            [0, -24.848484848484848],
+            [1.0736196319018418, -21.666666666666668],
+            [2.760736196319016, -18.78787878787879],
+            [4.141104294478531, -15.909090909090908],
+            [6.74846625766871, -13.484848484848484],
+            [9.969325153374236, -11.818181818181818],
+            [13.190184049079754, -11.363636363636363],
+            [16.257668711656436, -9.242424242424242],
+            [19.785276073619627, -9.696969696969697],
+            [23.00613496932516, -10.303030303030303],
+            [26.380368098159508, -9.242424242424242],
+            [29.447852760736197, -7.272727272727273],
+            [32.515337423312886, -6.363636363636363],
+            [35.582822085889575, -5.303030303030301],
+            [38.650306748466264, -5.151515151515152],
+            [41.25766871165645, -4.3939393939393945],
+            [44.018404907975466, -3.787878787878789],
+            [46.31901840490798, -3.333333333333332],
+            [50, -3.6363636363636367],
+        ],
+    }
+
+    matrix = []
+    for k, v in data.items():
+        x, y = np.array(v).T
+        f = interp1d(x, y)
+        new_x = np.linspace(-50, 50, 1000)
+        new_y = f(new_x)
+        matrix.append(pd.Series(index=new_x, data=new_y, name=k))
+    met_rate, utci_val, utci_delta = (
+        pd.concat(matrix, axis=1).unstack().reset_index().values.T
+    )
+
+    # create 2d interpolator between [MET, UTCI] and [ΔUTCI]
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        forecaster = interp2d(met_rate, utci_val, utci_delta)
+
+    # Calculate ΔUTCI
+    original_utci = collection_to_series(utci_collection)
+    utci_delta = [forecaster(met, i)[0] for i in original_utci.values]
+
+    return collection_from_series(original_utci + utci_delta)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci/calculate.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci/calculate.py
deleted file mode 100644
index 8d9f24dc..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci/calculate.py
+++ /dev/null
@@ -1,417 +0,0 @@
-from concurrent.futures import ProcessPoolExecutor
-from typing import List, Tuple, Union
-
-import numpy as np
-import numpy.typing as npt
-import pandas as pd
-from ladybug.datacollection import HourlyContinuousCollection
-from ladybug_comfort.collection.utci import UTCI
-from tqdm import tqdm
-
-
-def utci_vectorised(
-    ta: np.ndarray, tr: np.ndarray, vel: np.ndarray, rh: np.ndarray
-) -> np.ndarray:
-    """This method is a vectorised version of the universal_thermal_climate_index method defined in ladybug-tools
-    https://github.com/ladybug-tools/ladybug-comfort/blob/master/ladybug_comfort/utci.py
-
-    Args:
-        ta (np.ndarray): Air temperature [C]
-        tr (np.ndarray): Mean radiant temperature [C]
-        vel (np.ndarray): Wind speed 10 m above ground level [m/s]
-        rh (np.ndarray): Relative humidity [%]
-
-    Returns:
-        np.ndarray: The Universal Thermal Climate Index (UTCI) for the input conditions as approximated by a 4-D polynomial
-    """
-
-    g = (
-        -2836.5744,
-        -6028.076559,
-        19.54263612,
-        -0.02737830188,
-        0.000016261698,
-        7.0229056e-10,
-        -1.8680009e-13,
-    )
-    tk = ta + 273.15  # air temp in K
-    es = 2.7150305 * np.log(tk)
-    for i, x in enumerate(g):
-        es = es + (x * (tk ** (i - 2)))
-    es = np.exp(es) * 0.01
-    eh_pa = es * (rh / 100.0)  # partial vapor pressure
-    pa_pr = eh_pa / 10.0  # convert vapour pressure to kPa
-    d_tr = tr - ta  # difference between radiant and air temperature
-
-    utci_approx = (
-        ta
-        + 0.607562052
-        + -0.0227712343 * ta
-        + 8.06470249e-4 * ta * ta
-        + -1.54271372e-4 * ta * ta * ta
-        + -3.24651735e-6 * ta * ta * ta * ta
-        + 7.32602852e-8 * ta * ta * ta * ta * ta
-        + 1.35959073e-9 * ta * ta * ta * ta * ta * ta
-        + -2.25836520 * vel
-        + 0.0880326035 * ta * vel
-        + 0.00216844454 * ta * ta * vel
-        + -1.53347087e-5 * ta * ta * ta * vel
-        + -5.72983704e-7 * ta * ta * ta * ta * vel
-        + -2.55090145e-9 * ta * ta * ta * ta * ta * vel
-        + -0.751269505 * vel * vel
-        + -0.00408350271 * ta * vel * vel
-        + -5.21670675e-5 * ta * ta * vel * vel
-        + 1.94544667e-6 * ta * ta * ta * vel * vel
-        + 1.14099531e-8 * ta * ta * ta * ta * vel * vel
-        + 0.158137256 * vel * vel * vel
-        + -6.57263143e-5 * ta * vel * vel * vel
-        + 2.22697524e-7 * ta * ta * vel * vel * vel
-        + -4.16117031e-8 * ta * ta * ta * vel * vel * vel
-        + -0.0127762753 * vel * vel * vel * vel
-        + 9.66891875e-6 * ta * vel * vel * vel * vel
-        + 2.52785852e-9 * ta * ta * vel * vel * vel * vel
-        + 4.56306672e-4 * vel * vel * vel * vel * vel
-        + -1.74202546e-7 * ta * vel * vel * vel * vel * vel
-        + -5.91491269e-6 * vel * vel * vel * vel * vel * vel
-        + 0.398374029 * d_tr
-        + 1.83945314e-4 * ta * d_tr
-        + -1.73754510e-4 * ta * ta * d_tr
-        + -7.60781159e-7 * ta * ta * ta * d_tr
-        + 3.77830287e-8 * ta * ta * ta * ta * d_tr
-        + 5.43079673e-10 * ta * ta * ta * ta * ta * d_tr
-        + -0.0200518269 * vel * d_tr
-        + 8.92859837e-4 * ta * vel * d_tr
-        + 3.45433048e-6 * ta * ta * vel * d_tr
-        + -3.77925774e-7 * ta * ta * ta * vel * d_tr
-        + -1.69699377e-9 * ta * ta * ta * ta * vel * d_tr
-        + 1.69992415e-4 * vel * vel * d_tr
-        + -4.99204314e-5 * ta * vel * vel * d_tr
-        + 2.47417178e-7 * ta * ta * vel * vel * d_tr
-        + 1.07596466e-8 * ta * ta * ta * vel * vel * d_tr
-        + 8.49242932e-5 * vel * vel * vel * d_tr
-        + 1.35191328e-6 * ta * vel * vel * vel * d_tr
-        + -6.21531254e-9 * ta * ta * vel * vel * vel * d_tr
-        + -4.99410301e-6 * vel * vel * vel * vel * d_tr
-        + -1.89489258e-8 * ta * vel * vel * vel * vel * d_tr
-        + 8.15300114e-8 * vel * vel * vel * vel * vel * d_tr
-        + 7.55043090e-4 * d_tr * d_tr
-        + -5.65095215e-5 * ta * d_tr * d_tr
-        + -4.52166564e-7 * ta * ta * d_tr * d_tr
-        + 2.46688878e-8 * ta * ta * ta * d_tr * d_tr
-        + 2.42674348e-10 * ta * ta * ta * ta * d_tr * d_tr
-        + 1.54547250e-4 * vel * d_tr * d_tr
-        + 5.24110970e-6 * ta * vel * d_tr * d_tr
-        + -8.75874982e-8 * ta * ta * vel * d_tr * d_tr
-        + -1.50743064e-9 * ta * ta * ta * vel * d_tr * d_tr
-        + -1.56236307e-5 * vel * vel * d_tr * d_tr
-        + -1.33895614e-7 * ta * vel * vel * d_tr * d_tr
-        + 2.49709824e-9 * ta * ta * vel * vel * d_tr * d_tr
-        + 6.51711721e-7 * vel * vel * vel * d_tr * d_tr
-        + 1.94960053e-9 * ta * vel * vel * vel * d_tr * d_tr
-        + -1.00361113e-8 * vel * vel * vel * vel * d_tr * d_tr
-        + -1.21206673e-5 * d_tr * d_tr * d_tr
-        + -2.18203660e-7 * ta * d_tr * d_tr * d_tr
-        + 7.51269482e-9 * ta * ta * d_tr * d_tr * d_tr
-        + 9.79063848e-11 * ta * ta * ta * d_tr * d_tr * d_tr
-        + 1.25006734e-6 * vel * d_tr * d_tr * d_tr
-        + -1.81584736e-9 * ta * vel * d_tr * d_tr * d_tr
-        + -3.52197671e-10 * ta * ta * vel * d_tr * d_tr * d_tr
-        + -3.36514630e-8 * vel * vel * d_tr * d_tr * d_tr
-        + 1.35908359e-10 * ta * vel * vel * d_tr * d_tr * d_tr
-        + 4.17032620e-10 * vel * vel * vel * d_tr * d_tr * d_tr
-        + -1.30369025e-9 * d_tr * d_tr * d_tr * d_tr
-        + 4.13908461e-10 * ta * d_tr * d_tr * d_tr * d_tr
-        + 9.22652254e-12 * ta * ta * d_tr * d_tr * d_tr * d_tr
-        + -5.08220384e-9 * vel * d_tr * d_tr * d_tr * d_tr
-        + -2.24730961e-11 * ta * vel * d_tr * d_tr * d_tr * d_tr
-        + 1.17139133e-10 * vel * vel * d_tr * d_tr * d_tr * d_tr
-        + 6.62154879e-10 * d_tr * d_tr * d_tr * d_tr * d_tr
-        + 4.03863260e-13 * ta * d_tr * d_tr * d_tr * d_tr * d_tr
-        + 1.95087203e-12 * vel * d_tr * d_tr * d_tr * d_tr * d_tr
-        + -4.73602469e-12 * d_tr * d_tr * d_tr * d_tr * d_tr * d_tr
-        + 5.12733497 * pa_pr
-        + -0.312788561 * ta * pa_pr
-        + -0.0196701861 * ta * ta * pa_pr
-        + 9.99690870e-4 * ta * ta * ta * pa_pr
-        + 9.51738512e-6 * ta * ta * ta * ta * pa_pr
-        + -4.66426341e-7 * ta * ta * ta * ta * ta * pa_pr
-        + 0.548050612 * vel * pa_pr
-        + -0.00330552823 * ta * vel * pa_pr
-        + -0.00164119440 * ta * ta * vel * pa_pr
-        + -5.16670694e-6 * ta * ta * ta * vel * pa_pr
-        + 9.52692432e-7 * ta * ta * ta * ta * vel * pa_pr
-        + -0.0429223622 * vel * vel * pa_pr
-        + 0.00500845667 * ta * vel * vel * pa_pr
-        + 1.00601257e-6 * ta * ta * vel * vel * pa_pr
-        + -1.81748644e-6 * ta * ta * ta * vel * vel * pa_pr
-        + -1.25813502e-3 * vel * vel * vel * pa_pr
-        + -1.79330391e-4 * ta * vel * vel * vel * pa_pr
-        + 2.34994441e-6 * ta * ta * vel * vel * vel * pa_pr
-        + 1.29735808e-4 * vel * vel * vel * vel * pa_pr
-        + 1.29064870e-6 * ta * vel * vel * vel * vel * pa_pr
-        + -2.28558686e-6 * vel * vel * vel * vel * vel * pa_pr
-        + -0.0369476348 * d_tr * pa_pr
-        + 0.00162325322 * ta * d_tr * pa_pr
-        + -3.14279680e-5 * ta * ta * d_tr * pa_pr
-        + 2.59835559e-6 * ta * ta * ta * d_tr * pa_pr
-        + -4.77136523e-8 * ta * ta * ta * ta * d_tr * pa_pr
-        + 8.64203390e-3 * vel * d_tr * pa_pr
-        + -6.87405181e-4 * ta * vel * d_tr * pa_pr
-        + -9.13863872e-6 * ta * ta * vel * d_tr * pa_pr
-        + 5.15916806e-7 * ta * ta * ta * vel * d_tr * pa_pr
-        + -3.59217476e-5 * vel * vel * d_tr * pa_pr
-        + 3.28696511e-5 * ta * vel * vel * d_tr * pa_pr
-        + -7.10542454e-7 * ta * ta * vel * vel * d_tr * pa_pr
-        + -1.24382300e-5 * vel * vel * vel * d_tr * pa_pr
-        + -7.38584400e-9 * ta * vel * vel * vel * d_tr * pa_pr
-        + 2.20609296e-7 * vel * vel * vel * vel * d_tr * pa_pr
-        + -7.32469180e-4 * d_tr * d_tr * pa_pr
-        + -1.87381964e-5 * ta * d_tr * d_tr * pa_pr
-        + 4.80925239e-6 * ta * ta * d_tr * d_tr * pa_pr
-        + -8.75492040e-8 * ta * ta * ta * d_tr * d_tr * pa_pr
-        + 2.77862930e-5 * vel * d_tr * d_tr * pa_pr
-        + -5.06004592e-6 * ta * vel * d_tr * d_tr * pa_pr
-        + 1.14325367e-7 * ta * ta * vel * d_tr * d_tr * pa_pr
-        + 2.53016723e-6 * vel * vel * d_tr * d_tr * pa_pr
-        + -1.72857035e-8 * ta * vel * vel * d_tr * d_tr * pa_pr
-        + -3.95079398e-8 * vel * vel * vel * d_tr * d_tr * pa_pr
-        + -3.59413173e-7 * d_tr * d_tr * d_tr * pa_pr
-        + 7.04388046e-7 * ta * d_tr * d_tr * d_tr * pa_pr
-        + -1.89309167e-8 * ta * ta * d_tr * d_tr * d_tr * pa_pr
-        + -4.79768731e-7 * vel * d_tr * d_tr * d_tr * pa_pr
-        + 7.96079978e-9 * ta * vel * d_tr * d_tr * d_tr * pa_pr
-        + 1.62897058e-9 * vel * vel * d_tr * d_tr * d_tr * pa_pr
-        + 3.94367674e-8 * d_tr * d_tr * d_tr * d_tr * pa_pr
-        + -1.18566247e-9 * ta * d_tr * d_tr * d_tr * d_tr * pa_pr
-        + 3.34678041e-10 * vel * d_tr * d_tr * d_tr * d_tr * pa_pr
-        + -1.15606447e-10 * d_tr * d_tr * d_tr * d_tr * d_tr * pa_pr
-        + -2.80626406 * pa_pr * pa_pr
-        + 0.548712484 * ta * pa_pr * pa_pr
-        + -0.00399428410 * ta * ta * pa_pr * pa_pr
-        + -9.54009191e-4 * ta * ta * ta * pa_pr * pa_pr
-        + 1.93090978e-5 * ta * ta * ta * ta * pa_pr * pa_pr
-        + -0.308806365 * vel * pa_pr * pa_pr
-        + 0.0116952364 * ta * vel * pa_pr * pa_pr
-        + 4.95271903e-4 * ta * ta * vel * pa_pr * pa_pr
-        + -1.90710882e-5 * ta * ta * ta * vel * pa_pr * pa_pr
-        + 0.00210787756 * vel * vel * pa_pr * pa_pr
-        + -6.98445738e-4 * ta * vel * vel * pa_pr * pa_pr
-        + 2.30109073e-5 * ta * ta * vel * vel * pa_pr * pa_pr
-        + 4.17856590e-4 * vel * vel * vel * pa_pr * pa_pr
-        + -1.27043871e-5 * ta * vel * vel * vel * pa_pr * pa_pr
-        + -3.04620472e-6 * vel * vel * vel * vel * pa_pr * pa_pr
-        + 0.0514507424 * d_tr * pa_pr * pa_pr
-        + -0.00432510997 * ta * d_tr * pa_pr * pa_pr
-        + 8.99281156e-5 * ta * ta * d_tr * pa_pr * pa_pr
-        + -7.14663943e-7 * ta * ta * ta * d_tr * pa_pr * pa_pr
-        + -2.66016305e-4 * vel * d_tr * pa_pr * pa_pr
-        + 2.63789586e-4 * ta * vel * d_tr * pa_pr * pa_pr
-        + -7.01199003e-6 * ta * ta * vel * d_tr * pa_pr * pa_pr
-        + -1.06823306e-4 * vel * vel * d_tr * pa_pr * pa_pr
-        + 3.61341136e-6 * ta * vel * vel * d_tr * pa_pr * pa_pr
-        + 2.29748967e-7 * vel * vel * vel * d_tr * pa_pr * pa_pr
-        + 3.04788893e-4 * d_tr * d_tr * pa_pr * pa_pr
-        + -6.42070836e-5 * ta * d_tr * d_tr * pa_pr * pa_pr
-        + 1.16257971e-6 * ta * ta * d_tr * d_tr * pa_pr * pa_pr
-        + 7.68023384e-6 * vel * d_tr * d_tr * pa_pr * pa_pr
-        + -5.47446896e-7 * ta * vel * d_tr * d_tr * pa_pr * pa_pr
-        + -3.59937910e-8 * vel * vel * d_tr * d_tr * pa_pr * pa_pr
-        + -4.36497725e-6 * d_tr * d_tr * d_tr * pa_pr * pa_pr
-        + 1.68737969e-7 * ta * d_tr * d_tr * d_tr * pa_pr * pa_pr
-        + 2.67489271e-8 * vel * d_tr * d_tr * d_tr * pa_pr * pa_pr
-        + 3.23926897e-9 * d_tr * d_tr * d_tr * d_tr * pa_pr * pa_pr
-        + -0.0353874123 * pa_pr * pa_pr * pa_pr
-        + -0.221201190 * ta * pa_pr * pa_pr * pa_pr
-        + 0.0155126038 * ta * ta * pa_pr * pa_pr * pa_pr
-        + -2.63917279e-4 * ta * ta * ta * pa_pr * pa_pr * pa_pr
-        + 0.0453433455 * vel * pa_pr * pa_pr * pa_pr
-        + -0.00432943862 * ta * vel * pa_pr * pa_pr * pa_pr
-        + 1.45389826e-4 * ta * ta * vel * pa_pr * pa_pr * pa_pr
-        + 2.17508610e-4 * vel * vel * pa_pr * pa_pr * pa_pr
-        + -6.66724702e-5 * ta * vel * vel * pa_pr * pa_pr * pa_pr
-        + 3.33217140e-5 * vel * vel * vel * pa_pr * pa_pr * pa_pr
-        + -0.00226921615 * d_tr * pa_pr * pa_pr * pa_pr
-        + 3.80261982e-4 * ta * d_tr * pa_pr * pa_pr * pa_pr
-        + -5.45314314e-9 * ta * ta * d_tr * pa_pr * pa_pr * pa_pr
-        + -7.96355448e-4 * vel * d_tr * pa_pr * pa_pr * pa_pr
-        + 2.53458034e-5 * ta * vel * d_tr * pa_pr * pa_pr * pa_pr
-        + -6.31223658e-6 * vel * vel * d_tr * pa_pr * pa_pr * pa_pr
-        + 3.02122035e-4 * d_tr * d_tr * pa_pr * pa_pr * pa_pr
-        + -4.77403547e-6 * ta * d_tr * d_tr * pa_pr * pa_pr * pa_pr
-        + 1.73825715e-6 * vel * d_tr * d_tr * pa_pr * pa_pr * pa_pr
-        + -4.09087898e-7 * d_tr * d_tr * d_tr * pa_pr * pa_pr * pa_pr
-        + 0.614155345 * pa_pr * pa_pr * pa_pr * pa_pr
-        + -0.0616755931 * ta * pa_pr * pa_pr * pa_pr * pa_pr
-        + 0.00133374846 * ta * ta * pa_pr * pa_pr * pa_pr * pa_pr
-        + 0.00355375387 * vel * pa_pr * pa_pr * pa_pr * pa_pr
-        + -5.13027851e-4 * ta * vel * pa_pr * pa_pr * pa_pr * pa_pr
-        + 1.02449757e-4 * vel * vel * pa_pr * pa_pr * pa_pr * pa_pr
-        + -0.00148526421 * d_tr * pa_pr * pa_pr * pa_pr * pa_pr
-        + -4.11469183e-5 * ta * d_tr * pa_pr * pa_pr * pa_pr * pa_pr
-        + -6.80434415e-6 * vel * d_tr * pa_pr * pa_pr * pa_pr * pa_pr
-        + -9.77675906e-6 * d_tr * d_tr * pa_pr * pa_pr * pa_pr * pa_pr
-        + 0.0882773108 * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
-        + -0.00301859306 * ta * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
-        + 0.00104452989 * vel * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
-        + 2.47090539e-4 * d_tr * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
-        + 0.00148348065 * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr * pa_pr
-    )
-
-    return utci_approx
-
-
-def utci_parallel(
-    ta: np.ndarray, tr: np.ndarray, vel: np.ndarray, rh: np.ndarray
-) -> np.ndarray:
-    """Calculate UTCI a bit faster!
-
-    Args:
-        ta (np.ndarray):
-            Air temperature [C]
-        tr (np.ndarray):
-            Mean radiant temperature [C]
-        vel (np.ndarray):
-            Wind speed 10m above ground level [m/s]
-        rh (np.ndarray):
-            Relative humidity [%]
-
-    Returns:
-        np.ndarray:
-            UTCI values.
-    """
-
-    if ta.shape != tr.shape != vel.shape != rh.shape:
-        raise ValueError("Input arrays must be of the same shape.")
-    if any(
-        [
-            len(ta.shape) != 2,
-            len(tr.shape) != 2,
-            len(vel.shape) != 2,
-            len(rh.shape) != 2,
-        ]
-    ):
-        raise ValueError("Input arrays must be of shape (n, m).")
-
-    with ProcessPoolExecutor() as executor:
-        results = list(
-            tqdm(
-                executor.map(utci_vectorised, ta, tr, vel, rh),
-                total=len(ta),
-                desc="Calculating UTCI: ",
-            )
-        )
-
-    return np.concatenate(results).reshape(ta.shape)
-
-
-def utci(
-    air_temperature: Union[
-        HourlyContinuousCollection,
-        pd.DataFrame,
-        pd.Series,
-        npt.NDArray[np.float64],
-    ],
-    relative_humidity: Union[
-        HourlyContinuousCollection,
-        pd.DataFrame,
-        pd.Series,
-        npt.NDArray[np.float64],
-    ],
-    mean_radiant_temperature: Union[
-        HourlyContinuousCollection,
-        pd.DataFrame,
-        pd.Series,
-        npt.NDArray[np.float64],
-    ],
-    wind_speed: Union[
-        HourlyContinuousCollection,
-        pd.DataFrame,
-        pd.Series,
-        npt.NDArray[np.float64],
-    ],
-) -> Union[HourlyContinuousCollection, pd.DataFrame, npt.NDArray[np.float64]]:
-    """Return the UTCI for the given inputs.
-
-    Arguments:
-        air_temperature: Union[HourlyContinuousCollection, pd.DataFrame, pd.Series, NDArray[np.float64]]:
-            The air temperature in Celsius.
-        relative_humidity: Union[HourlyContinuousCollection, pd.DataFrame, pd.Series, NDArray[np.float64]]:
-            The relative humidity in percentage.
-        mean_radiant_temperature: Union[HourlyContinuousCollection, pd.DataFrame, pd.Series, NDArray[np.float64]]:
-            The mean radiant temperature in Celsius.
-        wind_speed: Union[HourlyContinuousCollection, pd.DataFrame, pd.Series, NDArray[np.float64]]:
-            The wind speed in m/s.
-
-    Returns:
-        HourlyContinuousCollection:
-            The calculated UTCI based on the shelter configuration for the given typology.
-    """
-    _inputs = [
-        air_temperature,
-        relative_humidity,
-        mean_radiant_temperature,
-        wind_speed,
-    ]
-
-    if all((isinstance(i, HourlyContinuousCollection) for i in _inputs)):
-        return UTCI(
-            air_temperature=air_temperature,
-            rel_humidity=relative_humidity,
-            rad_temperature=mean_radiant_temperature,
-            wind_speed=wind_speed,
-        ).universal_thermal_climate_index
-
-    if all((isinstance(i, (float, int)) for i in _inputs)):
-        return utci_vectorised(
-            ta=air_temperature,
-            rh=relative_humidity,
-            tr=mean_radiant_temperature,
-            vel=np.clip([wind_speed], 0, 17)[0],
-        )
-
-    if all((isinstance(i, pd.DataFrame) for i in _inputs)):
-        return pd.DataFrame(
-            utci_vectorised(
-                ta=air_temperature.values,
-                rh=relative_humidity.values,
-                tr=mean_radiant_temperature.values,
-                vel=wind_speed.clip(lower=0, upper=17).values,
-            ),
-            columns=_inputs[0].columns
-            if len(_inputs[0].columns) > 1
-            else ["Universal Thermal Climate Index (C)"],
-            index=_inputs[0].index,
-        )
-
-    if all((isinstance(i, pd.Series) for i in _inputs)):
-        return pd.Series(
-            utci_vectorised(
-                ta=air_temperature,
-                rh=relative_humidity,
-                tr=mean_radiant_temperature,
-                vel=wind_speed.clip(lower=0, upper=17),
-            ),
-            name="Universal Thermal Climate Index (C)",
-            index=_inputs[0].index,
-        )
-
-    if all((isinstance(i, (List, Tuple)) for i in _inputs)):
-        return utci_vectorised(
-            ta=np.array(air_temperature),
-            rh=np.array(relative_humidity),
-            tr=np.array(mean_radiant_temperature),
-            vel=np.clip(np.array(wind_speed), 0, 17),
-        )
-
-    try:
-        # assume numpy array
-        return utci_vectorised(
-            ta=air_temperature,
-            rh=relative_humidity,
-            tr=mean_radiant_temperature,
-            vel=np.clip(wind_speed, 0, 17),
-        )
-    except Exception as e:
-        raise ValueError(
-            "No possible means of calculating UTCI from that combination of inputs was found."
-        )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci/postprocess.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci/postprocess.py
deleted file mode 100644
index 50417b6a..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci/postprocess.py
+++ /dev/null
@@ -1,781 +0,0 @@
-import warnings
-from typing import Any, List, Tuple, Union
-
-import numpy as np
-import pandas as pd
-from ladybug.datacollection import HourlyContinuousCollection
-from ladybug.datatype.temperature import (
-    UniversalThermalClimateIndex as LB_UniversalThermalClimateIndex,
-)
-from ladybug.epw import EPW
-from ladybug_comfort.collection.solarcal import OutdoorSolarCal
-from ladybug_comfort.collection.utci import UTCI
-from scipy.interpolate import interp1d, interp2d
-
-from ...categorical.categories import UTCI_DEFAULT_CATEGORIES, Categorical
-from ...helpers import evaporative_cooling_effect, month_hour_binned_series
-from ...ladybug_extension.datacollection import (
-    collection_from_series,
-    collection_to_series,
-)
-from ...plot.utilities import contrasting_color
-
-
-def compare_monthly_utci(
-    utci_collections: List[HourlyContinuousCollection],
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
-    identifiers: Tuple[str] = None,
-    density: bool = False,
-    simplify: bool = False,
-) -> pd.DataFrame:
-    """Create a summary of a comparison between a "baseline" UTCI collection, and another.
-
-    Args:
-        utci_collections (List[HourlyContinuousCollection]):
-            A list of UTCI collections to compare.
-        utci_categories (Categories, optional):
-            A set of categories to use for the comparison.
-        identifiers (Tuple[str]):
-            A tuple of identifiers for the baseline and comparable collections.
-        density (bool, optional):
-            Return the proportion of time rather than the number of hours. Defaults to False.
-        simplify (bool, optional):
-            Simplify the summary table to use the "ComfortClass" of the UTCI categories (where these attribtues have been added to the object). Defaults to False.
-
-    Returns:
-        str:
-            A text summary of the differences between the given UTCI data collection.
-    """
-
-    # ensure each collection given it a UTCI collection
-    if len(utci_collections) < 2:
-        raise ValueError("At least two UTCI collections must be given to compare them.")
-    # check each collection is a utci collection
-
-    if any(
-        not isinstance(i.header.data_type, LB_UniversalThermalClimateIndex)
-        for i in utci_collections
-    ):
-        raise ValueError("Input collection is not a UTCI collection.")
-
-    if identifiers is None:
-        identifiers = range(len(utci_collections))
-    else:
-        assert len(identifiers) == len(
-            utci_collections
-        ), "The identifiers given must be the same length as the collections given."
-
-    dd = []
-    for i in utci_collections:
-        _df = utci_categories.timeseries_summary_monthly(
-            collection_to_series(i), density=density
-        )
-        _df.columns = [utci_categories.interval_from_bin_name(i) for i in _df.columns]
-        dd.append(_df)
-    df = pd.concat(dd, axis=1, keys=identifiers)
-    df = df.reorder_levels([1, 0], axis=1).sort_index(axis=1)
-    df = df.rename(columns=utci_categories._interval_bin_name)
-
-    if simplify:
-        try:
-            if len(utci_categories.comfort_classes) != len(
-                utci_categories.interval_index
-            ):
-                raise ValueError(
-                    "Monkey-patched comfort_class attributes are not the same length as the categories within this Category object."
-                )
-            if set(["Too cold", "Comfortable", "Too hot"]) != set(
-                [i.value for i in utci_categories.comfort_classes]
-            ):
-                raise ValueError(
-                    'Monkey-patched comfort_class attributes should contain values of either "Too cold", "Comfortable" or "Too hot".'
-                )
-
-            ddd = {
-                i: cc
-                for cc, i in list(
-                    zip(*[utci_categories.comfort_classes, utci_categories.bin_names])
-                )
-            }
-            cc_header = []
-            for col, _ in df.iteritems():
-                cc_header.append(ddd[col[0]].value)
-
-            df.columns = pd.MultiIndex.from_arrays(
-                [
-                    df.columns.get_level_values(0),
-                    df.columns.get_level_values(1),
-                    cc_header,
-                ],
-                names=["Category", "Identifier", "ComfortClass"],
-            )
-            df = df.groupby(
-                [
-                    df.columns.get_level_values("ComfortClass"),
-                    df.columns.get_level_values("Identifier"),
-                ],
-                axis=1,
-            ).sum()
-            df = df.reindex(
-                columns=df.columns.reindex(
-                    ["Too cold", "Comfortable", "Too hot"], level=0
-                )[0]
-            )
-            return df
-
-        except AttributeError as exc:
-            raise AttributeError(
-                'No ComfortClass found on given "utci_categories", you cannot use these categories for a simplified summary until you add these.'
-            ) from exc
-
-    return df
-
-
-def shade_benefit_category(
-    unshaded_utci: Union[HourlyContinuousCollection, pd.Series],
-    shaded_utci: Union[HourlyContinuousCollection, pd.Series],
-    comfort_limits: Tuple[float] = (9, 26),
-) -> pd.Series:
-    """Determine shade-gap analysis category, indicating where shade is not beneificial.
-
-    Args:
-        unshaded_utci (Union[HourlyContinuousCollection, pd.Series]):
-            A dataset containing unshaded UTCI values.
-        shaded_utci (Union[HourlyContinuousCollection, pd.Series]):
-            A dataset containing shaded UTCI values.
-        comfort_limits (Tuple[float], optional):
-            The range within which "comfort" is achieved. Defaults to (9, 26).
-
-    Returns:
-        pd.Series:
-            A catgorical series indicating shade-benefit.
-
-    """
-
-    # convert to series if not already
-    if isinstance(unshaded_utci, HourlyContinuousCollection):
-        unshaded_utci = collection_to_series(unshaded_utci)
-    if isinstance(shaded_utci, HourlyContinuousCollection):
-        shaded_utci = collection_to_series(shaded_utci)
-
-    if len(unshaded_utci) != len(shaded_utci):
-        raise ValueError(
-            f"Input sizes do not match ({len(unshaded_utci)} != {len(shaded_utci)})"
-        )
-
-    if sum(unshaded_utci == shaded_utci) == len(unshaded_utci):
-        raise ValueError("Input series are identical.")
-
-    # get limits
-    low, high = min(comfort_limits), max(comfort_limits)
-
-    # get distance to comfort (degrees from edge of "comfortable")
-    distance_from_comfort_unshaded = abs(
-        np.where(
-            unshaded_utci < low,
-            unshaded_utci - low,
-            np.where(unshaded_utci > high, unshaded_utci - high, 0),
-        )
-    )
-    distance_from_comfort_shaded = abs(
-        np.where(
-            shaded_utci < low,
-            shaded_utci - low,
-            np.where(shaded_utci > high, shaded_utci - high, 0),
-        )
-    )
-
-    # get boolean mask where comfortable
-    comfortable_unshaded = unshaded_utci.between(low, high)
-    comfortable_shaded = shaded_utci.between(low, high)
-
-    # get masks for each category
-    comfortable_without_shade = comfortable_unshaded
-    comfortable_with_shade = ~comfortable_unshaded & comfortable_shaded
-    shade_has_negative_impact = (
-        distance_from_comfort_unshaded < distance_from_comfort_shaded
-    )
-    shade_has_positive_impact = (
-        distance_from_comfort_unshaded > distance_from_comfort_shaded
-    )
-
-    # construct categorical series
-    shade_categories = np.where(
-        comfortable_without_shade,
-        "Comfortable without shade",
-        np.where(
-            comfortable_with_shade,
-            "Comfortable with shade",
-            np.where(
-                shade_has_negative_impact,
-                "Shade is detrimental",
-                np.where(shade_has_positive_impact, "Shade is beneficial", "Undefined"),
-            ),
-        ),
-    )
-
-    return pd.Series(shade_categories, index=unshaded_utci.index)
-
-
-def distance_to_comfortable(
-    utci_values: Any,
-    comfort_thresholds: Tuple[float] = (9, 26),
-    distance_to_comfort_band_centroid: bool = True,
-) -> Union[List[float], float]:
-    """
-    Get the distance between the given value/s and the "comfortable" category.
-
-    Args:
-        utci_values (Any):
-            A value or set of values representing UTCI temperature.
-        comfort_thresholds (List[float], optional):
-            The comfortable band of UTCI temperatures. Defaults to [9, 26].
-        distance_to_comfort_band_centroid (bool, optional):
-            If True, the distance to the centroid of the comfort band is plotted. If False, the
-            distance to the edge of the comfort band is plotted. Defaults to False.
-
-    Returns:
-        str:
-            A text summary of the given UTCI data collection.
-
-    """
-
-    if len(comfort_thresholds) != 2:
-        raise ValueError("comfort_thresholds must be a list of length 2.")
-
-    if len(set(comfort_thresholds)) != 2:
-        raise ValueError("comfort_thresholds must contain two unique values.")
-
-    if comfort_thresholds[0] > comfort_thresholds[1]:
-        warnings.warn("comfort_thresholds are not increasing. Swapping the values.")
-
-    low_limit = min(comfort_thresholds)
-    high_limit = max(comfort_thresholds)
-    midpoint = np.mean(comfort_thresholds)
-
-    vals = np.array(utci_values.values)
-    if not distance_to_comfort_band_centroid:
-        vals = np.where(
-            vals < low_limit,
-            vals - low_limit,
-            np.where(vals > high_limit, vals - high_limit, 0),
-        )
-    else:
-        vals = np.where(vals < midpoint, -(midpoint - vals), vals - midpoint)
-
-    name = (
-        f'Distance from "comfortable" [between {low_limit}°C and {high_limit}°C UTCI] (C)'
-        if not distance_to_comfort_band_centroid
-        else f'Distance from "comfortable" range midpoint [at {midpoint:0.1f}°C between {low_limit}°C and {high_limit}°C UTCI] (C)'
-    )
-
-    if isinstance(utci_values, pd.Series):
-        return pd.Series(vals, index=utci_values.index, name=name)
-
-    if isinstance(utci_values, pd.DataFrame):
-        return pd.DataFrame(vals, index=utci_values.index, columns=utci_values.columns)
-
-    if isinstance(utci_values, HourlyContinuousCollection):
-        if not isinstance(
-            utci_values.header.data_type, LB_UniversalThermalClimateIndex
-        ):
-            raise ValueError("Input collection is not a UTCI collection.")
-        return collection_from_series(
-            pd.Series(
-                vals,
-                index=collection_to_series(utci_values).index,
-                name=name,
-            )
-        )
-
-    return vals
-
-
-def feasible_utci_limits(
-    epw: EPW, include_additional_moisture: float = 0, as_dataframe: bool = False
-) -> Union[Tuple[HourlyContinuousCollection], pd.DataFrame]:
-    """Calculate the absolute min/max collections of UTCI based on possible shade, wind and moisture conditions.
-
-    Args:
-        epw (EPW):
-            The EPW object for which limits will be calculated.
-        include_additional_moisture (float, optional):
-            Include the effect of evaporative cooling on the UTCI limits. Default is 0, for no evaporative cooling.
-        as_dataframe (bool):
-            Return the output as a dataframe with two columns, instread of two separate collections.
-
-    Returns:
-        Union[Tuple[HourlyContinuousCollection], pd.DataFrame]:
-            The lowest possible UTCI and highest UTCI temperatures for each hour of the year.
-    """
-    mrt_unshaded = OutdoorSolarCal(
-        epw.location,
-        epw.direct_normal_radiation,
-        epw.diffuse_horizontal_radiation,
-        epw.horizontal_infrared_radiation_intensity,
-        epw.dry_bulb_temperature,
-    ).mean_radiant_temperature
-    dbt_evap, rh_evap = np.array(
-        [
-            evaporative_cooling_effect(
-                dry_bulb_temperature=_dbt,
-                relative_humidity=_rh,
-                evaporative_cooling_effectiveness=include_additional_moisture,
-                atmospheric_pressure=_atm,
-            )
-            for _dbt, _rh, _atm in list(
-                zip(
-                    *[
-                        epw.dry_bulb_temperature,
-                        epw.relative_humidity,
-                        epw.atmospheric_station_pressure,
-                    ]
-                )
-            )
-        ]
-    ).T
-    dbt_evap = epw.dry_bulb_temperature.get_aligned_collection(dbt_evap)
-    rh_evap = epw.relative_humidity.get_aligned_collection(rh_evap)
-
-    dbt_rh_options = (
-        [[dbt_evap, rh_evap], [epw.dry_bulb_temperature, epw.relative_humidity]]
-        if include_additional_moisture != 0
-        else [[epw.dry_bulb_temperature, epw.relative_humidity]]
-    )
-
-    utcis = []
-    utcis = []
-    for _dbt, _rh in dbt_rh_options:
-        for _ws in [
-            epw.wind_speed,
-            epw.wind_speed.get_aligned_collection(0),
-            epw.wind_speed * 1.1,
-        ]:
-            for _mrt in [epw.dry_bulb_temperature, mrt_unshaded]:
-                utcis.append(
-                    UTCI(
-                        air_temperature=_dbt,
-                        rad_temperature=_mrt,
-                        rel_humidity=_rh,
-                        wind_speed=_ws,
-                    ).universal_thermal_climate_index,
-                )
-    df = pd.concat([collection_to_series(i) for i in utcis], axis=1)
-    min_utci = collection_from_series(
-        df.min(axis=1).rename("Universal Thermal Climate Index (C)")
-    )
-    max_utci = collection_from_series(
-        df.max(axis=1).rename("Universal Thermal Climate Index (C)")
-    )
-
-    if as_dataframe:
-        return pd.concat(
-            [
-                collection_to_series(min_utci),
-                collection_to_series(max_utci),
-            ],
-            axis=1,
-            keys=["lowest", "highest"],
-        )
-
-    return min_utci, max_utci
-
-
-def feasible_utci_category_limits(
-    epw: EPW,
-    include_additional_moisture: float = 0,
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
-    density: bool = False,
-    simplify: bool = False,
-    mask: List[bool] = None,
-):
-    """Calculate the upper and lower proportional limits of UTCI categories based on possible shade, wind and moisture conditions.
-
-    Args:
-        epw (EPW):
-            The EPW object for which limits will be calculated.
-        include_additional_moisture (float, optional):
-            Include the effect of evaporative cooling on the UTCI limits. Default is 0, for no evaporative cooling.
-        utci_categories (Categorical, optional):
-            A set of categories to use for the comparison.
-        density (bool, optional):
-            Return the proportion of time rather than the number of hours. Defaults to False.
-        simplify (bool, optional):
-            Simplify the summary table to use the "ComfortClass" of the UTCI categories (where these attribtues have been added to the object). Defaults to False.
-        mask (List[bool], optional):
-            A list of booleans to mask the data. Defaults to None.
-
-    Returns:
-        pd.DataFrame:
-            A table with monthly time binned UTCI data.
-    """
-
-    lims = feasible_utci_limits(
-        epw, include_additional_moisture=include_additional_moisture, as_dataframe=True
-    )
-
-    if mask is not None:
-        lims = lims[mask]
-        if lims.index.month.nunique() != 12:
-            raise ValueError("Masked data include at least one value per month.")
-
-    df = pd.concat(
-        [
-            utci_categories.timeseries_summary_monthly(lims.lowest, density=density),
-            utci_categories.timeseries_summary_monthly(lims.highest, density=density),
-        ],
-        axis=1,
-        keys=lims.columns,
-    )
-    df = (
-        df.sort_index(axis=1)
-        .reorder_levels([1, 0], axis=1)
-        .sort_index(axis=1, ascending=[True, False])
-    )
-    df = df.rename(columns=utci_categories._interval_bin_name)
-
-    if simplify:
-        try:
-            if len(utci_categories.comfort_classes) != len(
-                utci_categories.interval_index
-            ):
-                raise ValueError(
-                    "Monkey-patched comfort_class attributes are not the same length as the categories within this Category object."
-                )
-            if set(["Too cold", "Comfortable", "Too hot"]) != set(
-                i.value for i in utci_categories.comfort_classes
-            ):
-                raise ValueError(
-                    'Monkey-patched comfort_class attributes should contain values of either "Too cold", "Comfortable" or "Too hot".'
-                )
-
-            ddd = {
-                i: cc
-                for cc, i in list(
-                    zip(*[utci_categories.comfort_classes, utci_categories.bin_names])
-                )
-            }
-            cc_header = []
-            for col, _ in df.iteritems():
-                cc_header.append(ddd[col[0]].value)
-
-            df.columns = pd.MultiIndex.from_arrays(
-                [
-                    df.columns.get_level_values(0),
-                    df.columns.get_level_values(1),
-                    cc_header,
-                ],
-                names=["Category", "Identifier", "ComfortClass"],
-            )
-            df = df.groupby(
-                [
-                    df.columns.get_level_values("ComfortClass"),
-                    df.columns.get_level_values("Identifier"),
-                ],
-                axis=1,
-            ).sum()
-            df = df.sort_index(axis=1, ascending=[True, False])
-            df = df.reindex(
-                columns=df.columns.reindex(
-                    ["Too cold", "Comfortable", "Too hot"], level=0
-                )[0]
-            )
-
-            return df
-
-        except AttributeError as exc:
-            raise AttributeError(
-                'No ComfortClass found on given "utci_categories", you cannot use these categories for a simplified summary until you add these.'
-            ) from exc
-
-    return df
-
-
-def month_hour_binned(
-    utci_data: Union[pd.Series, HourlyContinuousCollection],
-    month_bins: Tuple[Tuple[int]] = None,
-    hour_bins: Tuple[Tuple[int]] = None,
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
-    color_result: bool = False,
-    month_labels: Tuple[str] = None,
-    hour_labels: Tuple[str] = None,
-    agg: str = "mean",
-    **kwargs,
-) -> pd.DataFrame:
-    """Create a table with monthly hour binned UTCI data.
-
-    Args:
-        utci_data (Union[pd.Series, HourlyContinuousCollection]):
-            A collection of UTCI values.
-        month_bins (Tuple[List[int]]):
-            A list of lists of months to group data into.
-        hour_bins (Tuple[List[int]]):
-            A list of lists of hours to group data into.
-        utci_categories (Categorical, optional):
-            A set of UTCI categories to use for the comparison.
-        color_result (bool, optional):
-            Return a color-coded table. Defaults to False.
-        month_labels (List[str], optional):
-            A list of labels for the month bins. Defaults to None.
-        hour_labels (List[str], optional):
-            A list of labels for the hour bins. Defaults to None.
-        agg (str, optional):
-            The aggregation method to use for the data within each bin. Defaults to "mean".
-        **kwargs:
-            Additional keyword arguments to pass to the pandas.DataFrame.style.format method.
-            contrast_text (bool, optional):
-                If True, the text color will be chosen to contrast with the background color. Defaults to True.
-            round (int, optional):
-                The number of decimal places to round the data to. Defaults to 6.
-
-
-    Returns:
-        pd.DataFrame:
-            A table with monthly time binned UTCI data.
-    """
-
-    # check the utci_data is either a pd.Series or a HourlyContinuousCollection
-    if not isinstance(utci_data, (pd.Series, HourlyContinuousCollection)):
-        raise TypeError(
-            "utci_data must be either a pandas.Series or a ladybug.datacollection.HourlyContinuousCollection"
-        )
-
-    if isinstance(utci_data, HourlyContinuousCollection):
-        utci_data = collection_to_series(utci_data)
-
-    df = month_hour_binned_series(
-        series=utci_data,
-        month_bins=month_bins,
-        hour_bins=hour_bins,
-        month_labels=month_labels,
-        hour_labels=hour_labels,
-        agg=agg,
-    )
-
-    if color_result:
-        warnings.warn(
-            'The value returned by this method when "color_result" is applied is not a pd.Dataframe object. To get the dataframe use ".data".'
-        )
-
-        def _highlight(val):
-            bg_color = utci_categories.get_color(val)
-            if kwargs.pop("contrasting_text", True):
-                fg_color = contrasting_color(bg_color)
-            else:
-                fg_color = "black"
-            return f"color:{fg_color};background-color:{bg_color}"
-
-        return df.style.format(precision=kwargs.pop("round", 6)).applymap(_highlight)
-
-    return df
-
-
-def met_rate_adjustment(
-    utci_collection: HourlyContinuousCollection, met: float
-) -> HourlyContinuousCollection:
-    """Adjust a UTCI data collection using a target met-rate.
-
-    This method uses the relationshp between UTCI and MET rate described in LINDNER-CENDROWSKA, Katarzyna and BRÖDE, Peter, 2021. The evaluation of biothermal conditions for various forms of climatic therapy based on UTCI adjusted for activity [online]. 2021. IGiPZ PAN. [Accessed 27 October 2022]. Available from: http://rcin.org.pl/igipz/Content/194924/PDF/WA51_229409_r2021-t94-no2_G-Polonica-Linder.pdf.
-
-    +---------------------------------------------------------------+----------------------+
-    | Activity                                                      | Metabolic rate (MET) |
-    +---------------------------------------------------------------+----------------------+
-    | Neutral (resting in sitting or standing position)             | 1.1                  |
-    +---------------------------------------------------------------+----------------------+
-    | Slow walk (on even path, without load, at 3-4 km·h-1)         | 2.3                  |
-    | (default for UTCI calculation)                                |                      |
-    +---------------------------------------------------------------+----------------------+
-    | Fast walk (on even path, without load, at ~5 km·h-1)          | 3.4                  |
-    +---------------------------------------------------------------+----------------------+
-    | Marching (on even path, without load, at ~5.5 km·h-1)         | 4.0                  |
-    +---------------------------------------------------------------+----------------------+
-    | Bicycling (for pleasure, on flat terrain, at < 16 km·h-1)     | 4.0                  |
-    +---------------------------------------------------------------+----------------------+
-    | Nordic walking (for exercise, on flat terrain, at 5-6 km·h-1) | 4.8                  |
-    +---------------------------------------------------------------+----------------------+
-
-    Args:
-        utci_collection (HourlyContinuousCollection):
-            A UTCI data collection.
-        met (float, optional):
-            The metabolic rate to apply to this data collection.
-
-    Returns:
-        HourlyContinuousCollection:
-            An adjusted UTCI data collection.
-    """
-
-    if met < 1.1:
-        raise ValueError(
-            "met_rate must be >= 1.1 (representative of a human body at rest)."
-        )
-    if met > 4.8:
-        raise ValueError(
-            "met_rate must be <= 4.8 (representative of a exercise at 5-6km·h-1)."
-        )
-
-    # data below extracted from https://doi.org/10.7163/GPol.0199 in the format {MET: [UTCI, ΔUTCI]}
-    data = {
-        4.8: [
-            [-50, 71.81581439393939],
-            [-46.47239263803681, 71.5127840909091],
-            [-42.94478527607362, 71.36126893939394],
-            [-38.95705521472392, 70.14914772727273],
-            [-34.96932515337423, 67.421875],
-            [-30.521472392638035, 64.69460227272728],
-            [-26.993865030674847, 61.66429924242425],
-            [-23.006134969325153, 58.33096590909091],
-            [-17.944785276073617, 52.87642045454545],
-            [-12.576687116564415, 48.63399621212122],
-            [-6.441717791411044, 43.63399621212122],
-            [-1.380368098159508, 40.300662878787875],
-            [4.29447852760736, 35.755208333333336],
-            [9.815950920245399, 30.906723484848484],
-            [15.490797546012274, 26.512784090909093],
-            [23.00613496932516, 20.755208333333336],
-            [28.06748466257669, 16.967329545454547],
-            [30.98159509202455, 15.452178030303031],
-            [35.889570552147234, 12.573390151515156],
-            [42.63803680981596, 9.846117424242422],
-            [50, 6.967329545454547],
-        ],
-        4.0: [
-            [-50, 46.05823863636364],
-            [-47.69938650306749, 47.421875],
-            [-45.0920245398773, 48.63399621212122],
-            [-43.86503067484662, 50.60369318181819],
-            [-40.1840490797546, 51.96732954545455],
-            [-36.04294478527608, 51.36126893939394],
-            [-33.74233128834356, 50.906723484848484],
-            [-30.67484662576687, 49.543087121212125],
-            [-27.300613496932513, 47.87642045454545],
-            [-24.846625766871163, 46.36126893939394],
-            [-23.159509202453986, 44.99763257575758],
-            [-20.39877300613497, 42.87642045454545],
-            [-16.871165644171782, 39.69460227272728],
-            [-14.11042944785276, 37.573390151515156],
-            [-11.04294478527607, 35.60369318181819],
-            [-7.515337423312886, 34.24005681818182],
-            [-3.374233128834355, 32.27035984848485],
-            [1.0736196319018418, 29.84611742424243],
-            [4.141104294478531, 28.02793560606061],
-            [7.055214723926383, 25.45217803030303],
-            [9.969325153374236, 23.482481060606062],
-            [13.036809815950924, 21.664299242424242],
-            [16.41104294478528, 19.543087121212125],
-            [19.631901840490798, 17.27035984848485],
-            [22.852760736196316, 14.846117424242422],
-            [26.22699386503068, 12.724905303030305],
-            [29.601226993865026, 11.664299242424242],
-            [33.28220858895706, 9.846117424242422],
-            [36.65644171779141, 8.785511363636367],
-            [39.87730061349693, 8.330965909090907],
-            [42.63803680981596, 7.421875],
-            [46.012269938650306, 6.664299242424242],
-            [50, 5.452178030303031],
-        ],
-        3.4: [
-            [-50, 27.724905303030305],
-            [-47.239263803680984, 28.785511363636367],
-            [-44.47852760736196, 29.543087121212125],
-            [-41.717791411042946, 30.45217803030303],
-            [-38.34355828220859, 31.05823863636364],
-            [-35.2760736196319, 31.05823863636364],
-            [-33.43558282208589, 31.361268939393938],
-            [-30.98159509202454, 31.815814393939398],
-            [-28.220858895705522, 31.967329545454547],
-            [-26.380368098159508, 31.05823863636364],
-            [-24.846625766871163, 30.300662878787882],
-            [-23.006134969325153, 29.694602272727273],
-            [-21.165644171779142, 28.482481060606062],
-            [-18.558282208588956, 26.967329545454547],
-            [-16.411042944785272, 25.755208333333336],
-            [-14.11042944785276, 24.39157196969697],
-            [-11.65644171779141, 23.02793560606061],
-            [-8.742331288343557, 23.179450757575758],
-            [-5.828220858895705, 23.330965909090914],
-            [-2.4539877300613497, 22.573390151515156],
-            [0.6134969325153392, 21.361268939393938],
-            [3.374233128834355, 19.846117424242422],
-            [6.74846625766871, 17.876420454545453],
-            [9.662576687116562, 16.512784090909093],
-            [12.883435582822088, 14.846117424242422],
-            [16.104294478527606, 14.088541666666664],
-            [19.631901840490798, 11.815814393939398],
-            [22.085889570552155, 10.300662878787882],
-            [24.84662576687117, 8.785511363636367],
-            [28.52760736196319, 8.027935606060609],
-            [31.44171779141105, 7.1188446969696955],
-            [34.20245398773007, 6.058238636363637],
-            [37.269938650306756, 5.755208333333336],
-            [40.03067484662577, 5.452178030303031],
-            [42.94478527607362, 4.846117424242426],
-            [46.012269938650306, 4.088541666666668],
-            [50, 3.9370265151515156],
-        ],
-        2.3: [
-            [-50, -0.15151515151515227],
-            [50, 0],
-        ],
-        1.1: [
-            [-50, -27.87878787878788],
-            [-48.15950920245399, -26.96969696969697],
-            [-45.858895705521476, -26.21212121212121],
-            [-44.6319018404908, -24.09090909090909],
-            [-42.484662576687114, -21.96969696969697],
-            [-39.57055214723926, -19.848484848484848],
-            [-36.04294478527608, -19.242424242424242],
-            [-32.20858895705521, -20],
-            [-29.447852760736197, -21.363636363636363],
-            [-25.460122699386503, -23.03030303030303],
-            [-22.54601226993865, -24.848484848484848],
-            [-19.32515337423313, -26.666666666666668],
-            [-16.257668711656443, -28.484848484848484],
-            [-13.34355828220859, -30.151515151515152],
-            [-9.509202453987726, -30.90909090909091],
-            [-5.981595092024541, -31.060606060606062],
-            [-3.067484662576689, -29.393939393939394],
-            [-1.380368098159508, -27.272727272727273],
-            [0, -24.848484848484848],
-            [1.0736196319018418, -21.666666666666668],
-            [2.760736196319016, -18.78787878787879],
-            [4.141104294478531, -15.909090909090908],
-            [6.74846625766871, -13.484848484848484],
-            [9.969325153374236, -11.818181818181818],
-            [13.190184049079754, -11.363636363636363],
-            [16.257668711656436, -9.242424242424242],
-            [19.785276073619627, -9.696969696969697],
-            [23.00613496932516, -10.303030303030303],
-            [26.380368098159508, -9.242424242424242],
-            [29.447852760736197, -7.272727272727273],
-            [32.515337423312886, -6.363636363636363],
-            [35.582822085889575, -5.303030303030301],
-            [38.650306748466264, -5.151515151515152],
-            [41.25766871165645, -4.3939393939393945],
-            [44.018404907975466, -3.787878787878789],
-            [46.31901840490798, -3.333333333333332],
-            [50, -3.6363636363636367],
-        ],
-    }
-
-    matrix = []
-    for k, v in data.items():
-        x, y = np.array(v).T
-        f = interp1d(x, y)
-        new_x = np.linspace(-50, 50, 1000)
-        new_y = f(new_x)
-        matrix.append(pd.Series(index=new_x, data=new_y, name=k))
-    met_rate, utci_val, utci_delta = (
-        pd.concat(matrix, axis=1).unstack().reset_index().values.T
-    )
-
-    # create 2d interpolator between [MET, UTCI] and [ΔUTCI]
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        forecaster = interp2d(met_rate, utci_val, utci_delta)
-
-    # Calculate ΔUTCI
-    original_utci = collection_to_series(utci_collection)
-    utci_delta = [forecaster(met, i)[0] for i in original_utci.values]
-
-    return collection_from_series(original_utci + utci_delta)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/helpers.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/helpers.py
index 7ea2e318..2775579f 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/helpers.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/helpers.py
@@ -1,5 +1,6 @@
-from __future__ import annotations
-
+"""Helper methods used throughout the ladybugtools_toolkit."""
+# pylint: disable=C0302
+# pylint: disable=E0401
 import calendar
 import contextlib
 import copy
@@ -12,10 +13,12 @@
 from datetime import datetime, timedelta
 from enum import Enum, auto
 from pathlib import Path
-from typing import Any, Dict, List, Tuple, Union
+from typing import Any
 
 import numpy as np
 import pandas as pd
+from caseconverter import snakecase
+from honeybee.config import folders as hb_folders
 from ladybug.datatype.temperature import WetBulbTemperature
 from ladybug.epw import EPW, AnalysisPeriod, HourlyContinuousCollection, Location
 from ladybug.psychrometrics import wet_bulb_from_db_rh
@@ -28,26 +31,40 @@
     zhang_huang_solar_split,
 )
 from ladybug.sunpath import Sunpath
-from matplotlib.ticker import FuncFormatter
-from scipy.stats import weibull_min
-from tqdm import tqdm
+from ladybug_geometry.geometry2d import Vector2D
+from meteostat import Hourly, Point
 
+from .bhom.analytics import bhom_analytics
+from .bhom.logging import CONSOLE_LOGGER
+from .ladybug_extension.dt import lb_datetime_from_datetime
 
-@FuncFormatter
-def ZeroPadPercentFormatter(x: float) -> str:
-    """A matplotlib formatter for percentages that pads with zeros.
+# pylint: enable=E0401
 
-    Args:
-        x (float): The value to be formatted.
 
-    Returns:
-        str: The formatted string.
-    """
+def sanitise_string(string: str) -> str:
+    """Sanitise a string so that only path-safe characters remain."""
+
+    keep_characters = r"[^.A-Za-z0-9_-]"
+
+    return re.sub(keep_characters, "_", string).replace("__", "_").rstrip()
 
-    return f"{x:5.0%}"
 
+def convert_keys_to_snake_case(d: dict):
+    """Given a dictionary, convert all keys to snake_case."""
+    keys_to_skip = ["_t"]
+    if isinstance(d, dict):
+        return {
+            snakecase(k) if k not in keys_to_skip else k: convert_keys_to_snake_case(v)
+            for k, v in d.items()
+        }
+    if isinstance(d, list):
+        return [convert_keys_to_snake_case(x) for x in d]
+
+    return d
 
-def default_hour_analysis_periods() -> List[AnalysisPeriod]:
+
+@bhom_analytics()
+def default_hour_analysis_periods() -> list[AnalysisPeriod]:
     """A set of generic Analysis Period objects, spanning times of day."""
     f = io.StringIO()
     with contextlib.redirect_stdout(f):
@@ -61,7 +78,8 @@ def default_hour_analysis_periods() -> List[AnalysisPeriod]:
     return aps
 
 
-def default_month_analysis_periods() -> List[AnalysisPeriod]:
+@bhom_analytics()
+def default_month_analysis_periods() -> list[AnalysisPeriod]:
     """A set of generic Analysis Period objects, spanning month of year."""
     f = io.StringIO()
     with contextlib.redirect_stdout(f):
@@ -75,7 +93,8 @@ def default_month_analysis_periods() -> List[AnalysisPeriod]:
     return aps
 
 
-def default_combined_analysis_periods() -> List[AnalysisPeriod]:
+@bhom_analytics()
+def default_combined_analysis_periods() -> list[AnalysisPeriod]:
     """A set of generic Analysis Period objects, spanning combinations of time of day and month of year."""
     f = io.StringIO()
     with contextlib.redirect_stdout(f):
@@ -95,8 +114,10 @@ def default_combined_analysis_periods() -> List[AnalysisPeriod]:
     return aps
 
 
-def default_analysis_periods() -> List[AnalysisPeriod]:
-    """A set of generic Analysis Period objects, spanning all predefined combinations of time of ady and month of year."""
+@bhom_analytics()
+def default_analysis_periods() -> list[AnalysisPeriod]:
+    """A set of generic Analysis Period objects, spanning all predefined
+    combinations of time of day and month of year."""
     f = io.StringIO()
     with contextlib.redirect_stdout(f):
         aps = [
@@ -109,20 +130,22 @@ def default_analysis_periods() -> List[AnalysisPeriod]:
     return aps
 
 
-def chunks(lst: List[Any], chunksize: int):
+@bhom_analytics()
+def chunks(lst: list[Any], chunksize: int):
     """Partition an iterable into lists of length "chunksize".
 
     Args:
-        lst (List[Any]): The list to be partitioned.
+        lst (list[Any]): The list to be partitioned.
         chunksize (int): The size of each partition.
 
     Yields:
-        List[Any]: A list of length "chunksize".
+        list[Any]: A list of length "chunksize".
     """
     for i in range(0, len(lst), chunksize):
         yield lst[i : i + chunksize]
 
 
+@bhom_analytics()
 def scrape_weather(
     station: str,
     start_date: str = "1970-01-01",
@@ -131,7 +154,7 @@ def scrape_weather(
     resample: bool = False,
 ) -> pd.DataFrame:
     """Scrape historic data from global airport weather stations using their ICAO codes
-        (https://en.wikipedia.org/wiki/List_of_airports_by_IATA_and_ICAO_code)
+        (https://en.wikipedia.org/wiki/list_of_airports_by_IATA_and_ICAO_code)
 
     Args:
         station (str):
@@ -158,7 +181,34 @@ def scrape_weather(
         end_date = datetime.strptime(end_date, "%Y-%m-%d")
 
     # Scrape data from source website (https://mesonet.agron.iastate.edu/request/download.phtml)
-    uri = f"https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?station={station}&year1={start_date.year}&month1={start_date.month}&day1={start_date.day}&year2={end_date.year}&month2={end_date.month}&day2={end_date.day}&tz=Etc%2FUTC&format=onlycomma&latlon=yes&elev=yes&missing=null&trace=null&direct=no&data=tmpc&data=dwpc&data=relh&data=drct&data=sknt&data=alti&data=p01m&data=vsby&data=skyc1&data=skyc2&data=skyc3"
+    uri = (
+        "https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?"
+        f"station={station}&"
+        f"year1={start_date.year}&"
+        f"month1={start_date.month}&"
+        f"day1={start_date.day}&"
+        f"year2={end_date.year}&"
+        f"month2={end_date.month}&"
+        f"day2={end_date.day}&"
+        "tz=Etc%2FUTC&"
+        "format=onlycomma&"
+        "latlon=yes&"
+        "elev=yes&"
+        "missing=null&"
+        "trace=null&"
+        "direct=no&"
+        "data=tmpc&"
+        "data=dwpc&"
+        "data=relh&"
+        "data=drct&"
+        "data=sknt&"
+        "data=alti&"
+        "data=p01m&"
+        "data=vsby&"
+        "data=skyc1&"
+        "data=skyc2&"
+        "data=skyc3"
+    )
     df = pd.read_csv(
         uri,
         header=0,
@@ -305,11 +355,12 @@ def scrape_weather(
     return df
 
 
-def rolling_window(array: List[Any], window: int):
+@bhom_analytics()
+def rolling_window(array: list[Any], window: int):
     """Throwaway function here to roll a window along a list.
 
     Args:
-        array (List[Any]):
+        array (list[Any]):
             A 1D list of some kind.
         window (int):
             The size of the window to apply to the list.
@@ -319,7 +370,7 @@ def rolling_window(array: List[Any], window: int):
         returns [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8]]
 
     Returns:
-        List[List[Any]]:
+        list[list[Any]]:
             The resulting, "windowed" list.
     """
 
@@ -332,11 +383,20 @@ def rolling_window(array: List[Any], window: int):
     return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
 
 
+class DecayMethod(Enum):
+    """An enumeration of decay methods."""
+
+    LINEAR = auto()
+    PARABOLIC = auto()
+    SIGMOID = auto()
+
+
+@bhom_analytics()
 def proximity_decay(
     value: float,
     distance_to_value: float,
     max_distance: float,
-    decay_method: str = "linear",
+    decay_method: DecayMethod = DecayMethod.LINEAR,
 ) -> float:
     """Calculate the "decayed" value based on proximity (up to a maximum distance).
 
@@ -348,8 +408,8 @@ def proximity_decay(
         max_distance (float):
             The maximum distance to which magnitude is to be distributed. Beyond this, the input
             value is 0.
-        decay_method (str, optional):
-            A type of distribution (the shape of the distribution profile). Defaults to "linear".
+        decay_method (DecayMethod, optional):
+            A type of distribution (the shape of the distribution profile). Defaults to "DecayMethod.LINEAR".
 
     Returns:
         float:
@@ -358,16 +418,17 @@ def proximity_decay(
 
     distance_to_value = np.interp(distance_to_value, [0, max_distance], [0, 1])
 
-    if decay_method == "linear":
+    if decay_method == DecayMethod.LINEAR:
         return (1 - distance_to_value) * value
-    if decay_method == "parabolic":
+    if decay_method == DecayMethod.PARABOLIC:
         return (-(distance_to_value**2) + 1) * value
-    if decay_method == "sigmoid":
+    if decay_method == DecayMethod.SIGMOID:
         return (1 - (0.5 * (np.sin(distance_to_value * np.pi - np.pi / 2) + 1))) * value
 
     raise ValueError(f"Unknown curve type: {decay_method}")
 
 
+@bhom_analytics()
 def timedelta_tostring(time_delta: timedelta) -> str:
     """timedelta objects don't have a nice string representation, so this function converts them.
 
@@ -384,6 +445,7 @@ def timedelta_tostring(time_delta: timedelta) -> str:
     return f"{hours:02d}:{minutes:02d}"
 
 
+@bhom_analytics()
 def decay_rate_smoother(
     series: pd.Series,
     difference_threshold: float = -10,
@@ -434,6 +496,7 @@ def decay_rate_smoother(
     return new_series
 
 
+@bhom_analytics()
 def cardinality(direction_angle: float, directions: int = 16):
     """Returns the cardinal orientation of a given angle, where that angle is related to north at
         0 degrees.
@@ -523,6 +586,7 @@ def cardinality(direction_angle: float, directions: int = 16):
     return arr[(val % directions)]
 
 
+@bhom_analytics()
 def angle_from_cardinal(cardinal_direction: str) -> float:
     """
     For a given cardinal direction, return the corresponding angle in degrees.
@@ -577,11 +641,11 @@ def angle_from_cardinal(cardinal_direction: str) -> float:
     return lookup[cardinal_direction]
 
 
-def angle_from_north(vector: List[float]) -> float:
+def angle_from_north(vector: list[float]) -> float:
     """For an X, Y vector, determine the clockwise angle to north at [0, 1].
 
     Args:
-        vector (List[float]):
+        vector (list[float]):
             A vector of length 2.
 
     Returns:
@@ -594,120 +658,49 @@ def angle_from_north(vector: List[float]) -> float:
     return np.rad2deg((angle1 - angle2) % (2 * np.pi))
 
 
-def angle_to_vector(_angle_from_north: float) -> List[float]:
-    """Return the X, Y vector from of an angle from north at 0-degrees."""
-    if (_angle_from_north > 360) or (_angle_from_north < 0):
-        raise ValueError(
-            "angle_from_north must be between 0 and 360 degrees (inclusive)."
-        )
-
-    _angle_from_north = np.radians(_angle_from_north)
-
-    return np.sin(_angle_from_north), np.cos(_angle_from_north)
-
-
-def sanitise_string(string: str) -> str:
-    """Sanitise a string so that only path-safe characters remain."""
-    keep_characters = r"[^.A-Za-z0-9_-]"
-    return re.sub(keep_characters, "_", string).replace("__", "_").rstrip()
-
-
-def stringify_df_header(columns: List[Any]) -> List[str]:
-    """Convert a list of objects into their string represenations. This
-    method is mostly used for making DataFrames parqeut serialisable.
-    """
-
-    return [str(i) for i in columns]
-
-
-def unstringify_df_header(columns: List[str]) -> List[Any]:
-    """Convert a list of strings into a set of objecst capable of being used
-    as DataFrame column headers.
-    """
-
-    evaled = []
-    for i in columns:
-        try:
-            evaled.append(eval(i))  # pylint: disable=eval-used
-        except NameError:
-            evaled.append(i)
-
-    if all("(" in i for i in str(evaled)):
-        return pd.MultiIndex.from_tuples(evaled)
-    return evaled
-
-
-def store_dataset(
-    dataframe: pd.DataFrame, target_path: Path, downcast: bool = True
-) -> Path:
-    """Serialise a pandas DataFrame as a parquet file, including pre-processing to ensure it is serialisable, and optional downcasting to samller dtypes.
+def angle_to_vector(clockwise_angle_from_north: float) -> list[float]:
+    """Return the X, Y vector from of an angle from north at 0-degrees.
 
     Args:
-        dataframe (pd.DataFrame):
-            The dataframe to be serialised
-        target_path (Path):
-            The target file for storage.
-        downcast (bool, optional):
-            Optinal downcasting to reduce dataframe dtype complexity. Defaults to True.
+        clockwise_angle_from_north (float):
+            The angle from north in degrees clockwise from [0, 360], though
+            any number can be input here for angles greater than a full circle.
 
     Returns:
-        Path:
-            The path to the stored dataset.
+        list[float]:
+            A vector of length 2.
     """
 
-    target_path = Path(target_path)
-
-    if "parquet" not in target_path.suffix:
-        raise ValueError('This method only currently works for "*.parquet" files.')
-
-    if downcast:
-        # downcast dataframe type to save on storage
-        for col, _type in dataframe.dtypes.items():
-            if _type == "float64":
-                dataframe[col] = dataframe[col].astype(np.float32)
-            if _type == "int64":
-                dataframe[col] = dataframe[col].astype(np.int16)
-
-    # prepare dataframe for storage as parquet
-    dataframe.columns = stringify_df_header(dataframe.columns)
+    clockwise_angle_from_north = np.radians(clockwise_angle_from_north)
 
-    # store dataframe
-    dataframe.to_parquet(target_path, compression="snappy")
+    return np.sin(clockwise_angle_from_north), np.cos(clockwise_angle_from_north)
 
-    return target_path
 
-
-def load_dataset(target_path: Path, upcast: bool = True) -> pd.DataFrame:
-    """Read a stored dataset, including upcasting to float64.
+def epw_wind_vectors(epw: EPW, normalise: bool = False) -> list[Vector2D]:
+    """Return a list of vectors from the EPW wind direction and speed.
 
     Args:
-        target_path (Path):
-            The dataset path to be loaded.
-        upcast (bool, optional):
-            Optional upcasting of data to enable downstream calculations without issues!
+        epw (EPW):
+            An EPW object.
+        normalise (bool, optional):
+            Normalise the vectors. Defaults to False.
 
     Returns:
-        pd.DataFrame:
-            The loaded dataset as a dataframe.
+        list[Vector2D]:
+            A list of vectors.
     """
 
-    df = pd.read_parquet(target_path)
+    wind_direction = np.array(epw.wind_direction)
+    vectors = np.array(angle_to_vector(wind_direction))
 
-    if upcast:
-        for col, _type in dict(df.dtypes).items():
-            if _type == "float32":
-                df[col].astype(np.float64)
-            elif _type in ["int32", "int16", "int8"]:
-                df[col].astype(np.int64)
+    if not normalise:
+        vectors *= np.array(epw.wind_speed)
 
-    df.columns = unstringify_df_header(df.columns)
-
-    return df
+    return [Vector2D(*i) for i in vectors.T]
 
 
 class OpenMeteoVariable(Enum):
-    """An enumeration of the variables, and their metadata available
-    from OpenMeteo."""
+    """An enumeration of variables available from OpenMeteo, and their metadata for handling returned values."""
 
     TEMPERATURE_2M = auto()
     DEWPOINT_2M = auto()
@@ -741,7 +734,7 @@ class OpenMeteoVariable(Enum):
     SOIL_MOISTURE_100_TO_255CM = auto()
 
     @staticmethod
-    def __properties__() -> Dict[str, Dict[str, Union[str, float]]]:
+    def __properties__() -> dict[str, dict[str, str | float]]:
         """A dictionary of the properties of each variable."""
         return {
             OpenMeteoVariable.TEMPERATURE_2M.value: {
@@ -957,7 +950,7 @@ def __properties__() -> Dict[str, Dict[str, Union[str, float]]]:
         }
 
     @classmethod
-    def from_string(cls, name: str) -> OpenMeteoVariable:
+    def from_string(cls, name: str) -> "OpenMeteoVariable":
         """."""
         d = {
             "temperature_2m": cls.TEMPERATURE_2M,
@@ -1036,12 +1029,13 @@ def convert(self, value: float) -> float:
         return value * self.target_multiplier
 
 
+@bhom_analytics()
 def scrape_openmeteo(
     latitude: float,
     longitude: float,
-    start_date: Union[datetime, str],
-    end_date: Union[datetime, str],
-    variables: Tuple[OpenMeteoVariable] = None,
+    start_date: datetime | str,
+    end_date: datetime | str,
+    variables: tuple[OpenMeteoVariable] = None,
     convert_units: bool = False,
 ) -> pd.DataFrame:
     """Obtain historic hourly data from Open-Meteo.
@@ -1052,15 +1046,19 @@ def scrape_openmeteo(
             The latitude of the target site, in degrees.
         longitude (float):
             The longitude of the target site, in degrees.
-        start_date (Union[datetime, str]):
+        start_date (datetime | str):
             The start-date from which records will be obtained.
-        end_date (Union[datetime, str]):
+        end_date (datetime | str):
             The end-date beyond which records will be ignored.
-        variables (Tuple[OpenMeteoVariable]):
+        variables (tuple[OpenMeteoVariable]):
             A list of variables to query. If None, then all variables will be queried.
         convert_units (bool, optional):
             Convert units output into more common units, and rename headers accordingly.
 
+    Note:
+        This method saves the data to a local cache, and will return the cached data if it is less
+        than 28 days old. This is to avoid unnecessary API calls.
+
     Returns:
         pd.DataFrame:
             A DataFrame containing scraped data.
@@ -1074,8 +1072,13 @@ def scrape_openmeteo(
 
     if isinstance(start_date, str):
         start_date = datetime.strptime(start_date, "%Y-%m-%d")
+    if not isinstance(start_date, datetime):
+        raise ValueError("The start_date must be a datetime object or string.")
+
     if isinstance(end_date, str):
         end_date = datetime.strptime(end_date, "%Y-%m-%d")
+    if not isinstance(end_date, datetime):
+        raise ValueError("The end_date must be a datetime object or string.")
 
     if start_date > end_date:
         raise ValueError("The start_date must be before the end_date.")
@@ -1088,91 +1091,236 @@ def scrape_openmeteo(
                 "All values in the variables tuple must be of type OpenMeteoVariable."
             )
 
+    _dir = Path(hb_folders.default_simulation_folder) / "_lbt_tk_openmeteo"
+    _dir.mkdir(exist_ok=True, parents=True)
+
+    # check _dir for existence of scraped data matching this query
+    missing_variables = []
+    available_data = []
+    for var in variables:
+        # TODO - add check in here for whether data exists as subset of longer time period within cache
+        sp = (
+            _dir
+            / f"{latitude}_{longitude}_{start_date:%Y%m%d}_{end_date:%Y%m%d}_{var.name}.csv"
+        )
+        if sp.exists() and (
+            (datetime.now() - datetime.fromtimestamp(sp.stat().st_mtime)).days <= 60
+        ):
+            CONSOLE_LOGGER.info(f"Reloading cached data for {var.name}")
+            available_data.append(pd.read_csv(sp, index_col=0, parse_dates=True))
+        else:
+            missing_variables.append(var)
+
     # construct query string
-    var_strings = ",".join([i.openmeteo_name for i in variables])
-    query_string = f"https://archive-api.open-meteo.com/v1/era5?latitude={latitude}&longitude={longitude}&start_date={start_date:%Y-%m-%d}&end_date={end_date:%Y-%m-%d}&hourly={var_strings}"
+    var_strings = ",".join([var.openmeteo_name for var in missing_variables])
+    query_string = (
+        "https://archive-api.open-meteo.com/v1/era5?latitude="
+        f"{latitude}&longitude={longitude}&start_date={start_date:%Y-%m-%d}&"
+        f"end_date={end_date:%Y-%m-%d}&hourly={var_strings}"
+    )
 
     # request data
     with urllib.request.urlopen(query_string) as url:
         data = json.load(url)
 
-    # convert resultant data to dataframe
-    headers = [f"{k} ({v})" for (k, v) in data["hourly_units"].items()]
-    values = [v for _, v in data["hourly"].items()]
-    df = pd.DataFrame(np.array(values).T, columns=headers)
-    df = df.set_index(df.columns[0])
+    # construct dataframe
+    df = pd.DataFrame(data["hourly"])
+    df.set_index("time", inplace=True, drop=True)
     df.index.name = None
     df.index = pd.to_datetime(df.index)
     df = df.apply(pd.to_numeric, errors="coerce")
 
+    # write to cache
+    for col in df.columns:
+        sp = (
+            _dir
+            / f"{latitude}_{longitude}_{start_date:%Y%m%d}_{end_date:%Y%m%d}_{col}.csv"
+        )
+        df[col].to_csv(sp)
+
+    # append available_data
+    df = pd.concat(available_data + [df], axis=1)
+
     if not convert_units:
         return df
 
-    new_df = []
-    for i in OpenMeteoVariable:
-        try:
-            new_df.append(
-                i.convert(df[i.openmeteo_table_name]).rename(i.target_table_name)
-            )
-        except KeyError:
-            pass
-    return pd.concat(new_df, axis=1)
+    renamer = dict(
+        zip(
+            *[
+                [i.openmeteo_name for i in OpenMeteoVariable],
+                [i.target_table_name for i in OpenMeteoVariable],
+            ]
+        )
+    )
+
+    return df.rename(columns=renamer)
 
 
-def weibull_directional(
-    binned_data: Dict[Tuple[float, float], List[float]]
+def scrape_meteostat(
+    latitude: float,
+    longitude: float,
+    start_date: datetime | str,
+    end_date: datetime | str,
+    altitude: float = None,
+    convert_units: bool = False,
 ) -> pd.DataFrame:
-    """Calculate the weibull coefficients for a given set of binned data in the form {(low, high): [speeds], (low, high): [speeds]}, binned by the number of directions specified.
+    """Obtain historic hourly data from Meteostat.
+
     Args:
-        binned_data (Dict[Tuple[float, float], List[float]]):
-            A dictionary of binned wind speed data.
+        latitude (float):
+            The latitude of the target site, in degrees.
+        longitude (float):
+            The longitude of the target site, in degrees.
+        start_date (datetime | str):
+            The start-date from which records will be obtained.
+        end_date (datetime | str):
+            The end-date beyond which records will be ignored.
+        altitude (float, optional):
+            The altitude of the target site, in metres. Defaults to None.
+        convert_units (bool, optional):
+            Convert units output into more common units, and rename headers accordingly.
+
     Returns:
         pd.DataFrame:
-            A DataFrame with (direction_bin_low, direction_bin_high) as index, and weibull coefficients as columns.
+            A DataFrame containing scraped data.
     """
-    d = {}
-    for (low, high), speeds in tqdm(
-        binned_data.items(), desc="Calculating Weibull shape parameters"
-    ):
-        d[(low, high)] = weibull_pdf(speeds)
 
-    return pd.DataFrame.from_dict(d, orient="index", columns=["k", "loc", "c"])
+    # TODO - implement caching in here, similar to how it's done for the OpenMeteo method
+
+    if latitude < -90 or latitude > 90:
+        raise ValueError("The latitude must be between -90 and 90 degrees.")
+    if longitude < -180 or longitude > 180:
+        raise ValueError("The longitude must be between -180 and 180 degrees.")
+
+    if isinstance(start_date, str):
+        start_date = datetime.strptime(start_date, "%Y-%m-%d")
+    if not isinstance(start_date, datetime):
+        raise ValueError("The start_date must be a datetime object or string.")
+
+    if isinstance(end_date, str):
+        end_date = datetime.strptime(end_date, "%Y-%m-%d")
+    if not isinstance(end_date, datetime):
+        raise ValueError("The end_date must be a datetime object or string.")
+
+    location = Point(latitude, longitude, altitude)
+    data = Hourly(location, start_date, end_date).fetch()
+
+    if len(data) == 0:
+        raise ValueError("No data was returned from Meteostat.")
+
+    if convert_units:
+        converter = {
+            "temp": [1, "Dry Bulb Temperature (C)"],
+            "dwpt": [1, "Dew Point Temperature (C)"],
+            "rhum": [1, "Relative Humidity (%)"],
+            "prcp": [1, "Liquid Precipitation Depth (mm)"],
+            "snow": [0.1, "Snow Depth (cm)"],
+            "wdir": [1, "Wind Direction (degrees)"],
+            "wspd": [1 / 3.6, "Wind Speed (m/s)"],
+            "wpgt": [1 / 3.6, "Wind Gust (m/s)"],
+            "pres": [100, "Atmospheric Station Pressure (Pa)"],
+            "tsun": [1, "One Hour Sunshine (minutes)"],
+            "coco": [1, "Present Weather (text)"],
+        }
+        weather_codes = {
+            1: "Clear",
+            2: "Fair",
+            3: "Cloudy",
+            4: "Overcast",
+            5: "Fog",
+            6: "Freezing Fog",
+            7: "Light Rain",
+            8: "Rain",
+            9: "Heavy Rain",
+            10: "Freezing Rain",
+            11: "Heavy Freezing Rain",
+            12: "Sleet",
+            13: "Heavy Sleet",
+            14: "Light Snowfall",
+            15: "Snowfall",
+            16: "Heavy Snowfall",
+            17: "Rain Shower",
+            18: "Heavy Rain Shower",
+            19: "Sleet Shower",
+            20: "Heavy Sleet Shower",
+            21: "Snow Shower",
+            22: "Heavy Snow Shower",
+            23: "Lightning",
+            24: "Hail",
+            25: "Thunderstorm",
+            26: "Heavy Thunderstorm",
+            27: "Storm",
+        }
+        temp = []
+        for col_name, col_values in data.items():
+            if col_name == "coco":
+                temp.append(
+                    pd.Series(
+                        col_values.map(weather_codes), name=converter[col_name][1]
+                    )
+                )
+            else:
+                temp.append(
+                    (col_values * converter[col_name][0]).rename(converter[col_name][1])
+                )
+        return pd.concat(temp, axis=1)
 
+    return data
 
-def weibull_pdf(wind_speeds: List[float]) -> Tuple[float]:
-    """Estimate the two-parameter Weibull parameters for a set of wind speeds.
+
+@bhom_analytics()
+def get_soil_temperatures(
+    latitude: float,
+    longitude: float,
+    start_date: datetime | str,
+    end_date: datetime | str,
+    include_dbt: bool = False,
+) -> pd.DataFrame:
+    """Query Open-Meteo for soil temperature data.
 
     Args:
-        wind_speeds (List[float]):
-            A list of wind speeds.
+        latitude (float):
+            The latitude of the target site, in degrees.
+        longitude (float):
+            The longitude of the target site, in degrees.
+        start_date (datetime | str):
+            The start-date from which records will be obtained.
+        end_date (datetime | str):
+            The end-date beyond which records will be ignored.
+        include_dbt (bool, optional):
+            Include dry bulb temperature in the output. Defaults to False.
 
     Returns:
-        k (float):
-            Shape parameter
-        loc (float):
-            Location parameter.
-        c (float):
-            Scale parameter.
+        pd.DataFrame:
+            A DataFrame containing scraped data.
     """
-    ws = np.array(wind_speeds)
-    ws = ws[ws != 0]
-    ws = ws[~np.isnan(ws)]
-    if ws.min() < 0:
-        raise ValueError("Wind speeds must be positive.")
-    try:
-        return weibull_min.fit(ws)
-    except ValueError as exc:
-        warnings.warn(f"Not enough data to calculate Weibull parameters.\n{exc}")
-        return (np.nan, np.nan, np.nan)  # type: ignore
-
-
-def circular_weighted_mean(angles: List[float], weights: List[float] = None):
+
+    variables = [
+        OpenMeteoVariable.SOIL_TEMPERATURE_0_TO_7CM,
+        OpenMeteoVariable.SOIL_TEMPERATURE_7_TO_28CM,
+        OpenMeteoVariable.SOIL_TEMPERATURE_28_TO_100CM,
+        OpenMeteoVariable.SOIL_TEMPERATURE_100_TO_255CM,
+    ]
+    if include_dbt:
+        variables.append(OpenMeteoVariable.TEMPERATURE_2M)
+
+    return scrape_openmeteo(
+        latitude=latitude,
+        longitude=longitude,
+        start_date=start_date,
+        end_date=end_date,
+        variables=variables,
+        convert_units=True,
+    )
+
+
+def circular_weighted_mean(angles: list[float], weights: list[float] = None):
     """Get the average angle from a set of weighted angles.
 
     Args:
-        angles (List[float]):
+        angles (list[float]):
             A collection of equally weighted wind directions, in degrees from North (0).
-        weights (List[float]):
+        weights (list[float]):
             A collection of weights, which must sum to 1. Defaults to None which will equally weight all angles.
 
     Returns:
@@ -1180,6 +1328,8 @@ def circular_weighted_mean(angles: List[float], weights: List[float] = None):
             An average wind direction.
     """
     angles = np.array(angles)
+    angles = np.where(angles == 360, 0, angles)
+
     if weights is None:
         weights = np.ones_like(angles) / len(angles)
     weights = np.array(weights)
@@ -1190,8 +1340,21 @@ def circular_weighted_mean(angles: List[float], weights: List[float] = None):
     if np.any(angles < 0) or np.any(angles > 360):
         raise ValueError("Input angles exist outside of expected range (0-360).")
 
-    weights = np.array(weights)
-    weights = weights / sum(weights)
+    # checks for opposing or equally spaced angles, with equal weighting
+    if len(set(weights)) == 1:
+        _sorted = np.sort(angles)
+        if len(set(angles)) == 2:
+            a, b = np.meshgrid(_sorted, _sorted)
+            if np.any(a - b == 180):
+                warnings.warn(
+                    "Input angles are opposing, meaning determining the mean is impossible. An attempt will be made to determine the mean, but this will be perpendicular to the opposing angles and not accurate."
+                )
+        if any(np.diff(_sorted) == 360 / len(angles)):
+            warnings.warn(
+                "Input angles are equally spaced, meaning determining the mean is impossible. An attempt will be made to determine the mean, but this will not be accurate."
+            )
+
+    weights = np.array(weights) / sum(weights)
 
     x = y = 0.0
     for angle, weight in zip(angles, weights):
@@ -1203,43 +1366,10 @@ def circular_weighted_mean(angles: List[float], weights: List[float] = None):
     if mean < 0:
         mean = 360 + mean
 
-    return mean
-
-
-def wind_direction_average(angles: List[float]) -> float:
-    """Get the average wind direction from a set of wind directions.
-
-    Args:
-        angles (List[float]):
-            A collection of equally weighted wind directions, in degrees from North (0).
+    if mean in (360.0, -0.0):
+        mean = 0.0
 
-    Returns:
-        float:
-            An average angle.
-    """
-
-    angles = np.array(angles)  # type: ignore
-
-    if np.any(angles < 0) or np.any(angles > 360):  # type: ignore
-        raise ValueError("Input angles exist outside of expected range (0-360).")
-
-    if len(angles) == 0:
-        return np.NaN
-
-    angles = np.radians(angles)  # type: ignore
-
-    average_angle = np.round(
-        np.arctan2(
-            (1 / len(angles) * np.sin(angles)).sum(),
-            (1 / len(angles) * np.cos(angles)).sum(),
-        ),
-        2,
-    )
-
-    if average_angle < 0:
-        average_angle = (np.pi * 2) - -average_angle
-
-    return np.degrees(average_angle)
+    return np.round(mean, 5)
 
 
 def wind_speed_at_height(
@@ -1258,29 +1388,39 @@ def wind_speed_at_height(
             The original height of the wind speed being translated.
         target_height (float):
             The target height of the wind speed being translated.
+
         **kwargs:
-            Additional keyword arguments to be passed to the
-            wind_speed_at_height function. This includes:
-                terrain_roughness_length (float):
-                    A value describing how rough the ground is. Default is
-                    0.03 for Open flat terrain; grass, few isolated obstacles.
-                log_function (bool, optional):
-                    Set to True to used the log transformation method, or
-                    False for the exponent method. Defaults to True.
+            Additional keyword arguments to pass to the translation method. These include:
+            terrain_roughness_length (float):
+                A value describing how rough the ground is. Default is
+                0.03 for Open flat terrain; grass, few isolated obstacles.
+            log_function (bool, optional):
+                Set to True to used the log transformation method, or
+                False for the exponent method. Defaults to True.
 
     Notes:
         Terrain roughness lengths can be found in the following table:
-            | Terrain description                               |  z0 (m)   |
-            ----------------------------------------------------|-----------|
-            | Open sea, Fetch at least 5 km                     |    0.0002 |
-            | Mud flats, snow; no vegetation, no obstacles      |    0.005  |
-            | Open flat terrain; grass, few isolated obstacle   |    0.03   |
-            | Low crops; occasional large obstacles, x/H > 20   |    0.10   |
-            | High crops; scattered obstacles, 15 < x/H < 20    |    0.25   |
-            | parkland, bushes; numerous obstacles, x/H ≈ 10    |    0.5    |
-            | Regular large obstacle coverage (suburb, forest)  |    1.0    |
-            | City centre with high- and low-rise buildings     |  ≥ 2      |
-            ----------------------------------------------------------------|
+
+        +---------------------------------------------------+-----------+
+        | Terrain description                               |  z0 (m)   |
+        +===================================================+===========+
+        | Open sea, Fetch at least 5 km                     |    0.0002 |
+        +---------------------------------------------------+-----------+
+        | Mud flats, snow; no vegetation, no obstacles      |    0.005  |
+        +---------------------------------------------------+-----------+
+        | Open flat terrain; grass, few isolated obstacle   |    0.03   |
+        +---------------------------------------------------+-----------+
+        | Low crops; occasional large obstacles, x/H > 20   |    0.10   |
+        +---------------------------------------------------+-----------+
+        | High crops; scattered obstacles, 15 < x/H < 20    |    0.25   |
+        +---------------------------------------------------+-----------+
+        | Parkland, bushes; numerous obstacles, x/H ≈ 10    |    0.5    |
+        +---------------------------------------------------+-----------+
+        | Regular large obstacle coverage (suburb, forest)  |    1.0    |
+        +---------------------------------------------------+-----------+
+        | City centre with high- and low-rise buildings     |    ≥ 2    |
+        +---------------------------------------------------+-----------+
+
 
     Returns:
         float:
@@ -1308,6 +1448,7 @@ def temperature_at_height(
     target_height: float,
     **kwargs,
 ) -> float:
+    # pylint: disable=C0301
     """Estimate the dry-bulb temperature at a given height from a referenced
         dry-bulb temperature at another height.
 
@@ -1319,7 +1460,7 @@ def temperature_at_height(
         target_height (float):
             The height to translate the reference temperature towards.
         **kwargs:
-            Additional keyword arguments to be passed to the temperature_at_height function. This includes:
+            Additional keyword arguments to pass to the translation method. These include:
             reduction_per_km_altitude_gain (float, optional):
                 The lapse rate of the atmosphere. Defaults to 0.0065 based
                 on https://scied.ucar.edu/learning-zone/atmosphere/change-atmosphere-altitude#:~:text=Near%20the%20Earth's%20surface%2C%20air,standard%20(average)%20lapse%20rate
@@ -1331,10 +1472,12 @@ def temperature_at_height(
         float:
             A translated air temperature.
     """
+    # pylint: enable=C0301
 
     if (target_height > 8000) or (reference_height > 8000):
         warnings.warn(
-            "The heights input into this calculation exist partially above the egde of the troposphere. This method is only valid below 8000m."
+            "The heights input into this calculation exist partially above "
+            "the egde of the troposphere. This method is only valid below 8000m."
         )
 
     lapse_rate = kwargs.get("lapse_rate", 0.0065)
@@ -1351,14 +1494,14 @@ def radiation_at_height(
     reference_height: float,
     **kwargs,
 ) -> float:
-    """Calculate the radiation at a given height, given a reference radiation
-        and height.
+    """Calculate the radiation at a given height, given a reference
+    radiation and height.
 
     References:
         Armel Oumbe, Lucien Wald. A parameterisation of vertical profile of
         solar irradiance for correcting solar fluxes for changes in terrain
-        elevation. Earth Observation and Water Cycle Science Conference,
-        Nov 2009, Frascati, Italy. pp.S05.
+        elevation. Earth Observation and Water Cycle Science Conference, Nov
+        2009, Frascati, Italy. pp.S05.
 
     Args:
         reference_value (float):
@@ -1368,10 +1511,9 @@ def radiation_at_height(
         reference_height (float, optional):
             The height at which the reference radiation was measured.
         **kwargs:
-            Additional keyword arguments to be passed to the
-            radiation_at_height function. This includes:
-                lapse_rate (float, optional):
-                    The lapse rate of the atmosphere. Defaults to 0.08.
+            Additional keyword arguments to pass to the translation method. These include:
+            lapse_rate (float, optional):
+                The lapse rate of the atmosphere. Defaults to 0.08.
 
     Returns:
         float:
@@ -1385,6 +1527,7 @@ def radiation_at_height(
     return reference_value + increase
 
 
+@bhom_analytics()
 def air_pressure_at_height(
     reference_value: float,
     target_height: float,
@@ -1410,6 +1553,7 @@ def air_pressure_at_height(
     )
 
 
+@bhom_analytics()
 def target_wind_speed_collection(
     epw: EPW, target_average_wind_speed: float, target_height: float
 ) -> HourlyContinuousCollection:
@@ -1445,6 +1589,7 @@ def target_wind_speed_collection(
     return epw.wind_speed * adjustment_factor
 
 
+@bhom_analytics()
 def dry_bulb_temperature_at_height(
     epw: EPW, target_height: float
 ) -> HourlyContinuousCollection:
@@ -1465,6 +1610,7 @@ def dry_bulb_temperature_at_height(
     return dbt_collection
 
 
+@bhom_analytics()
 def validate_timeseries(
     obj: Any,
     is_annual: bool = False,
@@ -1512,7 +1658,7 @@ def evaporative_cooling_effect(
     relative_humidity: float,
     evaporative_cooling_effectiveness: float,
     atmospheric_pressure: float = None,
-) -> List[float]:
+) -> list[float]:
     """
     For the inputs, calculate the effective DBT and RH values for the evaporative cooling
     effectiveness given.
@@ -1529,7 +1675,7 @@ def evaporative_cooling_effect(
             A pressure in Pa. Default is pressure at sea level (101325 Pa).
 
     Returns:
-        effective_dry_bulb_temperature, effective_relative_humidity (List[float]):
+        effective_dry_bulb_temperature, effective_relative_humidity (list[float]):
             A list of two values for the effective dry bulb temperature and relative humidity.
     """
 
@@ -1555,9 +1701,10 @@ def evaporative_cooling_effect(
     return [new_dbt, new_rh]
 
 
+@bhom_analytics()
 def evaporative_cooling_effect_collection(
     epw: EPW, evaporative_cooling_effectiveness: float = 0.3
-) -> List[HourlyContinuousCollection]:
+) -> list[HourlyContinuousCollection]:
     """Calculate the effective DBT and RH considering effects of evaporative cooling.
 
     Args:
@@ -1568,7 +1715,7 @@ def evaporative_cooling_effect_collection(
             of Misting.
 
     Returns:
-        List[HourlyContinuousCollection]:
+        list[HourlyContinuousCollection]:
             Adjusted dry-bulb temperature and relative humidity collections incorporating
             evaporative cooling effect.
     """
@@ -1605,9 +1752,8 @@ def evaporative_cooling_effect_collection(
     return [dbt, rh]
 
 
-def remove_leap_days(
-    pd_object: Union[pd.DataFrame, pd.Series]
-) -> Union[pd.DataFrame, pd.Series]:
+@bhom_analytics()
+def remove_leap_days(pd_object: pd.DataFrame | pd.Series) -> pd.DataFrame | pd.Series:
     """A removal of all timesteps within a time-indexed pandas
     object where the day is the 29th of February."""
 
@@ -1619,12 +1765,13 @@ def remove_leap_days(
     return pd_object[~mask]
 
 
+@bhom_analytics()
 def month_hour_binned_series(
     series: pd.Series,
-    month_bins: Tuple[Tuple[int]] = None,
-    hour_bins: Tuple[Tuple[int]] = None,
-    month_labels: Tuple[str] = None,
-    hour_labels: Tuple[str] = None,
+    month_bins: tuple[tuple[int]] = None,
+    hour_bins: tuple[tuple[int]] = None,
+    month_labels: tuple[str] = None,
+    hour_labels: tuple[str] = None,
     agg: str = "mean",
 ) -> pd.DataFrame:
     """Bin a series by hour and month.
@@ -1632,13 +1779,13 @@ def month_hour_binned_series(
     Args:
         series (pd.Series):
             A series with a datetime index.
-        hour_bins (Tuple[Tuple[int]], optional):
+        hour_bins (tuple[tuple[int]], optional):
             A list of lists of hours to bin by. Defaults to None which bins into the default_time_analysis_periods().
-        month_bins (Tuple[Tuple[int]], optional):
+        month_bins (tuple[tuple[int]], optional):
             A list of lists of months to bin by. Defaults to None which bins into default_month_analysis_periods.
-        hour_labels (List[str], optional):
+        hour_labels (list[str], optional):
             A list of labels to use for the hour bins. Defaults to None which just lists the hours in each bin.
-        month_labels (List[str], optional):
+        month_labels (list[str], optional):
             A list of labels to use for the month bins. Defaults to None which just lists the months in each bin.
         agg (str, optional):
             The aggregation method to use. Can be either "min", "mean", "median", "max" or "sum". Defaults to "mean".
@@ -1699,7 +1846,7 @@ def month_hour_binned_series(
         raise ValueError("hour_bins hours must be in the range 0-23")
     if (max(flat_months) != 12) or min(flat_months) != 1:
         raise ValueError("month_bins hours must be in the range 1-12")
-    # cehck for duplicates
+    # check for duplicates
     if len(set(flat_hours)) != len(flat_hours):
         raise ValueError("hour_bins hours must not contain duplicates")
     if len(set(flat_months)) != len(flat_months):
@@ -1745,3 +1892,77 @@ def month_hour_binned_series(
     df = pd.DataFrame(values, index=col_labels, columns=row_labels).T
 
     return df
+
+
+def sunrise_sunset(location: Location) -> pd.DataFrame():
+    """Calculate sunrise and sunset times for a given location and year. Includes
+    civil, nautical and astronomical twilight.
+
+    Args:
+        location (Location): The location to calculate sunrise and sunset for.
+
+    Returns:
+        pd.DataFrame: A DataFrame with sunrise and sunset times for each day of the year.
+    """
+
+    idx = pd.date_range("2017-01-01", "2017-12-31", freq="D")
+    sp = Sunpath.from_location(location)
+    df = pd.DataFrame(
+        [
+            {
+                **sp.calculate_sunrise_sunset_from_datetime(
+                    lb_datetime_from_datetime(ix), depression=0.5334
+                ),  # actual sunrise/set
+                **{
+                    f"civil {k}".replace("sunrise", "twilight start").replace(
+                        "sunset", "twilight end"
+                    ): v
+                    for k, v in sp.calculate_sunrise_sunset_from_datetime(
+                        lb_datetime_from_datetime(ix), depression=6
+                    ).items()
+                    if k != "noon"
+                },  # civil twilight
+                **{
+                    f"nautical {k}".replace("sunrise", "twilight start").replace(
+                        "sunset", "twilight end"
+                    ): v
+                    for k, v in sp.calculate_sunrise_sunset_from_datetime(
+                        lb_datetime_from_datetime(ix), depression=12
+                    ).items()
+                    if k != "noon"
+                },  # nautical twilight
+                **{
+                    f"astronomical {k}".replace("sunrise", "twilight start").replace(
+                        "sunset", "twilight end"
+                    ): v
+                    for k, v in sp.calculate_sunrise_sunset_from_datetime(
+                        lb_datetime_from_datetime(ix), depression=18
+                    ).items()
+                    if k != "noon"
+                },  # astronomical twilight
+            }
+            for ix in idx
+        ],
+        index=idx,
+    )
+    return df[
+        [
+            "astronomical twilight start",
+            "nautical twilight start",
+            "civil twilight start",
+            "sunrise",
+            "noon",
+            "sunset",
+            "civil twilight end",
+            "nautical twilight end",
+            "astronomical twilight end",
+        ]
+    ]
+
+
+@bhom_analytics()
+def safe_filename(filename: str) -> str:
+    """Remove all non-alphanumeric characters from a filename."""
+    return "".join(
+        [c for c in filename if c.isalpha() or c.isdigit() or c == " "]
+    ).strip()
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/model.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/model.py
new file mode 100644
index 00000000..afe37436
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/model.py
@@ -0,0 +1,360 @@
+"""Methods for handling honeybee models."""
+# pylint: disable=R0911
+from enum import Enum, auto  # pylint: disable=E0401
+
+import matplotlib.pyplot as plt
+from honeybee.model import AirBoundary, Face3D, Floor, Model, RoofCeiling, Wall
+from ladybug_geometry.geometry3d import LineSegment3D, Plane
+from matplotlib.collections import PolyCollection
+import numpy as np
+
+
+class HbModelGeometry(Enum):
+    """Honeybee model boundary types."""
+
+    WALL = auto()
+    FLOOR = auto()
+    ROOFCEILING = auto()
+    AIRBOUNDARY = auto()
+    SHADE = auto()
+    APERTURE = auto()
+    DOOR = auto()
+
+    def get_geometry(self, model: Model) -> list[object]:
+        """Return the objects of a model that correspond to the type of object."""
+        match self:
+            case HbModelGeometry.WALL:
+                return [i.geometry for i in model.faces if isinstance(i.type, Wall)]
+            case HbModelGeometry.FLOOR:
+                return [i.geometry for i in model.faces if isinstance(i.type, Floor)]
+            case HbModelGeometry.ROOFCEILING:
+                return [
+                    i.geometry for i in model.faces if isinstance(i.type, RoofCeiling)
+                ]
+            case HbModelGeometry.AIRBOUNDARY:
+                return [
+                    i.geometry for i in model.faces if isinstance(i.type, AirBoundary)
+                ]
+            case HbModelGeometry.SHADE:
+                return [i.geometry for i in model.shades]
+            case HbModelGeometry.APERTURE:
+                return [i.geometry for i in model.apertures]
+            case HbModelGeometry.DOOR:
+                return [i.geometry for i in model.doors]
+            case _:
+                raise ValueError(f"Invalid SliceColoring: {self}")
+
+    def slice_polycollection(self, model: Model, plane: Plane) -> PolyCollection:
+        """Slice a model with a plane and return a matplotlib PolyCollection.
+
+        Args:
+            model (Model): A Honeybee model to slice.
+            plane (Plane): A Ladybug 3D Plane object to slice the model with.
+
+        Returns:
+            PolyCollection: A matplotlib PolyCollection object.
+        """
+
+        geometries = self.get_geometry(model)
+        line_segments = plane_intersections(geometries, plane)
+
+        return linesegments_to_polycollection(
+            line_segments=line_segments,
+            facecolor=self.facecolor,
+            edgecolor=self.edgecolor,
+            alpha=self.alpha,
+            linewidth=self.linewidth,
+            zorder=self.zorder,
+        )
+
+    @property
+    def facecolor(self) -> str:
+        """Return the facecolor for the geometry type."""
+        match self:
+            case HbModelGeometry.WALL:
+                return "#E6B43C"
+            case HbModelGeometry.FLOOR:
+                return "#808080"
+            case HbModelGeometry.ROOFCEILING:
+                return "#801414"
+            case HbModelGeometry.AIRBOUNDARY:
+                return "#FFFFC8"
+            case HbModelGeometry.SHADE:
+                return "#784BBE"
+            case HbModelGeometry.APERTURE:
+                return "#40B4FF"
+            case HbModelGeometry.DOOR:
+                return "#A09664"
+            case _:
+                raise ValueError(f"Invalid SliceColoring: {self}")
+
+    @property
+    def edgecolor(self) -> str:
+        """Return the edgecolor for the geometry type."""
+        match self:
+            case HbModelGeometry.WALL:
+                return "#E6B43C"
+            case HbModelGeometry.FLOOR:
+                return "#808080"
+            case HbModelGeometry.ROOFCEILING:
+                return "#801414"
+            case HbModelGeometry.AIRBOUNDARY:
+                return "#FFFFC8"
+            case HbModelGeometry.SHADE:
+                return "#784BBE"
+            case HbModelGeometry.APERTURE:
+                return "#40B4FF"
+            case HbModelGeometry.DOOR:
+                return "#A09664"
+            case _:
+                raise ValueError(f"Invalid SliceColoring: {self}")
+
+    @property
+    def alpha(self) -> float:
+        """Return the alpha for the geometry type."""
+        match self:
+            case HbModelGeometry.WALL:
+                return 1
+            case HbModelGeometry.FLOOR:
+                return 1
+            case HbModelGeometry.ROOFCEILING:
+                return 1
+            case HbModelGeometry.AIRBOUNDARY:
+                return 1
+            case HbModelGeometry.SHADE:
+                return 1
+            case HbModelGeometry.APERTURE:
+                return 1
+            case HbModelGeometry.DOOR:
+                return 1
+            case _:
+                raise ValueError(f"Invalid SliceColoring: {self}")
+
+    @property
+    def linewidth(self) -> float:
+        """Return the linewidth for the geometry type."""
+        match self:
+            case HbModelGeometry.WALL:
+                return 1
+            case HbModelGeometry.FLOOR:
+                return 1
+            case HbModelGeometry.ROOFCEILING:
+                return 1
+            case HbModelGeometry.AIRBOUNDARY:
+                return 1
+            case HbModelGeometry.SHADE:
+                return 1
+            case HbModelGeometry.APERTURE:
+                return 1
+            case HbModelGeometry.DOOR:
+                return 1
+            case _:
+                raise ValueError(f"Invalid SliceColoring: {self}")
+
+    @property
+    def zorder(self) -> int:
+        """Set the default zorder of the geometry type."""
+        match self:
+            case HbModelGeometry.WALL:
+                return 6
+            case HbModelGeometry.FLOOR:
+                return 5
+            case HbModelGeometry.ROOFCEILING:
+                return 5
+            case HbModelGeometry.AIRBOUNDARY:
+                return 4
+            case HbModelGeometry.SHADE:
+                return 5
+            case HbModelGeometry.APERTURE:
+                return 7
+            case HbModelGeometry.DOOR:
+                return 7
+            case _:
+                raise ValueError(f"Invalid SliceColoring: {self}")
+
+
+def plane_intersections(
+    geometries: list[Face3D],
+    plane: Plane,
+) -> list[LineSegment3D]:
+    """Return the LineSegment3D objects that result from slicing a set of geometries with a plane.
+
+    Args:
+        geometries (list[Face3D]): A list of Face3D objects to slice.
+        plane (Plane): A Ladybug 3D Plane object to slice the model with.
+
+    Returns:
+        list[LineSegment3D]: A list of LineSegment3D objects resulting from the slice.
+    """
+
+    line_segments = []
+    for geometry in geometries:
+        try:
+            line_segments.extend(geometry.intersect_plane(plane))
+        except TypeError:
+            continue
+    return line_segments
+
+
+def linesegments_to_polycollection(
+    line_segments: list[LineSegment3D], plane: Plane = None, **kwargs
+) -> PolyCollection:
+    """Convert a list of LineSegment3D objects to a matplotlib PolyCollection.
+
+    Args:
+        line_segments (list[LineSegment3D]):
+            A list of LineSegment3D objects.
+        plane (Plane, optional):
+            A Ladybug 3D Plane object to project the segments onto. Defaults to None which does not project the
+            segments and instead asssumes the line segments are in an XY plane.
+        **kwargs:
+            Additional keyword arguments to pass to the matplotlib PolyCollection constructor.
+
+    Returns:
+        PolyCollection:
+            A matplotlib PolyCollection object.
+    """
+
+    plane = Plane() if plane is None else plane
+
+    _vertices = []
+    for segment in line_segments:
+        _vertices.append(
+            [
+                plane.xyz_to_xy(segment.p1).to_array(),
+                plane.xyz_to_xy(segment.p2).to_array(),
+            ]
+        )
+    return PolyCollection(verts=_vertices, closed=False, **kwargs)
+
+
+def slice_geometry(
+    hb_objects: list[object], plane: Plane, ax: plt.Axes = None, **kwargs
+) -> plt.Axes:
+    """Slice a set of Honeybee objects with a plane and plot their intersections.
+
+    Args:
+        hb_objects (list[object]):
+            A set of Honeybee objects to slice. These must have a geometry attribute.
+        plane (Plane):
+            A Ladybug 3D Plane object to slice the objects with.
+        ax (plt.Axes, optional):
+            The matplotlib axes to plot the intersections on. Defaults to None which uses the current axes.
+        **kwargs:
+            Additional keyword arguments to pass to the matplotlib PolyCollection constructor.
+
+    Returns:
+        plt.Axes:
+            A matplotlib axes with the intersections plotted.
+    """
+
+    if ax is None:
+        ax = plt.gca()
+
+    _vertices = []
+    for obj in hb_objects:
+        segments: list[LineSegment3D] = obj.geometry.intersect_plane(plane)
+        if segments is None:
+            continue
+        _vertices.extend(
+            [
+                [plane.xyz_to_xy(pt).to_array() for pt in segment.vertices]
+                for segment in segments
+            ]
+        )
+    _vertices = np.array(_vertices)
+    ax.add_artist(
+        PolyCollection(
+            verts=_vertices,
+            closed=False,
+            fc=None,
+            **kwargs,
+        ),
+    )
+
+    ax.autoscale_view()
+    ax.set_aspect("equal")
+
+    return ax
+
+
+def slice_model(model: Model, plane: Plane, ax: plt.Axes = None) -> plt.Axes:
+    """Slice a Honeybee model with a plane and plot the intersections.
+
+    Args:
+        model (Model): A Honeybee model to slice.
+        plane (Plane): A Ladybug 3D Plane object to slice the model with.
+        ax (plt.Axes, optional): The matplotlib axes to plot the intersections on.
+        Defaults to None which uses the current axes.
+
+    Returns:
+        plt.Axes: A matplotlib axes with the intersections plotted.
+    """
+
+    if ax is None:
+        ax = plt.gca()
+
+    meta = {
+        "wall": {
+            "objects": [i for i in model.faces if isinstance(i.type, Wall)],
+            "poly_kwargs": {
+                "color": "grey",
+                "zorder": 4,
+                "alpha": 1,
+                "lw": 0.5,
+            },
+        },
+        "floor": {
+            "objects": [i for i in model.faces if isinstance(i.type, Floor)],
+            "poly_kwargs": {
+                "color": "black",
+                "zorder": 5,
+                "alpha": 1,
+                "lw": 0.5,
+            },
+        },
+        "roofceiling": {
+            "objects": [i for i in model.faces if isinstance(i.type, RoofCeiling)],
+            "poly_kwargs": {
+                "color": "brown",
+                "zorder": 5,
+                "alpha": 1,
+                "lw": 0.5,
+            },
+        },
+        "airboundary": {
+            "objects": [i for i in model.faces if isinstance(i.type, AirBoundary)],
+            "poly_kwargs": {
+                "color": "pink",
+                "zorder": 3,
+                "alpha": 1,
+                "lw": 0.5,
+            },
+        },
+        "shade": {
+            "objects": model.shades,
+            "poly_kwargs": {
+                "color": "green",
+                "zorder": 3,
+                "alpha": 1,
+                "lw": 0.5,
+            },
+        },
+        "aperture": {
+            "objects": model.apertures,
+            "poly_kwargs": {
+                "color": "blue",
+                "zorder": 6,
+                "alpha": 1,
+                "lw": 0.5,
+            },
+        },
+    }
+
+    for _, v in meta.items():
+        slice_geometry(hb_objects=v["objects"], plane=plane, ax=ax, **v["poly_kwargs"])
+
+    ax.autoscale_view()
+    ax.set_aspect("equal")
+
+    return ax
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/results.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/results.py
index 6b0ee96d..aac11629 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/results.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/results.py
@@ -1,49 +1,59 @@
+"""Methods for loading Honeybee results files into Pandas DataFrames."""
+
+# pylint: disable=E0401
 import datetime
 import warnings
 from pathlib import Path
-from typing import Callable, List, Union
+from typing import Callable
+
+# pylint: enable=E0401
 
 import numpy as np
 import pandas as pd
 from ladybug.sql import SQLiteResult
-
+from ..bhom.analytics import bhom_analytics
 from ..ladybug_extension.datacollection import collection_to_series
 
 
-def load_files(func: Callable, files: List[Union[str, Path]]) -> pd.DataFrame:
+def _load_files(func: Callable, files: list[Path]) -> pd.DataFrame:
     """Load a set of input files and combine into a DataFrame with filename as header.
 
     Args:
         func (Callable):
             The function to use to load each of the files.
-        files (List[Union[str, Path]]):
+        files (list[Path]):
             A list of paths to the input files.
 
     Returns:
         pd.DataFrame: A DataFrame containing the data from the input files.
     """
+
     if isinstance(files, (str, Path)):
         files = [files]
 
+    files = [Path(i) for i in files]
+
     if len(files) == 0:
         raise FileNotFoundError("No files of the specified type were found.")
 
-    filenames = [Path(i).stem for i in files]
+    filenames = [i.stem for i in files]
     if len(set(filenames)) != len(filenames):
-        err_str = f"There are duplicate filenames in the list of input files for {func.__name__}. This may cause issues when trying to reference specific results sets!"
+        err_str = (
+            "There are duplicate filenames in the list of input files for "
+            f"{func.__name__}. This may cause issues when trying to reference "
+            "specific results sets!"
+        )
         warnings.warn(err_str)
 
     return pd.concat([func(i) for i in files], axis=1).sort_index(axis=1)
 
 
-def load_ill_file(
-    ill_file: Union[str, Path], sun_up_hours_file: Union[str, Path] = None
-) -> pd.DataFrame:
+def _load_ill_file(ill_file: Path, sun_up_hours_file: Path = None) -> pd.DataFrame:
     """Load a Radiance .ill file and return a DataFrame with the data.
 
     Args:
-        ill_file (Union[str, Path]): The path to the Radiance .ill file.
-        sun_up_hours_file (Union[str, Path], optional): The path to the sun-up-hours.txt file.
+        ill_file (Path): The path to the Radiance .ill file.
+        sun_up_hours_file (Path, optional): The path to the sun-up-hours.txt file.
 
     Returns:
         pd.DataFrame: A DataFrame containing the data from the .ill file.
@@ -52,6 +62,8 @@ def load_ill_file(
 
     if sun_up_hours_file is None:
         sun_up_hours_file = ill_file.parent / "sun-up-hours.txt"
+    else:
+        sun_up_hours_file = Path(sun_up_hours_file)
 
     df = pd.read_csv(ill_file, sep=r"\s+", header=None, index_col=None).T
     df.columns = pd.MultiIndex.from_product([[ill_file.stem], df.columns])
@@ -59,23 +71,26 @@ def load_ill_file(
     return df
 
 
-def load_ill(ill_files: Union[str, Path, List[Union[str, Path]]]) -> pd.DataFrame:
+@bhom_analytics()
+def load_ill(ill_files: Path | list[Path]) -> pd.DataFrame:
     """Load a single Radiance .ill file, or list of Radiance .ill files and return a combined DataFrame with the data.
 
     Args:
-        ill_files (Union[str, Path, List[Union[str, Path]]]): A single .ill file, or a list of .ill files.
+        ill_files (Path | list[Path]):
+            A single .ill file, or a list of .ill files.
 
     Returns:
-        pd.DataFrame: A DataFrame containing the data from the input .ill files.
+        pd.DataFrame:
+            A DataFrame containing the data from the input .ill files.
     """
-    return load_files(load_ill_file, ill_files)
+    return _load_files(_load_ill_file, ill_files)
 
 
-def load_npy_file(npy_file: Union[str, Path]) -> pd.DataFrame:
+def _load_npy_file(npy_file: Path) -> pd.DataFrame:
     """Load a Honeybee-Radiance .npy file and return a DataFrame with the data.
 
     Args:
-        npy_file (Union[str, Path]):
+        npy_file (Path):
             The path to the Radiance/Honeybee .npy file.
 
     Returns:
@@ -86,7 +101,7 @@ def load_npy_file(npy_file: Union[str, Path]) -> pd.DataFrame:
 
     # get the "results" directory and sun-up-hours file
     for parent in npy_file.parents:
-        if parent.name == "results":
+        if parent.name in ["results", "assets"]:
             sun_up_hours_file = parent / "sun-up-hours.txt"
             break
 
@@ -96,23 +111,25 @@ def load_npy_file(npy_file: Union[str, Path]) -> pd.DataFrame:
     return df
 
 
-def load_npy(npy_files: Union[str, Path, List[Union[str, Path]]]) -> pd.DataFrame:
-    """Load a single Honeybee-Radiance .npy file, or list of Honeybee-Radiance .npy files and return a combined DataFrame with the data.
+@bhom_analytics()
+def load_npy(npy_files: Path | list[Path]) -> pd.DataFrame:
+    """Load a single Honeybee-Radiance .npy file, or list of Honeybee-Radiance
+    .npy files and return a combined DataFrame with the data.
 
     Args:
-        npy_files (Union[str, Path, List[Union[str, Path]]]): A single .npy file, or a list of .npy files.
+        npy_files (Path | list[Path]): A single .npy file, or a list of .npy files.
 
     Returns:
         pd.DataFrame: A DataFrame containing the data from the input .npy files.
     """
-    return load_files(load_npy_file, npy_files)
+    return _load_files(_load_npy_file, npy_files)
 
 
-def load_pts_file(pts_file: Union[str, Path]) -> pd.DataFrame:
+def _load_pts_file(pts_file: Path) -> pd.DataFrame:
     """Load a Radiance .pts file and return a DataFrame with the data.
 
     Args:
-        pts_file (Union[str, Path]): The path to the Radiance .pts file.
+        pts_file (Path): The path to the Radiance .pts file.
 
     Returns:
         pd.DataFrame: A DataFrame containing the data from the .pts file.
@@ -125,25 +142,26 @@ def load_pts_file(pts_file: Union[str, Path]) -> pd.DataFrame:
     return df
 
 
-def load_pts(pts_files: Union[str, Path, List[Union[str, Path]]]) -> pd.DataFrame:
+@bhom_analytics()
+def load_pts(pts_files: Path | list[Path]) -> pd.DataFrame:
     """Load a single Radiance .pts file, or list of Radiance .pts files and return a combined DataFrame with the data.
 
     Args:
-        pts_files (Union[str, Path, List[Union[str, Path]]]): A single .pts file, or a list of .pts files.
+        pts_files (Path | list[Path]): A single .pts file, or a list of .pts files.
 
     Returns:
         pd.DataFrame: A DataFrame containing the data from the input .pts files.
     """
-    return load_files(load_pts_file, pts_files)
+    return _load_files(_load_pts_file, pts_files)
 
 
-def load_res_file(res_file: Union[str, Path]) -> pd.Series:
+def _load_res_file(res_file: Path) -> pd.Series:
     """Load a Radiance .res file and return a DataFrame with the data.
 
     NOTE: This also works with daylight metrics files (da, cda, udi, udi_lower and udi_upper).
 
     Args:
-        res_file (Union[str, Path]): The path to the Radiance .res file.
+        res_file (Path): The path to the Radiance .res file.
 
     Returns:
         pd.DataFrame: A DataFrame containing the data from the .res file.
@@ -154,25 +172,26 @@ def load_res_file(res_file: Union[str, Path]) -> pd.Series:
     return series
 
 
-def load_res(res_files: Union[str, Path, List[Union[str, Path]]]) -> pd.DataFrame:
+@bhom_analytics()
+def load_res(res_files: Path | list[Path]) -> pd.DataFrame:
     """Load a single Radiance .res file, or list of Radiance .res files and return a combined DataFrame with the data.
 
     NOTE: This also works with daylight metrics files (da, cda, udi, udi_lower and udi_upper).
 
     Args:
-        res_files (Union[str, Path, List[Union[str, Path]]]): A single .res file, or a list of .res files.
+        res_files (Path | list[Path]): A single .res file, or a list of .res files.
 
     Returns:
         pd.DataFrame: A DataFrame containing the data from the input .res files.
     """
-    return load_files(load_res_file, res_files)
+    return _load_files(_load_res_file, res_files)
 
 
-def load_sql_file(sql_file: Union[str, Path]) -> pd.DataFrame:
+def _load_sql_file(sql_file: Path) -> pd.DataFrame:
     """Return a DataFrame with hourly values along rows and variables along columns.
 
     Args:
-        sql_file (Union[str, Path]): The path to the EnergyPlus .sql file.
+        sql_file (Path): The path to the EnergyPlus .sql file.
 
     Returns:
         pd.DataFrame: A pandas DataFrame containing the data from the .sql file.
@@ -228,29 +247,33 @@ def _flatten(container):
     return df
 
 
-def load_sql(sql_files: Union[str, Path, List[Union[str, Path]]]) -> pd.DataFrame:
-    """Load a single EnergyPlus .sql file, or list of EnergyPlus .sql files and return a combined DataFrame with the data.
+@bhom_analytics()
+def load_sql(sql_files: Path | list[Path]) -> pd.DataFrame:
+    """Load a single EnergyPlus .sql file, or list of EnergyPlus .sql
+    files and return a combined DataFrame with the data.
 
     Args:
-        sql_files (Union[str, Path, List[Union[str, Path]]]): A single .sql file, or a list of .sql files.
+        sql_files (Path | list[Path]): A single .sql file, or a list of .sql files.
 
     Returns:
         pd.DataFrame: A DataFrame containing the data from the input .sql files.
     """
-    return load_files(load_sql_file, sql_files)
+    return _load_files(_load_sql_file, sql_files)
 
 
-def load_sun_up_hours(
-    sun_up_hours_file: Union[str, Path], year: int = 2017
-) -> pd.DatetimeIndex:
+@bhom_analytics()
+def load_sun_up_hours(sun_up_hours_file: Path, year: int = 2017) -> pd.DatetimeIndex:
     """Load a HB-Radiance generated sun-up-hours.txt file and return a DatetimeIndex with the data.
 
     Args:
-        sun_up_hours_file (Union[str, Path]): Path to the sun-up-hours.txt file.
-        year (int, optional): The year to be used to generate the DatetimeIndex. Defaults to 2017.
+        sun_up_hours_file (Path):
+            Path to the sun-up-hours.txt file.
+        year (int, optional):
+            The year to be used to generate the DatetimeIndex. Defaults to 2017.
 
     Returns:
-        pd.DatetimeIndex: A Pandas DatetimeIndex with the data from the sun-up-hours.txt file.
+        pd.DatetimeIndex:
+            A Pandas DatetimeIndex with the data from the sun-up-hours.txt file.
     """
 
     sun_up_hours_file = Path(sun_up_hours_file)
@@ -262,6 +285,7 @@ def load_sun_up_hours(
     return index
 
 
+@bhom_analytics()
 def make_annual(df: pd.DataFrame) -> pd.DataFrame:
     """Convert a DataFrame with partial annual data to a DataFrame with annual data.
 
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/simulation/__init__.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/simulation/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/simulation/radiance.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/simulation/radiance.py
new file mode 100644
index 00000000..da7dbb96
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/simulation/radiance.py
@@ -0,0 +1,864 @@
+"""_"""
+# pylint: disable=E0401
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from honeybee.config import folders as hb_folders
+from honeybee.model import Model
+from honeybee_radiance.sensorgrid import SensorGrid
+from honeybee_radiance.writer import _filter_by_pattern
+from honeybee_radiance_command.options.rfluxmtx import RfluxmtxOptions
+from honeybee_radiance_command.options.rpict import RpictOptions
+from honeybee_radiance_command.options.rtrace import RtraceOptions
+from honeybee_radiance_postprocess.results import _filter_grids_by_pattern
+from ladybug.analysisperiod import AnalysisPeriod
+from ladybug.wea import Wea
+from ladybug_geometry.geometry3d import Plane, Point3D
+from lbt_recipes.recipe import Recipe, RecipeSettings
+from matplotlib import pyplot as plt
+from matplotlib.colors import Normalize
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+from ...bhom.logging import CONSOLE_LOGGER
+from ...ladybug_extension.analysisperiod import describe_analysis_period
+from ..model import HbModelGeometry
+from ..results import load_ill, load_npy, load_res
+from .sensorgrids import as_patchcollection, get_limits, groupby_level
+
+# pylint: enable=E0401
+
+
+def radiance_parameters(
+    model: Model,
+    detail_dim: float,
+    recipe_type: str,
+    detail_level: int = 0,
+    additional_parameters: str = None,
+) -> str:
+    """Generate the default "recommended" Radiance parameters for a Honeybee
+    Radiance simulation.
+
+    This method also includes the estimation of ambient resolution based on the
+    model dimensions.
+
+    Args:
+        model: Model
+            A Honeybee Model.
+        detail_dim: float
+            The detail dimension in meters.
+        recipe_type: str
+            One of the following: 'point-in-time-grid', 'daylight-factor',
+            'point-in-time-image', 'annual'.
+        detail_level: int
+            One of 0 (low), 1 (medium) or 2 (high).
+        additional_parameters: str
+            Additional parameters to add to the Radiance command. Should be in
+            the format of a Radiance command string e.g. '-ab 2 -aa 0.25'.
+
+    Returns:
+        str: The Radiance parameters as a string.
+    """
+
+    # recommendations for radiance parameters
+    rtrace = {
+        "ab": [2, 3, 6],
+        "ad": [512, 2048, 4096],
+        "as_": [128, 2048, 4096],
+        "ar": [16, 64, 128],
+        "aa": [0.25, 0.2, 0.1],
+        "dj": [0, 0.5, 1],
+        "ds": [0.5, 0.25, 0.05],
+        "dt": [0.5, 0.25, 0.15],
+        "dc": [0.25, 0.5, 0.75],
+        "dr": [0, 1, 3],
+        "dp": [64, 256, 512],
+        "st": [0.85, 0.5, 0.15],
+        "lr": [4, 6, 8],
+        "lw": [0.05, 0.01, 0.005],
+        "ss": [0, 0.7, 1],
+    }
+
+    rpict = {
+        "ab": [2, 3, 6],
+        "ad": [512, 2048, 4096],
+        "as_": [128, 2048, 4096],
+        "ar": [16, 64, 128],
+        "aa": [0.25, 0.2, 0.1],
+        "ps": [8, 4, 2],
+        "pt": [0.15, 0.10, 0.05],
+        "pj": [0.6, 0.9, 0.9],
+        "dj": [0, 0.5, 1],
+        "ds": [0.5, 0.25, 0.05],
+        "dt": [0.5, 0.25, 0.15],
+        "dc": [0.25, 0.5, 0.75],
+        "dr": [0, 1, 3],
+        "dp": [64, 256, 512],
+        "st": [0.85, 0.5, 0.15],
+        "lr": [4, 6, 8],
+        "lw": [0.05, 0.01, 0.005],
+        "ss": [0, 0.7, 1],
+    }
+
+    rfluxmtx = {
+        "ab": [3, 5, 6],
+        "ad": [5000, 15000, 25000],
+        "as_": [128, 2048, 4096],
+        "ds": [0.5, 0.25, 0.05],
+        "dt": [0.5, 0.25, 0.15],
+        "dc": [0.25, 0.5, 0.75],
+        "dr": [0, 1, 3],
+        "dp": [64, 256, 512],
+        "st": [0.85, 0.5, 0.15],
+        "lr": [4, 6, 8],
+        "lw": [0.000002, 6.67e-07, 4e-07],
+        "ss": [0, 0.7, 1],
+        "c": [1, 1, 1],
+    }
+
+    # VALIDATION
+    recipe_types = {
+        "point-in-time-grid": [rtrace, RtraceOptions()],
+        "daylight-factor": [rtrace, RtraceOptions()],
+        "point-in-time-image": [rpict, RpictOptions()],
+        "annual": [rfluxmtx, RfluxmtxOptions()],
+        "annual-daylight": [rfluxmtx, RfluxmtxOptions()],
+        "annual-irradiance": [rfluxmtx, RfluxmtxOptions()],
+        "sky-view": [rtrace, RtraceOptions()],
+    }
+    if recipe_type not in recipe_types:
+        raise ValueError(
+            f"recipe_type ({recipe_type}) must be one of {recipe_types.keys()}"
+        )
+
+    if detail_level not in [0, 1, 2]:
+        raise ValueError(
+            r"detail_level ({detail_level}) must be one of 0 (low), 1 (medium) or 2 (high)."
+        )
+
+    options, obj = recipe_types[recipe_type]
+    for opt, vals in options.items():
+        setattr(obj, opt, vals[detail_level])
+
+    min_pt, max_pt = model.min, model.max
+    x_dim = max_pt.x - min_pt.x
+    y_dim = max_pt.y - min_pt.y
+    z_dim = max_pt.z - min_pt.z
+    longest_dim = max((x_dim, y_dim, z_dim))
+    try:
+        obj.ar = int((longest_dim * obj.aa) / detail_dim)
+    except TypeError as _:
+        obj.ar = int((longest_dim * 0.1) / detail_dim)
+
+    if additional_parameters:
+        obj.update_from_string(additional_parameters)
+
+    return obj.to_radiance()
+
+
+@dataclass(init=True, repr=True)
+class HoneybeeRadiance:
+    """A class for handling simulation using Radiance via Honeybee."""
+
+    model: Model = field(init=True, repr=True)
+
+    def __post_init__(self):
+        # check that model contains sensor grids and can be simulated.
+        if not self.model.properties.radiance.sensor_grids:
+            raise ValueError(
+                "The model does not contain any sensor grids. Please add sensor grids to the model."
+            )
+
+    @classmethod
+    def from_hbjson(cls, hbjson_file: Path) -> "HoneybeeRadiance":
+        """_"""
+        hbjson_file = Path(hbjson_file)
+        return cls(Model.from_hbjson(hbjson_file.as_posix()))
+
+    @property
+    def model_name(self) -> str:
+        """The name of the model."""
+        return self.model.identifier
+
+    @property
+    def output_directory(self) -> Path:
+        """_"""
+        _dir = Path(hb_folders.default_simulation_folder) / self.model_name
+        _dir.mkdir(exist_ok=True, parents=True)
+        return _dir
+
+    def get_sensorgrids(
+        self,
+        grids_filter: tuple[str] = "*",
+    ) -> list[SensorGrid]:
+        """Return a list of sensor grids for the given model.
+
+        Args:
+            grids_filter (Tuple[str], optional):
+                A list of strings to filter the grids.
+
+        Returns:
+            list[SensorGrid]:
+                A list of sensor grids.
+        """
+        if not isinstance(grids_filter, (list, tuple, str)):
+            raise ValueError(
+                "The grids_filter must be a string, list or tuple of strings."
+            )
+        return _filter_by_pattern(
+            self.model.properties.radiance.sensor_grids, grids_filter
+        )
+
+    def get_levels(self) -> list[float]:
+        """Return a list of levels for the given model.
+
+        Returns:
+            list[float]:
+                A list of levels.
+        """
+        level_grids = groupby_level(self.get_sensorgrids())
+        return list(level_grids.keys())
+
+    def simulate_daylight_factor(
+        self,
+        reload_old: bool = True,
+        delete_tempfiles: bool = True,
+        detail_level: int = 0,
+        detail_dim: float = 0.15,
+    ) -> Path:
+        """Run a daylight factor recipe for the current model.
+
+        Args:
+            reload_old (bool, optional):
+                Instead of running a full simulation, reload the existing results ... if they exist.
+                Defaults to True.
+            delete_tempfiles (bool, optional):
+                Delete temporary files created during simulation. This should roughly half the results directory size.
+                Defaults to True.
+            detail_level (float, optional):
+                Set the detail level of the simulation.
+                Defaults to 0 which is the lowest detail level.
+            detail_dim (float, optional):
+                Set the minimum geometry size to be accounted for in the raytracing.
+                Defaults to 0.15.
+
+        Returns:
+            Path:
+                The directory containing simulation results
+        """
+
+        recipe_name = "daylight-factor"
+        folder_name = "daylight_factor"
+
+        CONSOLE_LOGGER.info(f"Running {recipe_name} recipe for model {self.model_name}")
+
+        recipe = Recipe(recipe_name)
+        params = radiance_parameters(
+            model=self.model,
+            detail_dim=detail_dim,
+            recipe_type=recipe_name,
+            detail_level=detail_level,
+        )
+        recipe.input_value_by_name("model", self.model)
+        recipe.input_value_by_name("radiance-parameters", params)
+
+        settings = RecipeSettings(
+            folder=self.output_directory.as_posix(), reload_old=reload_old
+        )
+
+        results_folder = recipe.run(settings)
+
+        if delete_tempfiles:
+            for fp in (Path(results_folder) / f"{folder_name}/initial_results").glob(
+                "**/*.res"
+            ):
+                fp.unlink()
+
+        return Path(results_folder) / folder_name
+
+    def daylight_factor(
+        self,
+        grids_filter: tuple[str] = "*",
+    ) -> pd.DataFrame:
+        """Obtain daylight factor results for the given model simulation folder.
+
+        Args:
+            grids_filter (Tuple[str], optional):
+                A list of strings to filter the grids to return.
+                Defaults to ("*") which includes all grids.
+
+        Returns:
+            pd.DataFrame:
+                A pandas DataFrame with daylight factor results per grid requested.
+        """
+
+        results_folder = self.output_directory / "daylight_factor/results"
+        if not results_folder.exists():
+            raise ValueError(
+                "The given folder does not contain daylight factor results. Try running a simulation first."
+            )
+
+        if not isinstance(grids_filter, (list, tuple, str)):
+            raise ValueError(
+                "The grids_filter must be a string, list or tuple of strings."
+            )
+
+        with open(results_folder / "grids_info.json") as f:
+            grids_info = json.load(f)
+
+        renamer = {gi["identifier"]: gi["name"] for gi in grids_info}
+
+        files = [
+            results_folder / f"{i['identifier']}.res"
+            for i in _filter_grids_by_pattern(grids_info, grids_filter)
+        ]
+
+        return (pd.concat([load_res(files)], axis=1).sort_index(axis=1)).rename(
+            columns=renamer, level=0
+        )
+
+    def summarise_daylight_factor(
+        self,
+        grids_filter: tuple[str] = "*",
+    ) -> pd.DataFrame:
+        """Summarise daylight factor results for the given model simulation folder.
+
+        Args:
+            grids_filter (Tuple[str], optional):
+                A list of strings to filter the grids to return.
+                Defaults to ("*") which includes all grids.
+
+        Returns:
+            pd.DataFrame:
+                A pandas DataFrame with daylight factor results per grid requested.
+        """
+
+        summaries = []
+        for grid, (grid_name, values) in list(
+            zip(
+                *[
+                    self.get_sensorgrids(grids_filter),
+                    self.daylight_factor(grids_filter).items(),
+                ]
+            )
+        ):
+            vals = values.dropna().values
+            summaries.append(
+                pd.Series(
+                    {
+                        "Area": grid.mesh.area,
+                        "Minimum": vals.min(),
+                        "Average": vals.mean(),
+                        "Median": np.median(vals),
+                        "Maximum": vals.max(),
+                        "Uniformity": vals.min() / vals.mean(),
+                        "Area <2%": np.array(grid.mesh.face_areas)[vals < 2].sum()
+                        / grid.mesh.area
+                        * 100,
+                        "Area >5%": np.array(grid.mesh.face_areas)[vals > 5].sum()
+                        / grid.mesh.area
+                        * 100,
+                    },
+                    name=grid_name,
+                )
+            )
+        return pd.DataFrame(summaries)
+
+    def plot_daylight_factor(
+        self,
+        grids_filter: tuple[str] = "*",
+    ) -> list[Path]:
+        """Plot daylight factor results for the given model simulation folder.
+
+        Args:
+            grids_filter (Tuple[str], optional):
+                A list of strings to filter the grids to return.
+                Defaults to ("*") which includes all grids.
+
+        Returns:
+            list[Path]:
+                A list of paths to the plots.
+        """
+
+        plot_directory = self.output_directory / "plots"
+        plot_directory.mkdir(exist_ok=True, parents=True)
+
+        level_grids = groupby_level(self.get_sensorgrids(grids_filter))
+        results = self.daylight_factor(grids_filter)
+
+        sps = []
+        for nn, (level, grids) in enumerate(level_grids.items()):
+            fig, ax = plt.subplots(
+                1,
+                1,
+            )
+            ax.set_aspect("equal")
+            ax.autoscale()
+            ax.axis("off")
+            ax.set_title(f"{level:0.2f}m (Z-level)")
+
+            # add grid/values
+            for grid in grids:
+                vals = results[grid.full_identifier].dropna().values
+                pc = as_patchcollection(
+                    sensorgrid=grid, cmap="magma", norm=Normalize(vmin=0, vmax=10)
+                )
+                pc.set_array(vals)
+                ax.add_collection(pc)
+
+            # add colorbar
+            divider = make_axes_locatable(ax)
+            cax = divider.append_axes("right", size="5%", pad=0.15)
+            cb = fig.colorbar(
+                mappable=ax.get_children()[0], cax=cax, label="Daylight Factor (%)"
+            )
+            cb.outline.set_visible(False)
+
+            # add wireframe
+            for b in HbModelGeometry:
+                pc = b.slice_polycollection(
+                    model=self.model, plane=Plane(o=Point3D(0, 0, level + 0.5))
+                )
+                ax.add_collection(pc)
+
+            xlims, ylims = get_limits(grids)
+            ax.set_xlim(xlims)
+            ax.set_ylim(ylims)
+
+            plt.tight_layout()
+
+            # save the figure
+            sp = plot_directory / f"daylight_factor_{nn:02d}.png"
+            fig.savefig(sp, dpi=300, transparent=True)
+            plt.close(fig)
+            sps.append(sp)
+
+        return sps
+
+    def simulate_annual_daylight(
+        self,
+        wea: Wea,
+        reload_old: bool = True,
+        delete_tempfiles: bool = True,
+        north: float = 0,
+        detail_level: int = 0,
+        detail_dim: float = 0.15,
+    ) -> Path:
+        """Run an annual daylight recipe for the model.
+
+        Args:
+            wea (Wea):
+                A Ladybug Wea object.
+            reload_old (bool, optional):
+                Instead of running a full simulation, reload the existing results ... if they exist.
+                Defaults to True.
+            delete_tempfiles (bool, optional):
+                Delete temporary files created during simulation. This should roughly half the results directory size.
+                Defaults to True.
+            north (float, optional):
+                Set the angle to north. Defaults to 0 which assumes the model is correctly oriented.
+            detail_level (float, optional):
+                Set the detail level of the simulation. Defaults to 0 which is the lowest detail level.
+            detail_dim (float, optional):
+                Set the minimum geometry size to be accounted for in the raytracing. Defaults to 0.15.
+
+        Returns:
+            Path:
+                The directory containing simulation results
+        """
+
+        recipe_name = "annual-daylight"
+        folder_name = "annual_daylight"
+
+        CONSOLE_LOGGER.info(f"Running {recipe_name} recipe for model {self.model_name}")
+
+        recipe = Recipe(recipe_name)
+        params = radiance_parameters(
+            model=self.model,
+            detail_dim=detail_dim,
+            recipe_type=recipe_name,
+            detail_level=detail_level,
+        )
+        recipe.input_value_by_name("model", self.model)
+        recipe.input_value_by_name("wea", wea)
+        recipe.input_value_by_name("radiance-parameters", params)
+        recipe.input_value_by_name("north", north)
+
+        settings = RecipeSettings(
+            folder=self.output_directory.as_posix(), reload_old=reload_old
+        )
+
+        results_folder = recipe.run(settings)
+
+        if delete_tempfiles:
+            for fp in (Path(results_folder) / f"{folder_name}/calcs").glob("**/*.ill"):
+                fp.unlink()
+
+        return Path(results_folder) / folder_name
+
+    def annual_daylight(
+        self,
+        grids_filter: tuple[str] = "*",
+    ) -> pd.DataFrame:
+        """Obtain annual daylight results for the given model simulation folder.
+
+        Args:
+            grids_filter (Tuple[str], optional):
+                A list of strings to filter the grids.
+                Defaults to ("*") which includes all grids.
+
+        Returns:
+            pd.DataFrame:
+                A pandas DataFrame with daylight results per grid requested.
+        """
+
+        results_folder = self.output_directory / "annual_daylight/results"
+        if not results_folder.exists():
+            raise ValueError(
+                "The given folder does not contain annual daylight results. Try running a simulation first."
+            )
+
+        if not isinstance(grids_filter, (list, tuple, str)):
+            raise ValueError(
+                "The grids_filter must be a string, list or tuple of strings."
+            )
+
+        with open(results_folder / "grids_info.json") as f:
+            grids_info = json.load(f)
+
+        renamer = {gi["identifier"]: gi["name"] for gi in grids_info}
+
+        files = [
+            results_folder / f"__static_apertures__/default/total/{i['identifier']}.npy"
+            for i in _filter_grids_by_pattern(grids_info, grids_filter)
+        ]
+
+        return (pd.concat([load_npy(files)], axis=1).sort_index(axis=1)).rename(
+            columns=renamer, level=0
+        )
+
+    def simulate_annual_irradiance(
+        self,
+        wea: Wea,
+        reload_old: bool = True,
+        delete_tempfiles: bool = True,
+        north: float = 0,
+        detail_level: int = 0,
+        detail_dim: float = 0.15,
+    ) -> Path:
+        """Run an annual irradiance recipe for the model.
+
+        Args:
+            wea (Wea):
+                A Ladybug Wea object.
+            reload_old (bool, optional):
+                Instead of running a full simulation, reload the existing results ... if they exist.
+                Defaults to True.
+            delete_tempfiles (bool, optional):
+                Delete temporary files created during simulation. This should roughly half the results directory size.
+                Defaults to True.
+            north (float, optional):
+                Set the angle to north. Defaults to 0 which assumes the model is correctly oriented.
+            detail_level (float, optional):
+                Set the detail level of the simulation. Defaults to 0 which is the lowest detail level.
+            detail_dim (float, optional):
+                Set the minimum geometry size to be accounted for in the raytracing. Defaults to 0.15.
+
+        Returns:
+            Path:
+                The directory containing simulation results
+        """
+
+        recipe_name = "annual-irradiance"
+        folder_name = "annual_irradiance"
+
+        CONSOLE_LOGGER.info(f"Running {recipe_name} recipe for model {self.model_name}")
+
+        recipe = Recipe(recipe_name)
+        params = radiance_parameters(
+            model=self.model,
+            detail_dim=detail_dim,
+            recipe_type=recipe_name,
+            detail_level=detail_level,
+        )
+        recipe.input_value_by_name("model", self.model)
+        recipe.input_value_by_name("wea", wea)
+        recipe.input_value_by_name("output-type", "solar")
+        recipe.input_value_by_name("radiance-parameters", params)
+        recipe.input_value_by_name("north", north)
+
+        settings = RecipeSettings(
+            folder=self.output_directory.as_posix(), reload_old=reload_old
+        )
+
+        results_folder = recipe.run(settings)
+
+        if delete_tempfiles:
+            for fp in (Path(results_folder) / f"{folder_name}/initial_results").glob(
+                "**/*.ill"
+            ):
+                fp.unlink()
+
+        return Path(results_folder) / folder_name
+
+    def annual_irradiance(
+        self,
+        grids_filter: tuple[str] = "*",
+        irradiance_type: str = "total",
+    ) -> pd.DataFrame:
+        """Obtain annual irradiance results for the given model simulation folder.
+
+        Args:
+            grids_filter (Tuple[str], optional):
+                A list of strings to filter the grids.
+                Defaults to ("*") which includes all grids.
+            irradiance_type (str, optional):
+                The type of irradiance to return. One of "total", "direct".
+                Defaults to "total".
+
+        Returns:
+            pd.DataFrame:
+                A pandas DataFrame with irradiance results per grid requested.
+        """
+
+        results_folder = (
+            self.output_directory / f"annual_irradiance/results/{irradiance_type}"
+        )
+        if not results_folder.exists():
+            raise ValueError(
+                "The given folder does not contain annual irradiance results. Try running a simulation first."
+            )
+
+        if not isinstance(grids_filter, (list, tuple, str)):
+            raise ValueError(
+                "The grids_filter must be a string, list or tuple of strings."
+            )
+
+        with open(results_folder / "grids_info.json") as f:
+            grids_info = json.load(f)
+
+        renamer = {gi["identifier"]: gi["name"] for gi in grids_info}
+
+        files = [
+            results_folder / f"{i['identifier']}.ill"
+            for i in _filter_grids_by_pattern(grids_info, grids_filter)
+        ]
+
+        return (pd.concat([load_ill(files)], axis=1).sort_index(axis=1)).rename(
+            columns=renamer, level=0
+        )
+
+    def simulate_sky_view(
+        self,
+        reload_old: bool = True,
+        delete_tempfiles: bool = True,
+        detail_level: int = 0,
+        detail_dim: float = 0.15,
+    ) -> Path:
+        """Run a sky view recipe for the current model.
+
+        Args:
+            reload_old (bool, optional):
+                Instead of running a full simulation, reload the existing results ... if they exist.
+                Defaults to True.
+            delete_tempfiles (bool, optional):
+                Delete temporary files created during simulation. This should roughly half the results directory size.
+                Defaults to True.
+            detail_level (float, optional):
+                Set the detail level of the simulation.
+                Defaults to 0 which is the lowest detail level.
+            detail_dim (float, optional):
+                Set the minimum geometry size to be accounted for in the raytracing.
+                Defaults to 0.15.
+
+        Returns:
+            Path:
+                The directory containing simulation results
+        """
+
+        recipe_name = "sky-view"
+        folder_name = "sky_view"
+
+        CONSOLE_LOGGER.info(f"Running {recipe_name} recipe for model {self.model_name}")
+
+        recipe = Recipe(recipe_name)
+        params = radiance_parameters(
+            model=self.model,
+            detail_dim=detail_dim,
+            recipe_type=recipe_name,
+            detail_level=detail_level,
+        )
+        recipe.input_value_by_name("model", self.model)
+        recipe.input_value_by_name("radiance-parameters", params)
+
+        settings = RecipeSettings(
+            folder=self.output_directory.as_posix(), reload_old=reload_old
+        )
+
+        results_folder = recipe.run(settings)
+
+        if delete_tempfiles:
+            for fp in (Path(results_folder) / f"{folder_name}/initial_results").glob(
+                "**/*.res"
+            ):
+                fp.unlink()
+
+        return Path(results_folder) / folder_name
+
+    def sky_view(
+        self,
+        grids_filter: tuple[str] = "*",
+    ) -> pd.DataFrame:
+        """Obtain sky view results for the given model simulation folder.
+
+        Args:
+            grids_filter (Tuple[str], optional):
+                A list of strings to filter the grids.
+                Defaults to ("*") which includes all grids.
+
+        Returns:
+            pd.DataFrame:
+                A pandas DataFrame with sky view results per grid requested.
+        """
+        results_folder = self.output_directory / "sky_view/results/sky_view"
+        if not results_folder.exists():
+            raise ValueError(
+                "The given folder does not contain sky view results. Try running a simulation first."
+            )
+
+        if not isinstance(grids_filter, (list, tuple, str)):
+            raise ValueError(
+                "The grids_filter must be a string, list or tuple of strings."
+            )
+
+        with open(results_folder / "grids_info.json") as f:
+            grids_info = json.load(f)
+
+        renamer = {gi["identifier"]: gi["name"] for gi in grids_info}
+
+        files = [
+            results_folder / f"{i['identifier']}.res"
+            for i in _filter_grids_by_pattern(grids_info, grids_filter)
+        ]
+
+        return (pd.concat([load_res(files)], axis=1).sort_index(axis=1)).rename(
+            columns=renamer, level=0
+        )
+
+    def simulate_direct_sun_hours(
+        self,
+        wea: Wea,
+        analysis_period: AnalysisPeriod = AnalysisPeriod(),
+        north: float = 0,
+        delete_tempfiles: bool = True,
+        reload_old: bool = True,
+    ) -> Path:
+        """Run a direct sun hours recipe for the current model.
+
+        Args:
+            wea (Wea):
+                A Ladybug Wea object.
+            analysis_period (AnalysisPeriod, optional):
+                The analysis period to run the simulation for. Defaults to AnalysisPeriod().
+            north (float, optional):
+                Set the angle to north. Defaults to 0 which assumes the model is correctly oriented.
+            delete_tempfiles (bool, optional):
+                Delete temporary files created during simulation. This should roughly half the results directory size.
+                Defaults to True.
+            reload_old (bool, optional):
+                Instead of running a full simulation, reload the existing results ... if they exist.
+                Defaults to True.
+
+        Returns:
+            Path:
+                The directory containing simulation results
+        """
+
+        ap_str = describe_analysis_period(
+            analysis_period=analysis_period, save_path=True, include_timestep=True
+        )
+        simulation_directory = self.output_directory / f"direct_sun_hours_{ap_str}"
+        simulation_directory.mkdir(exist_ok=True, parents=True)
+
+        recipe_name = "direct-sun-hours"
+        folder_name = "direct_sun_hours"
+
+        CONSOLE_LOGGER.info(
+            f"Running {recipe_name} recipe for model {self.model_name} over "
+            f"{describe_analysis_period(analysis_period, include_timestep=True)}"
+        )
+
+        recipe = Recipe(recipe_name)
+        recipe.input_value_by_name("model", self.model)
+        recipe.input_value_by_name("wea", wea)
+        recipe.input_value_by_name("timestep", analysis_period.timestep)
+        recipe.input_value_by_name("north", north)
+
+        settings = RecipeSettings(
+            folder=simulation_directory.as_posix(), reload_old=reload_old
+        )
+
+        results_folder = recipe.run(settings)
+
+        if delete_tempfiles:
+            for fp in (Path(results_folder) / f"{folder_name}/initial_results").glob(
+                "**/*.res"
+            ):
+                fp.unlink()
+            for fp in (Path(results_folder) / f"{folder_name}/initial_results").glob(
+                "**/*.ill"
+            ):
+                fp.unlink()
+
+        return Path(results_folder) / folder_name
+
+    def direct_sun_hours(
+        self,
+        grids_filter: tuple[str] = "*",
+        sun_hours_type: str = "cumulative",
+    ) -> pd.DataFrame:
+        """Obtain direct sun hours results for the given model simulation folder.
+
+        Args:
+            grids_filter (Tuple[str], optional):
+                A list of strings to filter the grids.
+                Defaults to ("*") which includes all grids.
+            sun_hours_type (str, optional):
+                The type of direct sun hours to return. One of "cumulative".
+                Defaults to "cumulative".
+
+        Returns:
+            pd.DataFrame:
+                A pandas DataFrame with direct sun hours results per grid requested.
+        """
+
+        if sun_hours_type != "cumulative":
+            raise NotImplementedError(
+                "Only cumulative direct sun hours are currently supported."
+            )
+
+        results_folders = list(self.output_directory.glob("direct_sun_hours_*"))
+
+        if not results_folders:
+            raise ValueError(
+                "The given folder does not contain direct sun hours results. Try running a simulation first."
+            )
+
+        dfs = []
+        for results_folder in results_folders:
+            rf = results_folder / f"direct_sun_hours/results/{sun_hours_type}"
+            with open(rf / "grids_info.json") as f:
+                grids_info = json.load(f)
+
+            renamer = {gi["identifier"]: gi["name"] for gi in grids_info}
+
+            files = [
+                rf / f"{i['identifier']}.res"
+                for i in _filter_grids_by_pattern(grids_info, grids_filter)
+            ]
+            df = load_res(files).rename(columns=renamer)
+            dfs.append(df)
+
+        return pd.concat(dfs, axis=1, keys=[rf.name for rf in results_folders])
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/simulation/sensorgrids.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/simulation/sensorgrids.py
new file mode 100644
index 00000000..bff27558
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/simulation/sensorgrids.py
@@ -0,0 +1,297 @@
+"""Methods for handling Honeybee sensorgrids."""
+# pylint: disable=E0401
+from warnings import warn
+
+import matplotlib.collections as mcollections
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import matplotlib.tri as mtri
+import numpy as np
+from honeybee_radiance.sensorgrid import SensorGrid
+from ladybug_geometry.geometry3d import Plane, Point3D, Vector3D
+from scipy.spatial.distance import cdist
+
+from ...ladybug_geometry_extension import mesh3d_get_plane
+from ...plot.utilities import create_triangulation
+
+# pylint: enable=E0401
+
+
+def is_planar(sensorgrid: SensorGrid) -> bool:
+    """Check if a sensorgrid is planar.
+
+    Args:
+        sensorgrid (SensorGrid): A honeybee-radiance SensorGrid.
+
+    Returns:
+        bool: True if the sensorgrid is planar, False otherwise.
+    """
+
+    plane = Plane.from_three_points(
+        *[Point3D.from_array(i.pos) for i in sensorgrid.sensors[0:3]]
+    )
+    for sensor in sensorgrid.sensors:
+        if not np.isclose(
+            a=plane.distance_to_point(point=Point3D.from_array(sensor.pos)), b=0
+        ):
+            return False
+    return True
+
+
+def get_plane(sensorgrid: SensorGrid) -> Plane:
+    """Create a plane from a sensorgrid.
+
+    Args:
+        sensorgrid (SensorGrid):
+            A honeybee-radiance SensorGrid.
+
+    Returns:
+        Plane:
+            A ladybug-geometry Plane.
+    """
+
+    if len(sensorgrid.sensors) < 3:
+        raise ValueError(
+            "sensor grid must contain at least 3 points to create a plane."
+        )
+
+    for sensor in sensorgrid.sensors[2:]:
+        plane = Plane.from_three_points(
+            o=Point3D(*sensorgrid.sensors[0].pos),
+            p2=Point3D(*sensorgrid.sensors[1].pos),
+            p3=Point3D(*sensor.pos),
+        )
+        if sum(plane.n.to_array()) != 0:
+            break
+
+    if sum(plane.n.to_array()) == 0:
+        raise ValueError(
+            "sensor grid contains points which are colinear and cannot create a triangle."
+        )
+
+    if plane.n.z == -1:
+        plane = plane.flip()
+
+    origin = list(plane.o.to_array())
+    normal = list(plane.n.to_array())
+
+    for n, i in enumerate(origin):
+        if abs(i) == i:
+            origin[n] = abs(i)
+    for n, i in enumerate(normal):
+        if abs(i) == i:
+            normal[n] = abs(i)
+
+    if not is_planar(sensorgrid):
+        raise ValueError("sensorgrid must be planar to create a plane.")
+
+    return Plane(o=Point3D(*origin), n=Vector3D(*normal))
+
+
+def groupby_level(
+    sensorgrids: list[SensorGrid],
+) -> dict[float, list[SensorGrid]]:
+    """Group sensorgrids by their level.
+
+    Args:
+        sensorgrids (list[SensorGrid]):
+            A list of honeybee-radiance SensorGrids.
+
+    Returns:
+        dict[float, list[SensorGrid]]:
+            A dictionary of lists of honeybee-radiance SensorGrids.
+    """
+
+    d = {}
+    for grid in sensorgrids:
+        level = grid.sensors[0].pos[-1]
+        if not is_planar(grid):
+            warn(
+                f"{grid} is not planar. This may cause issues when grouping by level. It will be assumed to be at level {level}."
+            )
+        if level not in d:
+            d[level] = [grid]
+        else:
+            d[level].append(grid)
+
+    return d
+
+
+def position_array(sensorgrid: SensorGrid) -> np.ndarray:
+    """Convert a honeybee-radiance SensorGrid to a numpy array of XYZ coordinates.
+
+    Args:
+        sensorgrid (SensorGrid):
+            A honeybee-radiance SensorGrid.
+
+    Returns:
+        np.ndarray:
+            A numpy array of the sensorgrid.
+    """
+
+    return np.array([i.pos for i in sensorgrid.sensors])
+
+
+def vector_array(sensorgrid: SensorGrid) -> np.ndarray:
+    """Convert a honeybee-radiance SensorGrid to a numpy array of XYZ coordinates.
+
+    Args:
+        sensorgrid (SensorGrid):
+            A honeybee-radiance SensorGrid.
+
+    Returns:
+        np.ndarray:
+            A numpy array of the sensorgrid.
+    """
+
+    return np.array([i.dir for i in sensorgrid.sensors])
+
+
+def estimate_spacing(sensorgrid: SensorGrid) -> float:
+    """Estimate the spacing of a sensorgrid.
+
+    Args:
+        sensorgrid (SensorGrid):
+            A honeybee-radiance SensorGrid.
+
+    Returns:
+        float:
+            The estimated spacing (between sensors) of the sensorgrid.
+    """
+    if not isinstance(sensorgrid, SensorGrid):
+        raise ValueError("sensorgrid must be a honeybee-radiance SensorGrid.")
+
+    if not is_planar(sensorgrid):
+        raise ValueError("sensorgrid must be planar.")
+
+    pts = position_array(sensorgrid)
+
+    return np.sort(np.sort(cdist(pts, pts))[:, 1])[-1]
+
+
+def as_triangulation(
+    sensorgrid: SensorGrid,
+    alpha_adjust: float = 0.1,
+) -> mtri.Triangulation:
+    """Create matploltib triangulation from a SensorGrid object.
+
+    Args:
+        sensorgrid (SensorGrid):
+            A honeybee-radiance SensorGrid.
+        alpha_adjust (float, optional):
+            A value to adjust the alpha value of the triangulation. Defaults to 0.1.
+
+    Returns:
+        Triangulation: A matplotlib triangulation.
+    """
+
+    alpha = estimate_spacing(sensorgrid) + alpha_adjust
+    plane = get_plane(sensorgrid)
+
+    if not any([(plane.n.z == 1), (plane.n.z == -1)]):
+        warn(
+            "The sensorgrid given is planar, but not in the XY plane. You may need to rotate this when visualising it!"
+        )
+
+    x, y = np.array(
+        [
+            plane.xyz_to_xy(Point3D(*sensor.pos)).to_array()
+            for sensor in sensorgrid.sensors
+        ]
+    ).T
+
+    return create_triangulation(x, y, alpha=alpha)
+
+
+def as_patchcollection(
+    sensorgrid: SensorGrid, **kwargs
+) -> mcollections.PatchCollection:
+    """Get the matplotlib PatchCollection for a set of sensor grids.
+
+    Args:
+        sensorgrid (SensorGrid):
+            A honeybee-radiance SensorGrid.
+        **kwargs:
+            Additional keyword arguments to pass to the PatchCollection objects created.
+
+    Returns:
+        mcollections.PatchCollection:
+            The matplotlib PatchCollection.
+    """
+
+    if sensorgrid.mesh is None:
+        raise ValueError(
+            f"{sensorgrid} does not have a mesh. This should have been assigned when the sensorgrid was created."
+        )
+
+    # flatten the mesh to 2D in XY plane, raising warnings if the mesh is not 2D planar
+    mesh3d_get_plane(mesh=sensorgrid.mesh)
+
+    patches = []
+    for face in sensorgrid.mesh.face_vertices:
+        patches.append(
+            mpatches.Polygon(np.array([i.to_array()[:2] for i in face]), closed=False)
+        )
+    return mcollections.PatchCollection(patches, **kwargs)
+
+
+def plot_values(
+    sensorgrid: SensorGrid, values: list[float], ax: plt.Axes = None, **kwargs
+) -> plt.Axes:
+    """Plot a sensorgrid with values.
+
+    Args:
+        sensorgrid (SensorGrid):
+            A honeybee-radiance SensorGrid.
+        values (List[float]):
+            A list of values to plot.
+        ax (plt.Axes, optional):
+            A matplotlib Axes object. Defaults to None.
+        **kwargs:
+            Additional keyword arguments to pass to the PatchCollection objects created.
+
+    Returns:
+        mcollections.PatchCollection: The matplotlib PatchCollection.
+    """
+
+    if ax is None:
+        ax = plt.gca()
+
+    pc = as_patchcollection(sensorgrid, **kwargs)
+    pc.set_array(values)
+    ax.add_collection(pc)
+
+    ax.autoscale_view()
+    ax.set_aspect("equal")
+
+    return ax
+
+
+def get_limits(sensorgrids: list[SensorGrid], buffer: float = 2) -> tuple[tuple[float]]:
+    """Get the X/Y limits from a set of sensor grids.
+
+    Args:
+        sensorgrids (list[SensorGrid]):
+            A list of honeybee-radiance SensorGrids.
+        buffer (float, optional):
+            A buffer to add to the limits. Defaults to 2.
+
+    Returns:
+        tuple[tuple[float]]: The X/Y limits.
+    """
+
+    x_low = []
+    x_high = []
+    y_low = []
+    y_high = []
+    for grid in sensorgrids:
+        x, y, _ = position_array(grid).T
+        x_low.append(np.min(x))
+        x_high.append(np.max(x))
+        y_low.append(np.min(y))
+        y_high.append(np.max(y))
+
+    return (
+        (np.min(x_low) - buffer, np.max(x_high) + buffer),
+        (np.min(y_low) - buffer, np.max(y_high) + buffer),
+    )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/sri.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/sri.py
new file mode 100644
index 00000000..a79c2cc5
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/honeybee_extension/sri.py
@@ -0,0 +1,206 @@
+"""Methods for calculating the Solar Reflective Index (SRI) of materials and constructions."""
+
+import numpy as np
+from honeybee_energy.construction.opaque import OpaqueConstruction
+from honeybee_energy.material.opaque import EnergyMaterial
+from ..bhom.analytics import bhom_analytics
+
+
+@bhom_analytics()
+def calculate_sri(
+    solar_reflectance: float,
+    thermal_emittance: float,
+    insolation: float = 1000,
+    air_temperature: float = 36.85,
+    sky_temperature: float = 26.85,
+    wind_speed: float = 4,
+) -> float:
+    """Calculate the SRI of a material from its reflectance and emittance.
+    Note, this method assumes a horizontal material (facing the sky).
+
+    This method is based on the tool created by Ronnen Levinson, Heat Island
+    Group, LBNL. It uses the method from ASTM Standard E1980-11 to calculate
+    the SRI of a material, given its solar reflectance and thermal emittance.
+
+    Wind speed is used inestead of wind convection coeffeicnt, based on the
+    suggested values in ASTM E1980-11.
+
+    Args:
+        solar_reflectance (float): Solar reflectance of the material. Unitless,
+            between 0 (black body) and 1 (white-body).
+        thermal_emittance (float): Thermal emittance of the material. Unitless,
+            between 0 (white-body) and 1 (black-body).
+        insolation (float, optional): Insolation incident on the material.
+            Defaults to 1000W/m2.
+        air_temperature (float, optional): Air temperature. Defaults to 36.85C.
+        sky_temperature (float, optional): Sky temperature. Defaults to 26.85C.
+        wind_speed (float, optional): Speed of wind. Defaults to 4m/s.
+
+    Returns:
+        float: SRI of the material. Unitless.
+    """
+
+    if not 0 <= solar_reflectance <= 1:
+        raise ValueError(
+            "Solar reflectance must be between 0 (black body) and 1 (white-body)."
+        )
+    if not 0 <= thermal_emittance <= 1:
+        raise ValueError(
+            "Thermal emittance must be between 0 (white-body) and 1 (black-body)."
+        )
+    if wind_speed < 0:
+        raise ValueError("Wind speed must be greater than 0.")
+
+    # convert wind speed to wind convection coeffecient
+    speeds = [1, 4, 8]
+    coeffs = [5, 12, 30]
+    wind_convection_coefficient = np.interp(wind_speed, speeds, coeffs)
+
+    # set the sensitivity threshold for the iterative calculation. Lower is more accurate but slower.
+    threshold = 0.5  # W
+    increment = 0.01  # K
+    iterations = 100000
+
+    air_temperature = air_temperature + 273.15  # K
+    sky_temperature = sky_temperature + 273.15  # K
+
+    sigma = 5.67e-8  # W m-2 K-4 Stefan-Boltzmann constant
+    blackbody_solar_reflectance = 0.05  #  0 - 1
+    whitebody_solar_reflectance = 0.8  #  0 - 1
+    blackbody_thermal_emittance = 0.9  #  0 - 1
+    whitebody_thermal_emittance = 0.9  #  0 - 1
+
+    surface_temperature = 200.0  # K
+    n = 0
+    while not np.isclose(
+        (1 - solar_reflectance) * insolation
+        - (
+            thermal_emittance
+            * sigma
+            * (surface_temperature**4 - sky_temperature**4)
+            + wind_convection_coefficient * (surface_temperature - air_temperature)
+        ),
+        0,
+        atol=threshold,
+    ):
+        n += 1
+        if n > iterations:
+            raise ValueError(
+                f"SRI calculation did not converge. Surface temperature of {surface_temperature - 273.15}C."
+            )
+        surface_temperature += increment
+
+    blackbody_surface_temperature = 200.0  # K
+    n = 0
+    while not np.isclose(
+        (1 - blackbody_solar_reflectance) * insolation
+        - (
+            blackbody_thermal_emittance
+            * sigma
+            * (blackbody_surface_temperature**4 - sky_temperature**4)
+            + wind_convection_coefficient
+            * (blackbody_surface_temperature - air_temperature)
+        ),
+        0,
+        atol=threshold,
+    ):
+        n += 1
+        if n > iterations:
+            raise ValueError("SRI calculation did not converge.")
+        blackbody_surface_temperature += increment
+
+    whitebody_surface_temperature = 200.0  # K
+    n = 0
+    while not np.isclose(
+        (1 - whitebody_solar_reflectance) * insolation
+        - (
+            whitebody_thermal_emittance
+            * sigma
+            * (whitebody_surface_temperature**4 - sky_temperature**4)
+            + wind_convection_coefficient
+            * (whitebody_surface_temperature - air_temperature)
+        ),
+        0,
+        atol=threshold,
+    ):
+        n += 1
+        if n > iterations:
+            raise ValueError("SRI calculation did not converge.")
+        whitebody_surface_temperature += increment
+
+    solar_reflective_index = (
+        100
+        * (blackbody_surface_temperature - surface_temperature)
+        / (blackbody_surface_temperature - whitebody_surface_temperature)
+    )
+
+    if solar_reflective_index < 0:
+        return 0
+
+    return solar_reflective_index
+
+
+@bhom_analytics()
+def material_sri(
+    material: EnergyMaterial,
+    insolation: float = 1000,
+    air_temperature: float = 36.85,
+    sky_temperature: float = 26.85,
+    wind_speed: float = 4,
+) -> float:
+    """Calculate the SRI of a Honeybee material.
+    Note, this method assumes a horizontal material (facing the sky).
+
+    Args:
+        material (_EnergyMaterialOpaqueBase): A Honeybee opaque material.
+        insolation (float, optional): Insolation incident on the material.
+            Defaults to 1000W/m2.
+        air_temperature (float, optional): Air temperature. Defaults to 36.85C.
+        sky_temperature (float, optional): Sky temperature. Defaults to 26.85C.
+        wind_speed (float, optional): Speed of wind. Defaults to 4m/s.
+
+    Returns:
+        float: SRI of the material. Unitless.
+    """
+
+    return calculate_sri(
+        solar_reflectance=material.solar_reflectance,
+        thermal_emittance=material.thermal_absorptance,
+        insolation=insolation,
+        air_temperature=air_temperature,
+        sky_temperature=sky_temperature,
+        wind_speed=wind_speed,
+    )
+
+
+@bhom_analytics()
+def construction_sri(
+    construction: OpaqueConstruction,
+    insolation: float = 1000,
+    air_temperature: float = 36.85,
+    sky_temperature: float = 26.85,
+    wind_speed: float = 4,
+) -> float:
+    """Calculate the SRI of a Honeybee construction.
+    Note, this method assumes a horizontal construction (facing the sky).
+
+    Args:
+        construction (Opaqueconstruction): A Honeybee construction material.
+        insolation (float, optional): Insolation incident on the material.
+            Defaults to 1000W/m2.
+        air_temperature (float, optional): Air temperature. Defaults to 36.85C.
+        sky_temperature (float, optional): Sky temperature. Defaults to 26.85C.
+        wind_speed (float, optional): Speed of wind. Defaults to 4m/s.
+
+    Returns:
+        float: SRI of the construction. Unitless.
+    """
+
+    return calculate_sri(
+        solar_reflectance=construction.outside_solar_reflectance,
+        thermal_emittance=construction.outside_emissivity,
+        insolation=insolation,
+        air_temperature=air_temperature,
+        sky_temperature=sky_temperature,
+        wind_speed=wind_speed,
+    )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/analysis_period.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/analysisperiod.py
similarity index 63%
rename from LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/analysis_period.py
rename to LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/analysisperiod.py
index 15fbc284..1c0b349d 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/analysis_period.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/analysisperiod.py
@@ -1,11 +1,15 @@
+"""Methods for manipulating Ladybug analysis periods."""
+
+# pylint: disable=E0401
 import calendar
 from datetime import datetime
-from typing import List, Union
+
+# pylint: enable=E0401
 
 import numpy as np
 import pandas as pd
 from ladybug.analysisperiod import AnalysisPeriod
-
+from ..bhom.analytics import bhom_analytics
 from .dt import lb_datetime_from_datetime
 
 
@@ -15,10 +19,12 @@ def analysis_period_to_datetimes(
     """Convert an AnalysisPeriod object into a Pandas DatetimeIndex.
 
     Args:
-        analysis_period (AnalysisPeriod): An AnalysisPeriod object.
+        analysis_period (AnalysisPeriod):
+            An AnalysisPeriod object.
 
     Returns:
-        pd.DatetimeIndex: A Pandas DatetimeIndex object.
+        pd.DatetimeIndex:
+            A Pandas DatetimeIndex object.
     """
 
     datetimes = pd.to_datetime(analysis_period.datetimes)
@@ -26,16 +32,21 @@ def analysis_period_to_datetimes(
     return datetimes
 
 
+@bhom_analytics()
 def analysis_period_to_boolean(
-    analysis_periods: Union[List[AnalysisPeriod], AnalysisPeriod],
-) -> List[bool]:
-    """Convert an AnalysisPeriod object into a list of booleans where values within the Period are also within a default whole analysis period of the same interval.
+    analysis_periods: list[AnalysisPeriod] | AnalysisPeriod,
+) -> list[bool]:
+    """Convert an AnalysisPeriod object into a list of booleans where values
+    within the Period are also within a default whole analysis period of the
+    same interval.
 
     Args:
-        analysis_periods (List[AnalysisPeriod]): A list of AnalysisPeriod objects.
+        analysis_periods (list[AnalysisPeriod]):
+            A list of AnalysisPeriod objects.
 
     Returns:
-        List[bool]: A list of booleans
+        list[bool]:
+            A list of booleans
     """
 
     if isinstance(analysis_periods, AnalysisPeriod):
@@ -53,7 +64,9 @@ def analysis_period_to_boolean(
         AnalysisPeriod(timestep=analysis_periods[0].timestep)
     )
 
-    # for each analysis period in analysis_periods, create a list of booleans where values within the Period are also within a default whole analysis period of the same interval
+    # for each analysis period in analysis_periods, create a list of booleans
+    # where values within the Period are also within a default whole analysis
+    # period of the same interval
     bools = []
     for ap in analysis_periods:
         bools.append(generic_datetimes.isin(analysis_period_to_datetimes(ap)))
@@ -61,14 +74,18 @@ def analysis_period_to_boolean(
     return np.any(bools, axis=0)
 
 
-def analysis_period_from_datetimes(datetimes: List[datetime]) -> AnalysisPeriod:
-    """Convert a list of datetimes (in order from earliest to latest) into an AnalysisPeriod object.
+@bhom_analytics()
+def analysis_period_from_datetimes(datetimes: list[datetime]) -> AnalysisPeriod:
+    """Convert a list of datetimes (in order from earliest to latest) into an
+    AnalysisPeriod object.
 
     Args:
-        datetimes (List[datetime]): A list of datetimes.
+        datetimes (list[datetime]):
+            qA list of datetimes.
 
     Returns:
-        AnalysisPeriod: An AnalysisPeriod object.
+        AnalysisPeriod:
+            An AnalysisPeriod object.
     """
 
     inferred_timestep = (60 * 60) / (datetimes[1] - datetimes[0]).seconds
@@ -88,20 +105,25 @@ def analysis_period_from_datetimes(datetimes: List[datetime]) -> AnalysisPeriod:
     return analysis_period
 
 
+@bhom_analytics()
 def describe_analysis_period(
-    analysis_period: List[AnalysisPeriod],
+    analysis_period: list[AnalysisPeriod],
     save_path: bool = False,
     include_timestep: bool = False,
 ) -> str:
     """Create a description of the given analysis period.
 
     Args:
-        analysis_period (AnalysisPeriod): A Ladybug analysis period.
-        save_path (bool, optional): If True, create a path-safe string from the analysis period.
-        include_timestep (bool, optional): If True, include the timestep in the description.
+        analysis_period (AnalysisPeriod):
+            A Ladybug analysis period.
+        save_path (bool, optional):
+            If True, create a path-safe string from the analysis period.
+        include_timestep (bool, optional):
+            If True, include the timestep in the description.
 
     Returns:
-        str: A description of the analysis period.
+        str:
+            A description of the analysis period.
     """
 
     if isinstance(analysis_period, AnalysisPeriod):
@@ -134,13 +156,24 @@ def describe_analysis_period(
             raise ValueError("Only one analysis period can be used for a save path.")
         analysis_period = analysis_period[0]
         if include_timestep:
-            return f"{analysis_period.st_month:02}{analysis_period.st_day:02}_{analysis_period.end_month:02}{analysis_period.end_day:02}_{analysis_period.st_hour:02}_{analysis_period.end_hour:02}_{analysis_period.timestep:02}"
-        return f"{analysis_period.st_month:02}{analysis_period.st_day:02}_{analysis_period.end_month:02}{analysis_period.end_day:02}_{analysis_period.st_hour:02}_{analysis_period.end_hour:02}"
+            return (
+                f"{analysis_period.st_month:02}{analysis_period.st_day:02}_"
+                f"{analysis_period.end_month:02}{analysis_period.end_day:02}_"
+                f"{analysis_period.st_hour:02}_{analysis_period.end_hour:02}_"
+                f"{analysis_period.timestep:02}"
+            )
+        return (
+            f"{analysis_period.st_month:02}{analysis_period.st_day:02}_"
+            f"{analysis_period.end_month:02}{analysis_period.end_day:02}_"
+            f"{analysis_period.st_hour:02}_{analysis_period.end_hour:02}"
+        )
 
     base_str = []
     for ap in analysis_period:
         base_str.append(
-            f"{calendar.month_abbr[ap.st_month]} {ap.st_day:02} to {calendar.month_abbr[ap.end_month]} {ap.end_day:02} between {ap.st_hour:02}:00 and {ap.end_hour:02}:59"
+            f"{calendar.month_abbr[ap.st_month]} {ap.st_day:02} to "
+            f"{calendar.month_abbr[ap.end_month]} {ap.end_day:02} between "
+            f"{ap.st_hour:02}:00 and {ap.end_hour:02}:59"
         )
     base_str = ", and ".join(base_str)
 
@@ -150,13 +183,19 @@ def describe_analysis_period(
     return base_str
 
 
+def analysis_period_from_description(description_str: str) -> AnalysisPeriod:
+    # TODO: implement this, based on the output from the method above to recreate the analysis period
+    raise NotImplementedError()
+
+
+@bhom_analytics()
 def do_analysis_periods_represent_entire_year(
-    analysis_periods: List[AnalysisPeriod],
+    analysis_periods: list[AnalysisPeriod],
 ) -> bool:
     """Check a list of analysis periods to see if they represent an entire year.
 
     Args:
-        analysis_periods (List[AnalysisPeriod]):
+        analysis_periods (list[AnalysisPeriod]):
             A list of analysis periods.
 
     Returns:
@@ -167,7 +206,8 @@ def do_analysis_periods_represent_entire_year(
     """
     if any(ap.end_hour < ap.st_hour for ap in analysis_periods):
         raise ValueError(
-            "To combine time periods crossing midnight, AnalysisPeriod should be split into two parts - one for either side of midnight."
+            "To combine time periods crossing midnight, AnalysisPeriod should "
+            "be split into two parts - one for either side of midnight."
         )
 
     # Validation
@@ -178,7 +218,8 @@ def do_analysis_periods_represent_entire_year(
         ap.is_leap_year != analysis_periods[0].is_leap_year for ap in analysis_periods
     ):
         raise ValueError(
-            "All input analysis periods must be either leap year, or not leap year. Mixed leapedness is not allowed."
+            "All input analysis periods must be either leap year, or not leap "
+            "year. Mixed leapedness is not allowed."
         )
 
     target_datetimes = analysis_period_to_datetimes(AnalysisPeriod())
@@ -194,17 +235,23 @@ def do_analysis_periods_represent_entire_year(
     if actual_timesteps > target_timesteps:
         duplicates = actual_datetimes[actual_datetimes.duplicated()]
         raise ValueError(
-            f"The number of timesteps contained within the input analysis periods is greater than {target_timesteps}. Duplicate timesteps are {duplicates}"
+            "The number of timesteps contained within the input analysis "
+            f"periods is greater than {target_timesteps}. Duplicate timesteps "
+            f"are {duplicates}"
         )
     if actual_timesteps < target_timesteps:
+        # pylint: disable=E1125
         missing = (
             pd.DatetimeIndex(list(set(target_datetimes) - set(actual_datetimes)))
             .to_series()
             .sort_index()
             .index
         )
+        # pylint: enable=E1125
         raise ValueError(
-            f"The number of timesteps contained within the input analysis periods is less than {target_timesteps}. Missing timesteps are {missing}"
+            "The number of timesteps contained within the input analysis "
+            f"periods is less than {target_timesteps}. Missing timesteps "
+            f"are {missing}"
         )
 
     return True
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/datacollection.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/datacollection.py
index c67c8c5d..55e67a91 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/datacollection.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/datacollection.py
@@ -1,8 +1,12 @@
-import json
+"""Methods for manipulating Ladybug data collections."""
+
+# pylint: disable=E0401
 import warnings
 from datetime import datetime, timedelta
 from pathlib import Path
-from typing import Any, Dict, List, Tuple, Union
+from typing import Any
+
+# pylint: enable=E0401
 
 import numpy as np
 import pandas as pd
@@ -13,27 +17,23 @@
     MonthlyCollection,
 )
 from ladybug.datatype.angle import Angle
-from ladybug.datautil import (
-    collections_from_csv,
-    collections_from_json,
-    collections_to_csv,
-    collections_to_json,
-)
 from ladybug.dt import DateTime
+from ..bhom.analytics import bhom_analytics
+from ..helpers import circular_weighted_mean
+from .analysisperiod import analysis_period_to_datetimes
+from .analysisperiod import describe_analysis_period
+from .header import header_from_string, header_to_string
 
-from ..helpers import circular_weighted_mean, wind_direction_average
-from .analysis_period import analysis_period_to_datetimes
-from .analysis_period import describe_analysis_period as describe_analysis_period
-from .header import header_from_string as header_from_string
-from .header import header_to_string as header_to_string
 
-
-def collection_to_series(collection: BaseCollection) -> pd.Series:
+def collection_to_series(collection: BaseCollection, name: str = None) -> pd.Series:
     """Convert a Ladybug hourlyContinuousCollection object into a Pandas Series object.
 
     Args:
         collection (BaseCollection):
             Ladybug data collection object.
+        name (str, optional):
+            The name of the resulting Pandas Series object. Defaults to None,
+            which uses the collection datatype.
 
     Returns:
         pd.Series:
@@ -47,102 +47,62 @@ def collection_to_series(collection: BaseCollection) -> pd.Series:
     return pd.Series(
         data=collection.values,
         index=index,
-        name=header_to_string(collection.header),
+        name=header_to_string(collection.header) if not name else name,
     )
 
 
-def collection_to_json(
-    collections: List[BaseCollection], json_path: Union[Path, str], indent: int = None
-) -> Path:
-    """Save Ladybug BaseCollection-like objects into a JSON file.
+def collection_from_series(series: pd.Series) -> BaseCollection:
+    """Convert a Pandas Series object into a Ladybug BaseCollection-like object.
 
     Args:
-        collections (BaseCollection):
-            A Ladybug BaseCollection-like object.
-        json_path (Union[Path, str]):
-            The path to the JSON file.
-        indent (str, optional):
-            The indentation to use in the resulting JSON file. Defaults to None.
+        series (pd.Series): A Pandas Series object.
 
     Returns:
-        Path:
-            The path to the JSON file.
-
+        BaseCollection: A Ladybug BaseCollection-like object.
     """
 
-    json_path = Path(json_path)
-
-    if not all(isinstance(n, BaseCollection) for n in collections):
-        raise ValueError(
-            'All elements of the input "collections" must inherit from BaseCollection.'
-        )
-
-    if json_path.suffix != ".json":
-        raise ValueError("The target file must be a *.json file.")
-
-    return Path(
-        collections_to_json(
-            collections,
-            folder=json_path.parent.as_posix(),
-            file_name=json_path.name,
-            indent=indent,
-        )
+    header = header_from_string(
+        series.name, is_leap_year=series.index.is_leap_year.any()
     )
+    header.metadata["source"] = "From custom pd.Series"
 
+    freq = pd.infer_freq(series.index)
+    if freq in ["H"]:
+        if series.index.is_leap_year.any():
+            if len(series.index) != 8784:
+                raise ValueError(
+                    "The number of values in the series must be 8784 for leap years."
+                )
+        else:
+            if len(series.index) != 8760:
+                raise ValueError("The series must have 8760 rows for non-leap years.")
 
-def collection_to_csv(
-    collections: List[BaseCollection], csv_path: Union[Path, str]
-) -> Path:
-    """Save Ladybug BaseCollection-like objects into a CSV file.
-
-    Args:
-        collections (BaseCollection):
-            A Ladybug BaseCollection-like object.
-        csv_path (Union[Path, str]):
-            The path to the CSV file.
-
-    Returns:
-        Path:
-            The path to the CSV file.
-
-    """
-
-    csv_path = Path(csv_path)
-
-    if not all(isinstance(n, BaseCollection) for n in collections):
-        raise ValueError(
-            'All elements of the input "collections" must inherit from BaseCollection.'
-        )
-
-    return Path(
-        collections_to_csv(
-            collections,
-            folder=csv_path.parent.as_posix(),
-            file_name=csv_path.name,
+        return HourlyContinuousCollection(
+            header=header,
+            values=series.values,
         )
-    )
-
 
-def collection_to_array(collection: BaseCollection) -> np.ndarray:
-    """Convert a Ladybug BaseCollection-like object into a numpy array.
-
-    Args:
-        collection: A Ladybug BaseCollection-like object.
+    if freq in ["M", "MS"]:
+        if len(series.index) != 12:
+            raise ValueError("The series must have 12 rows for months.")
 
-    Returns:
-        np.ndarray: A numpy array.
-    """
+        return MonthlyCollection(
+            header=header,
+            values=series.values.tolist(),
+            datetimes=range(1, 13),
+        )
 
-    return np.array(collection.values)
+    raise ValueError("The series must be hourly or monthly.")
 
 
+@bhom_analytics()
 def percentile(
-    collections: List[BaseCollection], nth_percentile: float
+    collections: list[BaseCollection], nth_percentile: float
 ) -> BaseCollection:
     """Create an nth percentile of the given data collections.
 
     Args:
-        collections (List[BaseCollection]):
+        collections (list[BaseCollection]):
             A list of collections.
         nth_percentile (float):
             A percentile, between 0 and 1.
@@ -170,11 +130,12 @@ def percentile(
     )
 
 
-def minimum(collections: List[BaseCollection]) -> BaseCollection:
+@bhom_analytics()
+def minimum(collections: list[BaseCollection]) -> BaseCollection:
     """Create a Minimum of the given data collections.
 
     Args:
-        collections (List[BaseCollection]):
+        collections (list[BaseCollection]):
             A list of collections.
 
     Returns:
@@ -195,11 +156,12 @@ def minimum(collections: List[BaseCollection]) -> BaseCollection:
     return collection_from_series(df.min(axis=1).rename(series_name))
 
 
-def maximum(collections: List[BaseCollection]) -> BaseCollection:
+@bhom_analytics()
+def maximum(collections: list[BaseCollection]) -> BaseCollection:
     """Create a Maximum of the given data collections.
 
     Args:
-        collections (List[BaseCollection]):
+        collections (list[BaseCollection]):
             A list of collections.
 
     Returns:
@@ -220,128 +182,21 @@ def maximum(collections: List[BaseCollection]) -> BaseCollection:
     return collection_from_series(df.max(axis=1).rename(series_name))
 
 
-def collection_from_series(series: pd.Series) -> BaseCollection:
-    """Convert a Pandas Series object into a Ladybug BaseCollection-like object.
-
-    Args:
-        series (pd.Series): A Pandas Series object.
-
-    Returns:
-        BaseCollection: A Ladybug BaseCollection-like object.
-    """
-
-    header = header_from_string(
-        series.name, is_leap_year=series.index.is_leap_year.any()
-    )
-    header.metadata["source"] = "From custom pd.Series"
-
-    freq = pd.infer_freq(series.index)
-    if freq in ["H"]:
-        if series.index.is_leap_year.any():
-            if len(series.index) != 8784:
-                raise ValueError(
-                    "The number of values in the series must be 8784 for leap years."
-                )
-        else:
-            if len(series.index) != 8760:
-                raise ValueError("The series must have 8760 rows for non-leap years.")
-
-        return HourlyContinuousCollection(
-            header=header,
-            values=series.values,
-        )
-
-    if freq in ["M", "MS"]:
-        if len(series.index) != 12:
-            raise ValueError("The series must have 12 rows for months.")
-
-        return MonthlyCollection(
-            header=header,
-            values=series.values.tolist(),
-            datetimes=range(1, 13),
-        )
-
-    raise ValueError("The series must be hourly or monthly.")
-
-
-def collection_from_json(json_path: Union[Path, str]) -> List[BaseCollection]:
-    """Load a JSON containing serialised Ladybug BaseCollection-like objects.
-
-    Args:
-        json_path (Union[Path, str]): The path to the JSON file.
-
-    Returns:
-        List[BaseCollection]: A list of Ladybug BaseCollection-like object.
-
-    """
-
-    json_path = Path(json_path)
-
-    if json_path.suffix != ".json":
-        raise ValueError("The target file must be a *.json file.")
-
-    return collections_from_json(json_path.as_posix())
-
-
-def collection_from_dict(dictionary: Dict[str, Any]) -> BaseCollection:
-    """Convert a JSON compliant dictionary object into a ladybug EPW.
-
-    Args:
-        Dict[str, Any]:
-            A sanitised dictionary.
-
-    Returns:
-        BaseCollection:
-            A ladybug collection object.
-    """
-
-    json_str = json.dumps(dictionary)
-
-    # custom handling of non-standard JSON NaN/Inf values
-    json_str = json_str.replace('"min": "-inf"', '"min": -Infinity')
-    json_str = json_str.replace('"max": "inf"', '"max": Infinity')
-
-    # pylint: disable=broad-exception-caught
-    try:
-        return HourlyContinuousCollection.from_dict(json.loads(json_str))
-    except Exception:
-        return MonthlyCollection.from_dict(json.loads(json_str))
-    # pylint: enable=broad-exception-caught
-
-
-def collection_from_csv(csv_path: Union[Path, str]) -> List[BaseCollection]:
-    """Load a CSV containing serialised Ladybug BaseCollection-like objects.
-
-    Args:
-        csv_path (Union[Path, str]): The path to the CSV file.
-
-    Returns:
-        List[BaseCollection]: A list of Ladybug BaseCollection-like object.
-
-    """
-
-    csv_path = Path(csv_path)
-
-    if csv_path.suffix != ".csv":
-        raise ValueError("The target file must be a *.csv file.")
-
-    return collections_from_csv(csv_path.as_posix())
-
-
+@bhom_analytics()
 def summarise_collection(
     collection: BaseCollection,
     _n_common: int = 3,
-) -> List[str]:
+) -> list[str]:
     """Describe a datacollection.
 
     Args:
         ...
 
     Returns:
-        List[str]: A list of strings describing this data collection.
+        list[str]: A list of strings describing this data collection.
 
     """
-
+    # pylint: disable=C0301
     descriptions = []
 
     series = collection_to_series(collection)
@@ -392,7 +247,7 @@ def summarise_collection(
         )
 
     if isinstance(collection.header.data_type, Angle):
-        _mean = wind_direction_average(series.values)
+        _mean = circular_weighted_mean(series.values)
         descriptions.append(f"Mean {name} is {_mean:,.01f}{unit}.")
     else:
         # mean value
@@ -420,7 +275,9 @@ def summarise_collection(
         _n_max = len(series[series == _max])
         _max_mean_time = series.groupby(series.index.time).mean().idxmax()
         descriptions.append(
-            f"Maximum {name} is {_max:,.01f}{unit}, occurring {_n_max} time{f's and typically at {_max_mean_time:%H:%M}' if _n_max > 1 else f' on {series.idxmax():%b %d at %H:%M}'}."
+            f"Maximum {name} is {_max:,.01f}{unit}, "
+            f"occurring {_n_max} time"
+            f"{f's and typically at {_max_mean_time:%H:%M}' if _n_max > 1 else f' on {series.idxmax():%b %d at %H:%M}'}."
         )
 
     # common values
@@ -449,25 +306,30 @@ def summarise_collection(
         # cumulative values
         if rate_of_change:
             descriptions.append(
-                f"Cumulative total {name} for {period} is {series.sum():,.0f}{collection.to_time_rate_of_change().header.unit}."
+                f"Cumulative total {name} for {period} is "
+                f"{series.sum():,.0f}{collection.to_time_rate_of_change().header.unit}."
             )
 
         # agg months
         _month_mean = series.resample("MS").mean()
         descriptions.append(
-            f"The month with the lowest mean {name} is {_month_mean.idxmin():%B}, with a value of {_month_mean.min():,.1f}{unit}."
+            f"The month with the lowest mean {name} is "
+            f"{_month_mean.idxmin():%B}, with a value of {_month_mean.min():,.1f}{unit}."
         )
         descriptions.append(
-            f"The month with the highest mean {name} is {_month_mean.idxmax():%B}, with a value of {_month_mean.max():,.1f}{unit}."
+            f"The month with the highest mean {name} is "
+            f"{_month_mean.idxmax():%B}, with a value of {_month_mean.max():,.1f}{unit}."
         )
 
         # agg times
         _time_mean = series.groupby(series.index.time).mean()
         descriptions.append(
-            f"The time when the highest mean {name} typically occurs is {_time_mean.idxmax():%H:%M}, with a value of {_time_mean.max():,.1f}{unit}."
+            f"The time when the highest mean {name} typically occurs "
+            f"is {_time_mean.idxmax():%H:%M}, with a value of {_time_mean.max():,.1f}{unit}."
         )
         descriptions.append(
-            f"The time when the lowest mean {name} typically occurs is {_time_mean.idxmin():%H:%M}, with a mean value of {_time_mean.min():,.1f}{unit}."
+            f"The time when the lowest mean {name} typically occurs "
+            f"is {_time_mean.idxmin():%H:%M}, with a mean value of {_time_mean.min():,.1f}{unit}."
         )
 
         # diurnal
@@ -476,25 +338,33 @@ def summarise_collection(
         _month_grp_max = _month_grp.max()
         _month_range_month_avg = _month_grp_max - _month_grp_min
         descriptions.append(
-            f"The month when the largest range of {name} typically occurs is {_month_range_month_avg.idxmax():%B}, with values between {_month_grp_min.loc[_month_range_month_avg.idxmax()]:,.1f}{unit} and {_month_grp_max.loc[_month_range_month_avg.idxmax()]:,.1f}{unit}."
+            f"The month when the largest range of {name} typically occurs "
+            f"is {_month_range_month_avg.idxmax():%B}, with values between "
+            f"{_month_grp_min.loc[_month_range_month_avg.idxmax()]:,.1f}{unit} "
+            f"and {_month_grp_max.loc[_month_range_month_avg.idxmax()]:,.1f}{unit}."
         )
         descriptions.append(
-            f"The month when the smallest range of {name} typically occurs is {_month_range_month_avg.idxmin():%B}, with values between {_month_grp_min.loc[_month_range_month_avg.idxmin()]:,.1f}{unit} and {_month_grp_max.loc[_month_range_month_avg.idxmin()]:,.1f}{unit}."
+            f"The month when the smallest range of {name} typically occurs "
+            f"is {_month_range_month_avg.idxmin():%B}, with values between "
+            f"{_month_grp_min.loc[_month_range_month_avg.idxmin()]:,.1f}{unit} "
+            f"and {_month_grp_max.loc[_month_range_month_avg.idxmin()]:,.1f}{unit}."
         )
-
+    # pylint: enable=C0301
     return descriptions
 
 
+@bhom_analytics()
 def average(
-    collections: List[BaseCollection], weights: List[float] = None
+    collections: list[BaseCollection], weights: list[float] = None
 ) -> BaseCollection:
     """Create an Average of the given data collections.
 
     Args:
-        collections (List[BaseCollection]):
+        collections (list[BaseCollection]):
             A list of collections.
-        weights (List[float], optional):
-            A list of weights for each collection. Defaults to None which evenly weights each collection.
+        weights (list[float], optional):
+            A list of weights for each collection.
+            Defaults to None which evenly weights each collection.
 
     Returns:
         BaseCollection:
@@ -507,7 +377,8 @@ def average(
     for col in collections[1:]:
         if col.header.data_type != collections[0].header.data_type:
             raise ValueError(
-                f"You cannot get the average across non-alike datatypes ({col.header.unit} != {collections[0].header.unit})."
+                "You cannot get the average across non-alike datatypes "
+                f"({col.header.unit} != {collections[0].header.unit})."
             )
 
     if weights is None:
@@ -535,6 +406,7 @@ def average(
     return collections[0].get_aligned_collection(vals)
 
 
+@bhom_analytics()
 def to_hourly(
     collection: MonthlyCollection, method: str = None
 ) -> HourlyContinuousCollection:
@@ -579,7 +451,8 @@ def to_hourly(
     )
 
 
-def peak_time(collection: BaseCollection) -> Tuple[Any, Tuple[DateTime]]:
+@bhom_analytics()
+def peak_time(collection: BaseCollection) -> tuple[Any, tuple[DateTime]]:
     """Find the peak value within a collection, and the time, or times at which it occurs.
 
     Args:
@@ -587,7 +460,7 @@ def peak_time(collection: BaseCollection) -> Tuple[Any, Tuple[DateTime]]:
             A Ladybug DataCollection.
 
     Returns:
-        peak_value, times (Tuple[Any, Tuple[DateTime]]):
+        peak_value, times (tuple[Any, tuple[DateTime]]):
             The peak value and times it occurs.
     """
 
@@ -600,6 +473,7 @@ def peak_time(collection: BaseCollection) -> Tuple[Any, Tuple[DateTime]]:
     return peak_value, times
 
 
+@bhom_analytics()
 def create_typical_day(
     collection: HourlyContinuousCollection,
     centroid: DateTime,
@@ -609,13 +483,24 @@ def create_typical_day(
     """Create a single day representative of conditions centered about the centroid DateTime.
 
     Args:
-        collection (HourlyContinuousCollection): The collection to sample from.
-        centroid (DateTime): The center date around which samples will be taken. The centroid will be included in the sample and more granular detail than "Date" will be ignored.
-        sample_size (int): The number of days about the centroid to sample from. If even, then this number will be increased by one to ensure that half-days are not sampled. A 10 day sample would include 5 days before and 5 days after the centroid.
-        agg (str, optional): The aggregation method to use. Defaults to "mean".
+        collection (HourlyContinuousCollection):
+            The collection to sample from.
+        centroid (DateTime):
+            The center date around which samples will be taken.
+            The centroid will be included in the sample and more granular
+            detail than "Date" will be ignored.
+        sample_size (int):
+            The number of days about the centroid to sample from.
+            If even, then this number will be increased by one to ensure
+            that half-days are not sampled. A 10 day sample would include
+            5 days before and 5 days after the centroid.
+        agg (str, optional):
+            The aggregation method to use. Defaults to "mean".
 
     Returns:
-        HourlyContinuousCollection: The filtered sample. This will have the same header as the input collection but will be only 1-day long.
+        HourlyContinuousCollection:
+            The filtered sample. This will have the same header as the input
+            collection but will be only 1-day long.
     """
 
     # check that the collection is hourly
@@ -673,5 +558,7 @@ def create_typical_day(
             end_day=centroid.day,
         )
     )
+    # pylint: disable=protected-access
     _base_col._values = sample.groupby(sample.index.hour).agg(agg).values
+    # pylint: enable=protected-access
     return _base_col
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/dt.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/dt.py
index 0fefebc8..6687f1c6 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/dt.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/dt.py
@@ -1,8 +1,12 @@
-from datetime import datetime
+"""Methods for manipulating Ladybug datetime objects."""
+
+from datetime import datetime  # pylint: disable=E0401
 
 from ladybug.dt import DateTime
+from ..bhom.analytics import bhom_analytics
 
 
+@bhom_analytics()
 def lb_datetime_to_datetime(lb_datetime: DateTime) -> datetime:
     """Convert a Ladybug DateTime object into a Python datetime object.
 
@@ -24,6 +28,7 @@ def lb_datetime_to_datetime(lb_datetime: DateTime) -> datetime:
     )
 
 
+@bhom_analytics()
 def lb_datetime_from_datetime(date_time: datetime) -> DateTime:
     """Convert a Python datetime object into a Ladybug DateTime object.
 
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/epw.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/epw.py
index f8fce7e8..7d18ff69 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/epw.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/epw.py
@@ -1,13 +1,15 @@
+"""Methods for manipulating Ladybug EPW objects."""
+# pylint: disable=C0302
+# pylint: disable=E0401
 import calendar
 import copy
 import datetime
-import itertools
 import json
 import warnings
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import field
 from pathlib import Path
-from typing import Any, Dict, List, Tuple
+from typing import Any
+
+# pylint: enable=E0401
 
 import numpy as np
 import pandas as pd
@@ -31,10 +33,9 @@
 )
 from ladybug.skymodel import clearness_index as lb_ci
 from ladybug.sunpath import Sun, Sunpath
-from ladybug.wea import Wea
 from ladybug_comfort.degreetime import cooling_degree_time, heating_degree_time
 
-from ..external_comfort.ground_temperature import hourly_ground_temperature
+from ..bhom.analytics import bhom_analytics
 from ..helpers import (
     air_pressure_at_height,
     radiation_at_height,
@@ -42,14 +43,15 @@
     timedelta_tostring,
     wind_speed_at_height,
 )
-from .analysis_period import analysis_period_to_datetimes
+from .analysisperiod import analysis_period_to_datetimes
 from .datacollection import average as average_collection
 from .datacollection import collection_to_series
-from .header import header_to_string as header_to_string
-from .location import average_location
-from .location import location_to_string as location_to_string
+from .groundtemperature import hourly_ground_temperature
+from .header import header_to_string
+from .location import average_location, location_to_string
 
 
+@bhom_analytics()
 def epw_to_dataframe(
     epw: EPW, include_additional: bool = False, **kwargs
 ) -> pd.DataFrame:
@@ -59,13 +61,11 @@ def epw_to_dataframe(
         epw (EPW):
             An EPW object.
         include_additional (bool, optional):
-            Set to False to not include additional calculated properties. Default is False.
+            Set to False to not include additional calculated properties.
+            Default is False.
         **kwargs:
-            Additional keyword arguments to be passed to the to_dataframe method.
-            ground_temperature_depth (float):
-                The depth in m at which to calculate ground temperatures. Default is 0.5.
-            soil_diffusivity (float):
-                The soil diffusivity in m2/s. Default is 3.1e-7.
+            Keyword arguments are passed to `hourly_ground_temperature()` to
+            calculate the temperature of the ground.
 
     Returns:
         pd.DataFrame:
@@ -109,11 +109,12 @@ def epw_to_dataframe(
     for prop in properties:
         try:
             s = collection_to_series(getattr(epw, prop))
-            s.rename((Path(epw.file_path).stem, "EPW", s.name), inplace=True)
+            # s.rename((Path(epw.file_path).stem, "EPW", s.name), inplace=True)
             all_series.append(s)
         except (ValueError, TypeError):
             warnings.warn(
-                f"{prop} is not available in this EPW file. This is most likely because this file does not follow normal EPW content conventions."
+                f"{prop} is not available in this EPW file. This is most likely "
+                "because this file does not follow normal EPW content conventions."
             )
 
     if not include_additional:
@@ -162,16 +163,17 @@ def epw_to_dataframe(
         ground_temp,
     ]:
         s = collection_to_series(collection)
-        s.rename((Path(epw.file_path).stem, "EPW", s.name), inplace=True)
+        # s.rename((Path(epw.file_path).stem, "EPW", s.name), inplace=True)
         all_series.append(s)
 
     return pd.concat(all_series, axis=1).sort_index(axis=1)
 
 
+@bhom_analytics()
 def epw_from_dataframe(
     dataframe: pd.DataFrame,
     location: Location = None,
-    monthly_ground_temperature: Dict[float, MonthlyCollection] = None,
+    monthly_ground_temperature: dict[float, MonthlyCollection] = None,
     comments_1: str = None,
     comments_2: str = None,
 ) -> EPW:
@@ -292,6 +294,7 @@ def epw_from_dataframe(
     return epw_obj
 
 
+@bhom_analytics()
 def wet_bulb_temperature(epw: EPW) -> HourlyContinuousCollection:
     """Calculate an annual hourly wet bulb temperature for a given EPW.
 
@@ -315,9 +318,10 @@ def wet_bulb_temperature(epw: EPW) -> HourlyContinuousCollection:
     )
 
 
+@bhom_analytics()
 def unique_wind_speed_direction(
-    epw: EPW, schedule: List[int] = None
-) -> List[Tuple[float, float]]:
+    epw: EPW, schedule: list[int] = None
+) -> list[tuple[float, float]]:
     """Return a set of unique wind speeds and directions for an EPW file.
 
     Args:
@@ -325,7 +329,7 @@ def unique_wind_speed_direction(
         schedule (epw): a mask of hours to include in the unique
 
     Returns:
-        List[List[float, float]]: A list of unique (wind_speed, wind_direction).
+        list[tuple[float, float]]: A list of unique (wind_speed, wind_direction).
     """
 
     df = pd.concat(
@@ -342,32 +346,8 @@ def unique_wind_speed_direction(
     return df.drop_duplicates().values
 
 
-def epw_to_dict(epw: EPW) -> Dict[str, Any]:
-    """Convert a ladybug EPW object into a JSON-able compliant dictionary.
-
-    Args:
-        epw (EPW):
-            An EPW object.
-
-    Returns:
-        Dict[str, Any]:
-            A sanitised dictionary.
-    """
-    d = epw.to_dict()
-    json_str = json.dumps(d)
-
-    # custom handling of non-standard JSON NaN/Inf values
-    json_str = json_str.replace('"min": -Infinity', '"min": "-inf"')
-    json_str = json_str.replace('"max": Infinity', '"max": "inf"')
-
-    # custom handling of float-indexed values
-    for k, _ in d["monthly_ground_temps"].items():
-        json_str = json_str.replace(f'"{k}": {{', f'"_{k}": {{'.replace(".", "_"))
-
-    return json.loads(json_str)
-
-
-def sun_position_list(epw: EPW) -> List[Sun]:
+@bhom_analytics()
+def sun_position_list(epw: EPW) -> list[Sun]:
     """
     Calculate sun positions for a given epw file.
 
@@ -375,7 +355,7 @@ def sun_position_list(epw: EPW) -> List[Sun]:
         epw (EPW):
             An epw object.
     Returns:
-        List[Sun]:
+        list[Sun]:
             A list of Sun objects.
     """
 
@@ -384,6 +364,7 @@ def sun_position_list(epw: EPW) -> List[Sun]:
     return [sunpath.calculate_sun_from_hoy(i) for i in range(len(epw.years))]
 
 
+@bhom_analytics()
 def sun_position_collection(epw: EPW) -> HourlyContinuousCollection:
     """Calculate a set of Sun positions for each hour of the year.
 
@@ -409,6 +390,7 @@ def sun_position_collection(epw: EPW) -> HourlyContinuousCollection:
     )
 
 
+@bhom_analytics()
 def solar_time_hour(
     epw: EPW, eot: HourlyContinuousCollection = None
 ) -> HourlyContinuousCollection:
@@ -447,6 +429,7 @@ def solar_time_hour(
     )
 
 
+@bhom_analytics()
 def solar_time_datetime(
     epw: EPW, solar_time_hourly: HourlyContinuousCollection = None
 ) -> HourlyContinuousCollection:
@@ -478,7 +461,8 @@ def solar_time_datetime(
     )
     _datetimes = list(_datetimes)
 
-    # Sometimes the first datetime for solar time occurs before the target year - so this moves the first datetime to the previous day
+    # Sometimes the first datetime for solar time occurs before the target year -
+    # so this moves the first datetime to the previous day
     for i in range(12):
         if (_datetimes[i].year == _datetimes[-1].year) and (_datetimes[i].hour > 12):
             _datetimes[i] = _datetimes[i] - pd.Timedelta(days=1)
@@ -497,6 +481,7 @@ def solar_time_datetime(
     )
 
 
+@bhom_analytics()
 def solar_declination(epw: EPW) -> HourlyContinuousCollection:
     """Calculate solar declination for each hour of the year.
 
@@ -532,6 +517,7 @@ def solar_declination(epw: EPW) -> HourlyContinuousCollection:
     )
 
 
+@bhom_analytics()
 def solar_azimuth(
     epw: EPW, sun_position: HourlyContinuousCollection = None
 ) -> HourlyContinuousCollection:
@@ -564,6 +550,7 @@ def solar_azimuth(
     )
 
 
+@bhom_analytics()
 def solar_azimuth_radians(
     epw: EPW, sun_position: HourlyContinuousCollection = None
 ) -> HourlyContinuousCollection:
@@ -586,6 +573,7 @@ def solar_azimuth_radians(
     return collection
 
 
+@bhom_analytics()
 def solar_altitude(
     epw: EPW, sun_position: HourlyContinuousCollection = None
 ) -> HourlyContinuousCollection:
@@ -618,6 +606,7 @@ def solar_altitude(
     )
 
 
+@bhom_analytics()
 def solar_altitude_radians(
     epw: EPW, sun_position: HourlyContinuousCollection = None
 ) -> HourlyContinuousCollection:
@@ -640,6 +629,7 @@ def solar_altitude_radians(
     return collection
 
 
+@bhom_analytics()
 def humidity_ratio(epw: EPW) -> HourlyContinuousCollection:
     """Calculate an annual hourly humidity ratio for a given EPW.
 
@@ -663,11 +653,12 @@ def humidity_ratio(epw: EPW) -> HourlyContinuousCollection:
     )
 
 
-def epw_from_dict(dictionary: Dict[str, Any]) -> EPW:
+@bhom_analytics()
+def epw_from_dict(dictionary: dict[str, Any]) -> EPW:
     """Convert a JSON compliant dictionary object into a ladybug EPW.
 
     Args:
-        Dict[str, Any]:
+        dict[str, Any]:
             A sanitised dictionary.
 
     Returns:
@@ -689,6 +680,7 @@ def epw_from_dict(dictionary: Dict[str, Any]) -> EPW:
     return EPW.from_dict(json.loads(json_str))
 
 
+@bhom_analytics()
 def get_filename(epw: EPW, include_extension: bool = False) -> str:
     """Get the filename of the given EPW.
 
@@ -709,6 +701,7 @@ def get_filename(epw: EPW, include_extension: bool = False) -> str:
     return Path(epw.file_path).stem
 
 
+@bhom_analytics()
 def equality(epw0: EPW, epw1: EPW, include_header: bool = False) -> bool:
     """Check for equality between two EPW objects, with regards to the data contained within.
 
@@ -744,6 +737,7 @@ def equality(epw0: EPW, epw1: EPW, include_header: bool = False) -> bool:
         "direct_normal_radiation",
         "diffuse_horizontal_radiation",
         "atmospheric_station_pressure",
+        "monthly_ground_temperature",
     ]:
         if getattr(epw0, var) != getattr(epw1, var):
             warnings.warn(f"{var}: {epw0} != {epw1}")
@@ -752,6 +746,7 @@ def equality(epw0: EPW, epw1: EPW, include_header: bool = False) -> bool:
     return True
 
 
+@bhom_analytics()
 def equation_of_time(epw: EPW) -> HourlyContinuousCollection:
     """Calculate the equation of time for each hour of the year.
 
@@ -786,13 +781,14 @@ def equation_of_time(epw: EPW) -> HourlyContinuousCollection:
     )
 
 
-def epw_content_check(epw: EPW, fields: List[str] = None) -> bool:
+@bhom_analytics()
+def epw_content_check(epw: EPW, fields: list[str] = None) -> bool:
     """Check an EPW object for whether it contains all valid fields
 
     Args:
         epw (EPW):
             An EPW object.
-        fields (List[str], optional):
+        fields (list[str], optional):
             The fields subset to check.
 
     Returns:
@@ -854,6 +850,7 @@ def epw_content_check(epw: EPW, fields: List[str] = None) -> bool:
     return valid
 
 
+@bhom_analytics()
 def enthalpy(
     epw: EPW, hum_ratio: HourlyContinuousCollection = None
 ) -> HourlyContinuousCollection:
@@ -884,6 +881,7 @@ def enthalpy(
     )
 
 
+@bhom_analytics()
 def clearness_index(
     epw: EPW, sun_position: HourlyContinuousCollection = None
 ) -> HourlyContinuousCollection:
@@ -928,6 +926,7 @@ def clearness_index(
     )
 
 
+@bhom_analytics()
 def seasonality_from_day_length_location(
     location: Location, annotate: bool = False
 ) -> pd.Series:
@@ -1028,6 +1027,7 @@ def seasonality_from_day_length_location(
     return pd.Series(categories, index=idx, name="season")
 
 
+@bhom_analytics()
 def seasonality_from_temperature_timeseries(
     series: pd.Series, annotate: bool = False
 ) -> pd.Series:
@@ -1062,7 +1062,8 @@ def seasonality_from_temperature_timeseries(
     # check that weatherfile is "seasonal", by checking avg variance
     if series.std() <= 2.5:
         warnings.warn(
-            "Input dataset has a low variance, indicating that seasonality may not be determined accurately from dry-bulb temperature."
+            "Input dataset has a low variance, indicating that seasonality may "
+            "not be determined accurately from dry-bulb temperature."
         )
 
     # create the aggregate year
@@ -1227,24 +1228,30 @@ def seasonality_from_temperature_timeseries(
 
     # get mean temps if annotated
     if annotate:
-        s[
-            s == "Spring"
-        ] = f"Spring ({dbt[s == 'Spring'].mean():0.1f}°C average temperature, {spring_start:%b %d} to {summer_start:%b %d})"
-        s[
-            s == "Summer"
-        ] = f"Summer ({dbt[s == 'Summer'].mean():0.1f}°C average temperature, {summer_start:%b %d} to {autumn_start:%b %d})"
-        s[
-            s == "Autumn"
-        ] = f"Autumn ({dbt[s == 'Autumn'].mean():0.1f}°C average temperature, {autumn_start:%b %d} to {winter_start:%b %d})"
-        s[
-            s == "Winter"
-        ] = f"Winter ({dbt[s == 'Winter'].mean():0.1f}°C average temperature, {winter_start:%b %d} to {spring_start:%b %d})"
+        s[s == "Spring"] = (
+            f"Spring ({dbt[s == 'Spring'].mean():0.1f}°C average temperature, "
+            f"{spring_start:%b %d} to {summer_start:%b %d})"
+        )
+        s[s == "Summer"] = (
+            f"Summer ({dbt[s == 'Summer'].mean():0.1f}°C average temperature, "
+            f"{summer_start:%b %d} to {autumn_start:%b %d})"
+        )
+        s[s == "Autumn"] = (
+            f"Autumn ({dbt[s == 'Autumn'].mean():0.1f}°C average temperature, "
+            f"{autumn_start:%b %d} to {winter_start:%b %d})"
+        )
+        s[s == "Winter"] = (
+            f"Winter ({dbt[s == 'Winter'].mean():0.1f}°C average temperature, "
+            f"{winter_start:%b %d} to {spring_start:%b %d})"
+        )
 
     return s
 
 
+@bhom_analytics()
 def seasonality_from_day_length(epw: EPW, annotate: bool = False) -> pd.Series:
-    """Create a Series containing a category for each timestep of an EPW giving it's season based on day length (using sunrise/sunset).
+    """Create a Series containing a category for each timestep of an EPW giving
+    its season based on day length (using sunrise/sunset).
 
     Args:
         epw (EPW):
@@ -1260,6 +1267,7 @@ def seasonality_from_day_length(epw: EPW, annotate: bool = False) -> pd.Series:
     return seasonality_from_day_length_location(epw.location, annotate)
 
 
+@bhom_analytics()
 def seasonality_from_month(epw: EPW, annotate: bool = False) -> pd.Series:
     """Create a Series containing a category for each timestep of an EPW giving it's season.
 
@@ -1319,6 +1327,7 @@ def seasonality_from_month(epw: EPW, annotate: bool = False) -> pd.Series:
     return pd.Series(categories, index=idx, name="season")
 
 
+@bhom_analytics()
 def seasonality_from_temperature(epw: EPW, annotate: bool = False) -> pd.Series:
     """Create a Series containing a category for each timestep of an EPW giving it's season.
     Args:
@@ -1336,18 +1345,19 @@ def seasonality_from_temperature(epw: EPW, annotate: bool = False) -> pd.Series:
     )
 
 
+@bhom_analytics()
 def degree_time(
-    epws: List[EPW],
+    epws: list[EPW],
     heat_base: float = 18,
     cool_base: float = 23,
     return_type: str = "days",
-    names: List[str] = None,
+    names: list[str] = None,
 ) -> pd.DataFrame:
     """Get the heating/cooling degree days/hours from a given set of EPW
     objects.
 
     Args:
-        epws (List[EPW]):
+        epws (list[EPW]):
             A list of EPW objcts.
         heat_base (float, optional):
             The temperature at which heating kicks in. Defaults to 18.
@@ -1355,7 +1365,7 @@ def degree_time(
             The temperature at which cooling kicks in. Defaults to 23.
         return_type (str, optional):
             Return days or hours. Defaults to "days".
-        names (List[str], optional):
+        names (list[str], optional):
             Names to overide EPW names with. Defaults to None.
 
     Returns:
@@ -1414,8 +1424,8 @@ def degree_time(
             [cdd, hdd],
             axis=1,
             keys=[
-                f"Cooling Degree Days (>{cool_base})",
-                f"Heating Degree Days (<{heat_base})",
+                f"Cooling Degree Days (>{cool_base}°C)",
+                f"Heating Degree Days (<{heat_base}°C)",
             ],
         )
         .reorder_levels([1, 0], axis=1)
@@ -1423,16 +1433,22 @@ def degree_time(
     )
 
 
+@bhom_analytics()
 def translate_to_height(epw: EPW, target_height: float, save: bool = False) -> EPW:
-    """Translate an EPW to a different height above ground (assuming that the original height represented is at ground level, with wind measured at 10m).
+    """Translate an EPW to a different height above ground (assuming that the
+    original height represented is at ground level, with wind measured at 10m).
 
     Args:
-        epw (EPW): The EPW to translate.
-        target_height (float): The target height above ground, in m.
-        save (bool, optional): If True, save the translated EPW to disk. Defaults to False.
+        epw (EPW):
+            The EPW to translate.
+        target_height (float):
+            The target height above ground, in m.
+        save (bool, optional):
+            If True, save the translated EPW to disk. Defaults to False.
 
     Returns:
-        EPW: The translated EPW.
+        EPW:
+            The translated EPW.
     """
 
     new_epw = copy.deepcopy(epw)
@@ -1443,8 +1459,9 @@ def translate_to_height(epw: EPW, target_height: float, save: bool = False) -> E
 
     # modify header
     new_epw.comments_1 = (
-        epw.comments_1
-        + f"Modified from {original_height}m elevation at 10m above ground (assumed for EPW data), to {target_height}m above ground."
+        f"{epw.comments_1} "
+        f"Modified from {original_height}m elevation at 10m above ground "
+        f"(assumed for EPW data), to {target_height}m above ground."
     )
     new_epw.location.source = f"{epw.location.source}[@{target_height}M_ELEVATION]"
 
@@ -1614,12 +1631,12 @@ def translate_to_height(epw: EPW, target_height: float, save: bool = False) -> E
     ]
 
     # set filepath variable
-    # pylint disable=protected-access
+    # pylint: disable=protected-access
     new_epw._file_path = (
         Path(epw.file_path).parent
         / f"{Path(epw.file_path).stem}_at{target_height}m.epw"
     ).as_posix()
-    # pylint enable=protected-access
+    # pylint: enable=protected-access
 
     if save:
         new_epw.save(Path(new_epw.file_path).absolute().as_posix())
@@ -1630,15 +1647,16 @@ def translate_to_height(epw: EPW, target_height: float, save: bool = False) -> E
 def _representative_day() -> pd.DataFrame:
     """Create a table contiing hourly values for the "typical" day the given month."""
 
-    #! - TODO - add weighting of variables
-    #! - TODO - add option to use median or mean values
-    #! - TODO - add option to use specific time period (perhpas based on AnalysisPeriod, or more generic)
-    #! - TODO - return whole table (all collections? and otehr days), or just specific day
+    #! TODO - add weighting of variables
+    #! TODO - add option to use median or mean values
+    #! TODO - add option to use specific time period (perhpas based on AnalysisPeriod, or more generic)
+    #! TODO - return whole table (all collections? and otehr days), or just specific day
     #! TODO - ensure that wind direction is being accounted for correctly - aliginign with the peak weighted variable/s
 
-    raise NotImplementedError
+    raise NotImplementedError("")
 
 
+@bhom_analytics()
 def hottest_day(epw: EPW) -> datetime:
     """
     The hottest day in the year associated with this epw object
@@ -1655,6 +1673,7 @@ def hottest_day(epw: EPW) -> datetime:
     )
 
 
+@bhom_analytics()
 def coldest_day(epw: EPW) -> datetime:
     """
     The coldest day in the year associated with this epw object
@@ -1671,7 +1690,8 @@ def coldest_day(epw: EPW) -> datetime:
     )
 
 
-def sun_up_bool(epw: EPW) -> List[bool]:
+@bhom_analytics()
+def sun_up_bool(epw: EPW) -> list[bool]:
     """
     A list of booleans aligned with the input EPW stating whether the sun is above the horizon.
     """
@@ -1683,6 +1703,7 @@ def sun_up_bool(epw: EPW) -> List[bool]:
     return [i.altitude > 0 for i in suns]
 
 
+@bhom_analytics()
 def longest_day(epw: EPW) -> datetime:
     """
     The longest day in the year associated with this epw object."""
@@ -1695,6 +1716,7 @@ def longest_day(epw: EPW) -> datetime:
     return s[s > 0].resample("D").count().idxmax().to_pydatetime()
 
 
+@bhom_analytics()
 def shortest_day(epw: EPW) -> datetime:
     """
     The shortest day in the year associated with this epw object."""
@@ -1707,13 +1729,14 @@ def shortest_day(epw: EPW) -> datetime:
     return s[s > 0].resample("D").count().idxmin().to_pydatetime()
 
 
+@bhom_analytics()
 def average_epw(
-    epws: List[EPW],
+    epws: list[EPW],
     location: Location = None,
     comments_1: str = "",
     comments_2: str = "",
     file_path: str = "synthetic.epw",
-    weights: List[float] = None,
+    weights: list[float] = None,
 ) -> EPW:
     """For a set of input EPW files, construct an "average", with wind speeds and direction weighted based on speed."""
     warnings.warn("UNDER DEVELOPMENT")
@@ -1731,7 +1754,7 @@ def average_epw(
     # set metadata
     synthetic_epw.comments_1 = comments_1
     synthetic_epw.comments_2 = comments_2
-    synthetic_epw._file_path = file_path
+    synthetic_epw._file_path = file_path  # pylint: disable=protected-access
 
     # set ground temperayture data
     all_depths = []
@@ -1751,7 +1774,7 @@ def average_epw(
         try:
             for coll in collections[1:]:
                 base += coll
-        except:
+        except Exception as _:  # pylint: disable=broad-except
             pass
         base = base / len(collections)
         d[depth] = base
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/groundtemperature.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/groundtemperature.py
new file mode 100644
index 00000000..99d1edd9
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/groundtemperature.py
@@ -0,0 +1,278 @@
+"""Methods for calculating ground temperature values from EPW files."""
+
+# pylint: disable=E0401
+import inspect
+from warnings import warn
+
+# pylint: enable=E0401
+
+import numpy as np
+import pandas as pd
+from honeybee_energy.lib.scheduletypelimits import schedule_type_limit_by_identifier
+from honeybee_energy.schedule.fixedinterval import ScheduleFixedInterval
+from ladybug.datacollection import HourlyContinuousCollection, MonthlyCollection
+from ladybug.epw import EPW
+
+from ..bhom.analytics import bhom_analytics
+from .datacollection import collection_from_series, collection_to_series, to_hourly
+
+
+@bhom_analytics()
+def energyplus_strings(epw: EPW) -> str:
+    """Generate strings to add into EnergyPlus simulation for slightly more
+    accurate ground surface temperature results.
+
+    Args:
+        epw (EPW):
+            A ladybug EPW object.
+
+    Returns:
+        List[str]:
+            Strings to append to the EnergyPlus IDF simulation input file.
+    """
+
+    monthly_ground_temperatures = monthly_ground_temperature(epw, 0.5)
+
+    ground_temperature_str = inspect.cleandoc(
+        f"""
+            Site:GroundTemperature:BuildingSurface,
+                {monthly_ground_temperatures[0]}, !- January Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[1]}, !- February Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[2]}, !- March Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[3]}, !- April Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[4]}, !- May Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[5]}, !- June Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[6]}, !- July Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[7]}, !- August Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[8]}, !- September Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[9]}, !- October Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[10]}, !- November Surface Ground Temperature {{C}}
+                {monthly_ground_temperatures[11]}; !- December Surface Ground Temperature {{C}}
+            """
+    )
+
+    return ground_temperature_str
+
+
+@bhom_analytics()
+def eplus_otherside_coefficient(epw: EPW) -> str:
+    """Generate strings to add into EnergyPlus simulation for annual-hourly
+    ground temperature values applied to sub-ground surfaces.
+
+    Args:
+        epw (EPW):
+            A ladybug EPW object.
+
+    Returns:
+        List[str]:
+            Strings to append to the EnergyPlus IDF simulation input file.
+    """
+    gnd_temp_collection = hourly_ground_temperature(epw, 0.5)
+
+    gnd_schedule = ScheduleFixedInterval(
+        "GroundTemperatureSchedule",
+        gnd_temp_collection.values,
+        schedule_type_limit_by_identifier("Temperature"),
+    )
+
+    ground_temperature_str = inspect.cleandoc(
+        """
+            SurfaceProperty:OtherSideCoefficients,
+                GroundTemperature,          !- Name
+                0,                          !- Combined Convective/Radiative Film Coefficient {{W/m2-K}}
+                0.000000,                   !- Constant Temperature {{C}}"
+                1.000000,                   !- Constant Temperature Coefficient"
+                0.000000,                   !- External Dry-Bulb Temperature Coefficient"
+                0.000000,                   !- Ground Temperature Coefficient"
+                0.000000,                   !- Wind Speed Coefficient"
+                0.000000,                   !- Zone Air Temperature Coefficient"
+                GroundTemperatureSchedule;  !- Constant Temperature Schedule Name"
+        """
+    )
+
+    ground_temperature_str += f"\n\n{gnd_schedule.to_idf_compact()}"
+
+    return ground_temperature_str
+
+
+@bhom_analytics()
+def ground_temperature_at_depth(
+    epw: EPW, depth: float, soil_diffusivity: float = 0.31e-6
+) -> HourlyContinuousCollection:
+    # pylint: disable=W1401,C0301
+    """Construct annual hourly ground temperature at given depth.
+    This method uses the IEC 60287-3-1 method for approximating soil
+    temperature.
+
+    Args:
+        epw (EPW):
+            A ladybug EPW object.
+        depth (float):
+            The depth at which the ground temperature will be calculated.
+        soil_diffusivity (float, optional):
+            Soil diffusivity value. Common values available from the table
+            below. Defaults to 0.31e-6.
+
+    Example:
+        Below is a table from https://dx.doi.org/10.3390%2Fs16030306 detailing suitable diffusivity
+        values for different soil types:
+
+        +---------------------------+-----------------------------+-----------------------------------+-----------------------------------+
+        | RockType                  | Thermal Conductivity (W/mK) | Volumetric Heat Capacity (MJ/m3K) | Thermal Diffusivity (10^6 m2/s)   |
+        |                           +-----+-----+-----------------+-----------------------------------+-------+------+------+-------------+
+        |                           | Min | Typ | Max             |                                   | Min   | Typ  | Max                |
+        +===========================+=====+=====+=================+===================================+=======+======+====================+
+        | Basalt                    | 1.3 | 1.7 | 2.3             |     2.6                           | 0.5   | 0.65 | 0.88               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Greenstone                | 2.0 | 2.6 | 2.9             |     2.9                           | 0.69  | 0.90 | 1.00               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Gabbro                    | 1.7 | 1.9 | 2.5             |     2.6                           | 0.65  | 0.73 | 0.96               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Granite                   | 2.1 | 3.4 | 4.1             |     3.0                           | 0.70  | 1.13 | 1.37               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Peridotite                | 3.8 | 4,0 | 5.3             |     2.7                           | 1.41  | 1.48 | 1.96               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Gneiss                    | 1.9 | 2.9 | 4.0             |     2.4                           | 0.79  | 1.21 | 1.67               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Marble                    | 1.3 | 2.1 | 3.1             |     2.0                           | 0.65  | 1.05 | 1.55               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Mica schist               | 1.5 | 2,0 | 3.1             |     2.2                           | 0.68  | 0.91 | 1.41               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Shale sedimentary         | 1.5 | 2.1 | 2.1             |     2.5                           | 0.60  | 0.84 | 0.84               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Limestone                 | 2.5 | 2.8 | 4.0             |     2.4                           | 1.04  | 1.17 | 1.67               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Loam                      | 1.5 | 2.1 | 3.5             |     2.3                           | 0.65  | 0.91 | 1.52               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Quartzite                 | 3.6 | 6,0 | 6.6             |     2.2                           | 1.64  | 2.73 | 3.00               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Salt                      | 5.3 | 5.4 | 6.4             |     1.2                           | 4.42  | 4.50 | 5.33               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Sandstone                 | 1.3 | 2.3 | 5.1             |     2.8                           | 0.46  | 0.82 | 1.82               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Siltstones and argillites | 1.1 | 2.2 | 3.5             |     2.4                           | 0.46  | 0.92 | 1.46               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Dry gravel                | 0.4 | 0.4 | 0.5             |     1.6                           | 0.25  | 0.25 | 0.31               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Water saturated gravel    | 1.8 | 1.8 | 1.8             |     2.4                           | 0.75  | 0.75 | 0.75               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Dry sand                  | 0.3 | 0.4 | 0.55            |     1.6                           | 0.19  | 0.25 | 0.34               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Water saturated sand      | 1.7 | 2.4 | 5.0             |     2.9                           | 0.59  | 0.83 | 1.72               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Dry clay/silt             | 0.4 | 0.5 | 1.0             |     1.6                           | 0.25  | 0.31 | 0.62               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Water saturated clay/silt | 0.9 | 1.7 | 2.3             |     3.4                           | 0.26  | 0.5  | 0.68               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+        | Peat                      | 0.2 | 0.4 | 0.7             |     3.8                           | 0.05  | 0.10 | 0.18               |
+        +---------------------------+-----+-----+-----------------+-----------------------------------+-------+------+--------------------+
+
+    Returns:
+        HourlyContinuousCollection:
+            A data collection containing ground temperature values.
+    """
+    # pylint: enable=W1401,C0301
+
+    try:
+        return to_hourly(epw.monthly_ground_temperature[depth])
+    except (KeyError, ValueError):
+        dbt = collection_to_series(epw.dry_bulb_temperature)
+
+        dbt_range = dbt.max() - dbt.min()
+        dbt_mean = dbt.mean()
+        day_count = len(dbt.index.dayofyear.unique())
+
+        coldest_day = dbt.resample("D").min().values.argmin()
+        days_since_coldest_day = []
+        for i in range(day_count):
+            if i <= coldest_day:
+                days_since_coldest_day.append(day_count - (coldest_day - i))
+            else:
+                days_since_coldest_day.append(i - coldest_day)
+
+        annual_profile = 2 * np.pi / day_count
+        annual_profile_factored = np.sqrt(2 * soil_diffusivity * 86400 / annual_profile)
+        gnd_temp_daily = (
+            pd.Series(
+                [
+                    dbt_mean
+                    - (dbt_range / 2)
+                    * np.exp(-depth / annual_profile_factored)
+                    * np.cos((annual_profile * i) - (depth / annual_profile_factored))
+                    for i in days_since_coldest_day
+                ],
+                index=dbt.resample("D").min().index,
+            )
+            .resample("60T")
+            .mean()
+            .interpolate()
+            .values
+        )
+        gnd_temp_hourly = list(gnd_temp_daily) + list(gnd_temp_daily[0:23])
+        gnd_temp_hourly = pd.Series(
+            gnd_temp_hourly, index=dbt.index, name="Ground Temperature (C)"
+        )
+
+        return collection_from_series(gnd_temp_hourly)
+
+
+@bhom_analytics()
+def hourly_ground_temperature(
+    epw: EPW, depth: float = 0.5, soil_diffusivity: float = 0.31e-6
+) -> HourlyContinuousCollection:
+    """Return the hourly ground temperature values from the EPW file
+    (upsampled from any available monthly values), or approximate these if
+    not available.
+
+    Args:
+        epw (EPW):
+            The EPW file to extract ground temperature data from.
+        depth (float, optional):
+            The depth of the soil in meters. Defaults to 0.5 meters.
+        soil_diffusivity (float, optional):
+            The soil diffusivity in m2/s. Defaults to 0.31e-6 m2/s.
+
+    Returns:
+        MonthlyCollection:
+            A data collection containing ground temperature values at the
+            depth specified.
+    """
+
+    try:
+        return to_hourly(epw.monthly_ground_temperature[depth])
+    except KeyError:
+        return ground_temperature_at_depth(epw, depth, soil_diffusivity)
+
+
+@bhom_analytics()
+def monthly_ground_temperature(
+    epw: EPW, depth: float = 0.5, soil_diffusivity: float = 0.31e-6
+) -> MonthlyCollection:
+    """Return the monthly ground temperature values from the EPW file, or
+    approximate these if not available.
+
+    Args:
+        epw (EPW):
+            The EPW file to extract ground temperature data from.
+        depth (float, optional):
+            The depth of the soil in meters. Defaults to 0.5 meters.
+        soil_diffusivity (float, optional):
+            The soil diffusivity in m2/s. Defaults to 0.31e-6 m2/s.
+
+    Returns:
+        MonthlyCollection:
+            A data collection containing ground temperature values at the
+            depth specified.
+    """
+
+    try:
+        return epw.monthly_ground_temperature[depth]
+    except KeyError:
+        warn(
+            "The input EPW doesn't contain any monthly ground temperatures "
+            f"at {depth}m. An approximation method will be used to determine "
+            "ground temperature at that depth based on ambient dry-bulb."
+        )
+        return ground_temperature_at_depth(
+            epw, depth, soil_diffusivity
+        ).average_monthly()
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/header.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/header.py
index 6e4915a0..f9923e5e 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/header.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/header.py
@@ -1,9 +1,13 @@
+"""Methods for manipulating Ladybug header objects."""
+
 import pandas as pd
 from ladybug.analysisperiod import AnalysisPeriod
 from ladybug.datatype import TYPESDICT
 from ladybug.datatype.generic import GenericType
 from ladybug.header import Header
 
+from ..bhom.analytics import bhom_analytics
+
 
 def header_to_string(header: Header) -> str:
     """Convert a Ladybug header object into a string.
@@ -19,6 +23,7 @@ def header_to_string(header: Header) -> str:
     return f"{header.data_type} ({header.unit})"
 
 
+@bhom_analytics()
 def header_to_multiindex(header: Header) -> pd.MultiIndex:
     """Convert a Ladybug header object into a Pandas MultiIndex. Used for creating CSV headers for
         reloading by Ladybug.
@@ -78,6 +83,7 @@ def header_from_string(string: str, is_leap_year: bool = False) -> Header:
     )
 
 
+@bhom_analytics()
 def header_from_multiindex(multiindex: pd.MultiIndex) -> Header:
     """Convert a Pandas MultiIndex into a Ladybug header object.
 
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/koeppen_classification.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/koeppen_classification.py
index 4b1d9955..933afada 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/koeppen_classification.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/koeppen_classification.py
@@ -1,11 +1,16 @@
+"""Methods for determining the Koeppen climate classification of a location."""
+
+# pylint: disable=E0401
 from dataclasses import dataclass, field
 from enum import Enum
 from pathlib import Path
-from typing import Dict, Tuple, Union
+
+# pylint: enable=E0401
 
 import pandas as pd
 from ladybug.epw import EPW, Location
 from scipy import spatial
+from ..bhom.analytics import bhom_analytics
 
 
 @dataclass
@@ -54,6 +59,7 @@ def __post_init__(self):
         if self.classification not in classifications:
             raise ValueError(f"Invalid Koeppen classification: {self.classification}")
 
+    @bhom_analytics()
     def to_dict(self):
         """Return a dictionary representation of the KoeppenClassification."""
         return {
@@ -280,13 +286,14 @@ class KoeppenClimateClassifications(Enum):
     )
 
 
+@bhom_analytics()
 def koeppen_classification(
-    arg: Union[EPW, Location, str, Path, Tuple[float]]
+    arg: EPW | Location | Path | tuple[float],
 ) -> KoeppenClassification:
     """For the given EPW object, return a dict containing Koeppen climate classification data
 
     Args:koeppenAS
-        arg (Union[EPW, str, Path, Tuple[float]):
+        arg (EPW | Location | Path | tuple[float]):
             An EPW object, or a tuple of (latitude, longitude) values, or a string or Path to an EPW file.
 
     Returns:
@@ -304,7 +311,8 @@ def koeppen_classification(
         location = Location(latitude=arg[0], longitude=arg[1])
     else:
         raise TypeError(
-            f"Expected an EPW object, a tuple of (latitude, longitude) values, or a string or Path to an EPW file. Got {type(arg)}"
+            "Expected an EPW object, a tuple of (latitude, longitude) values, "
+            f"or a string or Path to an EPW file. Got {type(arg)}"
         )
 
     # Load koeppen climate lookup dataset
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/location.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/location.py
index 6267985b..0dd48426 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/location.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/location.py
@@ -1,10 +1,11 @@
-from typing import List
-from warnings import warn
-
+"""Methods for manipulating Ladybug Location objects."""
 import numpy as np
 from ladybug.location import Location
 
+from ..bhom.analytics import bhom_analytics
+
 
+@bhom_analytics()
 def location_to_string(location: Location) -> str:
     """Return a simple string representation of the Location object.
 
@@ -19,14 +20,19 @@ def location_to_string(location: Location) -> str:
     return f"{location.country.strip()} - {location.city.strip()}"
 
 
+@bhom_analytics()
 def average_location(
-    locations: List[Location], weights: List[float] = None
+    locations: list[Location], weights: list[float] = None
 ) -> Location:
-    """Create an average location from a list of locations. This will use weighting if provided to adjust latitude/longitude values.
+    """Create an average location from a list of locations.
+    This will use weighting if provided to adjust latitude/longitude values.
 
     Args:
-        locations (List[Location]): A set of ladybug Location objects.
-        weights (List[float], optional): A list of weights for each location. Defaults to None which evenly weights each location.
+        locations (list[Location]):
+            A set of ladybug Location objects.
+        weights (list[float], optional):
+            A list of weights for each location.
+            Defaults to None which evenly weights each location.
 
     Returns:
         Location: A synthetic location that is the average of all locations.
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/pv_yield.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/pv_yield.py
deleted file mode 100644
index f438229d..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/pv_yield.py
+++ /dev/null
@@ -1,478 +0,0 @@
-from __future__ import annotations
-
-import copy
-import itertools
-import warnings
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import dataclass, field
-from enum import Enum, auto
-from typing import Tuple
-
-import matplotlib.ticker as mticker
-import numpy as np
-import pandas as pd
-from ladybug.wea import EPW, AnalysisPeriod, Wea
-from matplotlib import pyplot as plt
-from tqdm import tqdm
-
-from .analysis_period import (
-    analysis_period_to_boolean,
-    analysis_period_to_datetimes,
-    describe_analysis_period,
-)
-from .location import location_to_string
-
-
-class IrradianceType(Enum):
-    """Irradiance types."""
-
-    TOTAL = auto()
-    DIRECT = auto()
-    DIFFUSE = auto()
-    REFLECTED = auto()
-
-    def to_string(self) -> str:
-        """Get the string representation of the IrradianceType."""
-        return self.name.title()
-
-
-class IrradianceUnit(Enum):
-    """Irradiance units."""
-
-    WH_M2 = auto()
-    KWH_M2 = auto()
-    MWH_M2 = auto()
-
-    def to_string(self) -> str:
-        """Get the string representation of the IrradianceUnit."""
-        d = {
-            self.WH_M2.value: "Wh/m$^{2}$",
-            self.KWH_M2.value: "kWh/m$^{2}$",
-            self.MWH_M2.value: "MWh/m$^{2}$",
-        }
-        return d[self.value]
-
-    @property
-    def multiplier(self) -> float:
-        """The multiplier for the unit."""
-        d = {
-            self.WH_M2.value: 1,
-            self.KWH_M2.value: 0.001,
-            self.MWH_M2.value: 0.000001,
-        }
-        return d[self.value]
-
-
-@dataclass(init=True, repr=True, eq=True)
-class PVYieldMatrix:
-    """Compute the annual cumulative radiation matrix per surface tilt and orientation, for a given Wea object.
-    Args:
-        wea (Wea):
-            The Wea object for which this calculation is made.
-        n_altitudes (int, optional):
-            The number of altitudes between 0 and 90 to calculate. Default is 10.
-        n_azimuths (int, optional):
-            The number of azimuths between 0 and 360 to calculate. Default is 19.
-        ground_reflectance (float, optional):
-            The ground reflectance to use for the calculation. Default is 0.2.
-        isotropic (bool, optional):
-            An isotropic sky assumes an even distribution of diffuse
-            irradiance across the sky while an anisotropic sky places more
-            diffuse irradiance near the solar disc.
-            Set to True to use isotropic sky model. Default is False.
-    Returns:
-        pd.DataFrame:
-            A table of insolation values for each simulated azimuth and tilt combo.
-    """
-
-    wea: Wea = field(init=True, repr=True)
-    n_altitudes: int = field(init=True, repr=True, default=10)  # 10
-    n_azimuths: int = field(init=True, repr=True, default=19)  # 19
-    ground_reflectance: float = field(init=True, repr=True, default=0.2)
-    isotropic: bool = field(init=True, repr=True, default=False)
-    ###########
-    _calculated_irradiance: Tuple[Tuple[Tuple[float]]] = field(
-        init=False, repr=False, default=None
-    )
-    _total: Tuple[Tuple[float]] = field(init=False, repr=False, default=None)
-    _direct: Tuple[Tuple[float]] = field(init=False, repr=False, default=None)
-    _diffuse: Tuple[Tuple[float]] = field(init=False, repr=False, default=None)
-    _reflected: Tuple[Tuple[float]] = field(init=False, repr=False, default=None)
-
-    def __post_init__(self):
-        if self.n_altitudes <= 2:
-            raise ValueError("n_altitudes must be greater than 2.")
-        if self.n_azimuths <= 2:
-            raise ValueError("n_azimuths must be greater than 2.")
-
-        self._calculated_irradiance = (
-            self._calculate_irradiance()
-            if self._calculated_irradiance is None
-            else self._calculated_irradiance
-        )
-        #####
-        self._total = tuple([i[0] for i in self._calculated_irradiance])
-        self._direct = tuple([i[1] for i in self._calculated_irradiance])
-        self._diffuse = tuple([i[2] for i in self._calculated_irradiance])
-        self._reflected = tuple([i[3] for i in self._calculated_irradiance])
-
-    @property
-    def altitudes(self) -> Tuple[float]:
-        """The altitudes to calculate."""
-        return np.linspace(0, 90, self.n_altitudes)
-
-    @property
-    def azimuths(self) -> Tuple[float]:
-        """The azimuths to calculate."""
-        return np.linspace(0, 360, self.n_azimuths)
-
-    @property
-    def _alt_az_combinations(self) -> Tuple[Tuple[float]]:
-        """The combinations of azimuths and altitudes to calculate."""
-        return np.array(list(itertools.product(self.altitudes, self.azimuths)))
-
-    def get_irradiance(
-        self,
-        irradiance_type: IrradianceType = IrradianceType.TOTAL,
-        analysis_period: AnalysisPeriod = AnalysisPeriod(),
-        agg: str = "sum",
-    ) -> pd.DataFrame:
-        """Get the irradiance for the given irradiance type.
-
-        Args:
-            irradiance_type (IrradianceType, optional):
-                The irradiance type to get. Default is IrradianceType.TOTAL.
-            analysis_period (AnalysisPeriod, optional):
-                The analysis period for which to calculate the irradiance. Default is AnalysisPeriod().
-            agg (str, optional):
-                The aggregation method to use for the calculation. Default is "sum".
-
-        Returns:
-            pd.DataFrame: A table of irradiance values for each simulated azimuth and tilt combo.
-        """
-        if irradiance_type.value == IrradianceType.TOTAL.value:
-            return self.total_irradiance(analysis_period=analysis_period, agg=agg)
-        if irradiance_type.value == IrradianceType.DIRECT.value:
-            return self.direct_irradiance(analysis_period=analysis_period, agg=agg)
-        if irradiance_type.value == IrradianceType.DIFFUSE.value:
-            return self.diffuse_irradiance(analysis_period=analysis_period, agg=agg)
-        if irradiance_type.value == IrradianceType.REFLECTED.value:
-            return self.reflected_irradiance(analysis_period=analysis_period, agg=agg)
-
-        raise ValueError("irradiance_type must be a IrradianceType.")
-
-    def total_irradiance(
-        self, analysis_period: AnalysisPeriod = AnalysisPeriod(), agg: str = "sum"
-    ) -> pd.DataFrame:
-        """The total irradiance for each combination of tilt and orientation, within the given analysis period.
-
-        Args:
-            analysis_period (AnalysisPeriod, optional):
-                The analysis period for which to calculate the total irradiance. Default is AnalysisPeriod().
-            agg (str, optional):
-                The aggregation method to use for the calculation. Default is "sum".
-        Returns:
-            pd.DataFrame:
-                A table of total irradiance values for each simulated azimuth and tilt combo.
-        """
-        mask = analysis_period_to_boolean(analysis_period)
-        dts = analysis_period_to_datetimes(analysis_period)
-
-        temp = pd.DataFrame(
-            np.array(self._total).T[mask].T,
-            columns=dts,
-            index=pd.MultiIndex.from_arrays(
-                self._alt_az_combinations.T, names=["altitude", "azimuth"]
-            ),
-        )
-        temp = temp.agg(agg, axis=1)
-        temp = temp.unstack()
-
-        return temp
-
-    def diffuse_irradiance(
-        self, analysis_period: AnalysisPeriod = AnalysisPeriod(), agg: str = "sum"
-    ) -> pd.DataFrame:
-        """The diffuse irradiance for each combination of tilt and orientation, within the given analysis period.
-
-        Args:
-            analysis_period (AnalysisPeriod, optional):
-                The analysis period for which to calculate the total irradiance. Default is AnalysisPeriod().
-            agg (str, optional):
-                The aggregation method to use for the calculation. Default is "sum".
-        Returns:
-            pd.DataFrame:
-                A table of diffuse irradiance values for each simulated azimuth and tilt combo.
-        """
-        mask = analysis_period_to_boolean(analysis_period)
-        dts = analysis_period_to_datetimes(analysis_period)
-
-        temp = pd.DataFrame(
-            np.array(self._diffuse).T[mask].T,
-            columns=dts,
-            index=pd.MultiIndex.from_arrays(
-                self._alt_az_combinations.T, names=["altitude", "azimuth"]
-            ),
-        )
-        temp = temp.agg(agg, axis=1)
-        temp = temp.unstack()
-
-        return temp
-
-    def direct_irradiance(
-        self, analysis_period: AnalysisPeriod = AnalysisPeriod(), agg: str = "sum"
-    ) -> pd.DataFrame:
-        """The direct irradiance for each combination of tilt and orientation, within the given analysis period.
-
-        Args:
-            analysis_period (AnalysisPeriod, optional):
-                The analysis period for which to calculate the total irradiance. Default is AnalysisPeriod().
-            agg (str, optional):
-                The aggregation method to use for the calculation. Default is "sum".
-        Returns:
-            pd.DataFrame:
-                A table of direct irradiance values for each simulated azimuth and tilt combo.
-        """
-        mask = analysis_period_to_boolean(analysis_period)
-        dts = analysis_period_to_datetimes(analysis_period)
-
-        temp = pd.DataFrame(
-            np.array(self._direct).T[mask].T,
-            columns=dts,
-            index=pd.MultiIndex.from_arrays(
-                self._alt_az_combinations.T, names=["altitude", "azimuth"]
-            ),
-        )
-        temp = temp.agg(agg, axis=1)
-        temp = temp.unstack()
-
-        return temp
-
-    def reflected_irradiance(
-        self, analysis_period: AnalysisPeriod = AnalysisPeriod(), agg: str = "sum"
-    ) -> pd.DataFrame:
-        """The reflected irradiance for each combination of tilt and orientation, within the given analysis period.
-
-        Args:
-            analysis_period (AnalysisPeriod, optional):
-                The analysis period for which to calculate the total irradiance. Default is AnalysisPeriod().
-            agg (str, optional):
-                The aggregation method to use for the calculation. Default is "sum".
-        Returns:
-            pd.DataFrame:
-                A table of reflected irradiance values for each simulated azimuth and tilt combo.
-        """
-        mask = analysis_period_to_boolean(analysis_period)
-        dts = analysis_period_to_datetimes(analysis_period)
-
-        temp = pd.DataFrame(
-            np.array(self._reflected).T[mask].T,
-            columns=dts,
-            index=pd.MultiIndex.from_arrays(
-                self._alt_az_combinations.T, names=["altitude", "azimuth"]
-            ),
-        )
-        temp = temp.agg(agg, axis=1)
-        temp = temp.unstack()
-
-        return temp
-
-    def _calculate_irradiance(self) -> Tuple[Tuple[Tuple[float]]]:
-        """Calculate the irradiance in W/m2 for each combination of tilt and orientation."""
-        results = []
-        pbar = tqdm(self._alt_az_combinations)
-        for _alt, _az in pbar:
-            pbar.set_description(
-                f"Calculating irradiance for (Altitude: {_alt:0.1f}°, Azimuth: {_az:0.1f}°)"
-            )
-            result = self.wea.directional_irradiance(
-                _alt, _az, self.ground_reflectance, self.isotropic
-            )
-            results.append([i.values for i in result])
-
-        return results
-
-    def _calculate_irradiance_threaded(self) -> Tuple[Tuple[Tuple[float]]]:
-        def task(alt_az):
-            _alt, _az = alt_az
-            result = self.wea.directional_irradiance(
-                _alt, _az, self.ground_reflectance, self.isotropic
-            )
-            return [i.values for i in result]
-
-        results = []
-        with ThreadPoolExecutor() as executor:
-            futures = [executor.submit(task, i) for i in self._alt_az_combinations]
-            for future in futures:
-                # retrieve the result
-                results.append(future.result())
-        return results
-
-    def plot_tof(
-        self,
-        ax: plt.Axes = None,
-        irradiance_type: IrradianceType = IrradianceType.TOTAL,
-        agg: str = "sum",
-        analysis_period: AnalysisPeriod = AnalysisPeriod(),
-        irradiance_unit: IrradianceUnit = IrradianceUnit.WH_M2,
-        **kwargs,
-    ) -> plt.Axes:
-        """Plot the total, direct, diffuse and reflected irradiance for each combination of tilt and orientation, within the given analysis period."""
-
-        if ax is None:
-            ax = plt.gca()
-
-        default_kwargs = {"cmap": "YlOrRd", "levels": 100}
-        kwargs = {**default_kwargs, **kwargs}
-
-        # prepare data for plotting
-        data = (
-            (
-                self.get_irradiance(
-                    irradiance_type=irradiance_type,
-                    agg=agg,
-                    analysis_period=analysis_period,
-                )
-                * irradiance_unit.multiplier
-            )
-            .unstack()
-            .reset_index()
-            .rename(columns={0: "vals"})
-        )
-        max_idx = data.sort_values("vals", ascending=False).index[0]
-        max_alt = data.altitude[max_idx]
-        max_az = data.azimuth[max_idx]
-        max_val = data.vals[max_idx]
-        if max_val == 0:
-            warnings.warn("No irradiance values found for the given analysis period.")
-
-        # set defaults for plot kwargs
-        title = kwargs.pop(
-            "title",
-            f"{location_to_string(self.wea.location)}\n{describe_analysis_period(analysis_period)} ({irradiance_type.to_string()}, {agg})",
-        )
-        quantiles = kwargs.pop("quantiles", [0.25, 0.5, 0.75, 0.95])
-
-        # populate plot
-        tcf = ax.tricontourf(
-            data.azimuth.values,
-            data.altitude.values,
-            data.vals.values,
-            extend="max",
-            **kwargs,
-        )
-        if max_val != 0:
-            quantile_vals = [max_val * i for i in quantiles]
-            tcl = ax.tricontour(
-                data.azimuth.values,
-                data.altitude.values,
-                data.vals.values / max_val,
-                levels=quantiles,
-                colors="k",
-                linestyles="--",
-                alpha=0.5,
-            )
-
-            def cl_fmt(x):
-                return f"{x:.0%} ({max_val * x:,.0f})"
-
-            _ = ax.clabel(tcl, fmt=cl_fmt)
-
-            # maximium pt
-            ax.scatter(max_az, max_alt, c="k", s=10, marker="x")
-            alt_offset = (90 / 100) * 0.5 if max_alt <= 45 else -(90 / 100) * 0.5
-            az_offset = (360 / 100) * 0.5 if max_az <= 180 else -(360 / 100) * 0.5
-            ha = "left" if max_az <= 180 else "right"
-            va = "bottom" if max_alt <= 45 else "top"
-            ax.text(
-                max_az + az_offset,
-                max_alt + alt_offset,
-                f"{max_val:,.0f}{irradiance_unit.to_string()}\n({max_az:0.0f}°, {max_alt:0.0f}°)",
-                ha=ha,
-                va=va,
-                c="k",
-                weight="bold",
-                size="small",
-            )
-            ax.axvline(max_az, 0, max_alt / 90, c="w", ls=":", lw=0.5, alpha=0.25)
-            ax.axhline(max_alt, 0, max_az / 360, c="w", ls=":", lw=0.5, alpha=0.25)
-
-        # format plot
-        for spine in ["top", "right"]:
-            ax.spines[spine].set_visible(False)
-        ax.xaxis.set_major_locator(mticker.MultipleLocator(base=30))
-        ax.yaxis.set_major_locator(mticker.MultipleLocator(base=10))
-
-        def cb_fmt(x, pos):
-            return f"{x:,.0f}"
-
-        cb = plt.colorbar(
-            tcf,
-            ax=ax,
-            orientation="vertical",
-            drawedges=False,
-            fraction=0.05,
-            aspect=25,
-            pad=0.02,
-            label=irradiance_unit.to_string(),
-            format=mticker.FuncFormatter(cb_fmt),
-        )
-        cb.outline.set_visible(False)
-        cb.locator = mticker.MaxNLocator(nbins=10, prune=None)
-        if max_val != 0:
-            for quantile_val in quantile_vals:
-                cb.ax.plot([0, 1], [quantile_val] * 2, "k", ls="--", alpha=0.5)
-
-        # add decorators
-        ax.set_title(title)
-        ax.set_xlabel("Panel orientation (clockwise from North at 0°)")
-        ax.set_ylabel("Panel tilt (0° facing the horizon, 90° facing the sky)")
-        plt.tight_layout()
-
-        return ax
-
-    @classmethod
-    def from_epw(
-        cls, epw: EPW, n_altitudes: int = 10, n_azimuths: int = 19
-    ) -> PVYieldMatrix:
-        """Create this object from an EPW file.
-
-        Args:
-            epw (EPW):
-                The EPW file to use (as a ladybug object).
-            n_altitudes (int, optional):
-                The number of altitudes to use. Defaults to 10.
-            n_azimuths (int, optional):
-                The number of azimuths to use. Defaults to 19.
-
-        Returns:
-            PVYieldMatrix:
-                The PVYieldMatrix object.
-        """
-        return cls(
-            wea=Wea.from_epw_file(epw.file_path),
-            n_altitudes=n_altitudes,
-            n_azimuths=n_azimuths,
-        )
-
-    @classmethod
-    def from_epw_file(cls, epw_file: str, n_altitudes: int = 10, n_azimuths: int = 19):
-        """Create this object from an EPW file.
-
-        Args:
-            epw_file (str):
-                The EPW file to use (as a path).
-            n_altitudes (int, optional):
-                The number of altitudes to use. Defaults to 10.
-            n_azimuths (int, optional):
-                The number of azimuths to use. Defaults to 19.
-
-        Returns:
-            PVYieldMatrix:
-                The PVYieldMatrix object.
-        """
-        return cls(
-            wea=Wea.from_epw_file(epw_file),
-            n_altitudes=n_altitudes,
-            n_azimuths=n_azimuths,
-        )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_geometry_extension/__init__.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_geometry_extension/__init__.py
new file mode 100644
index 00000000..cfbaa226
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_geometry_extension/__init__.py
@@ -0,0 +1,58 @@
+"""Methods for handling Ladybug geometry."""
+from warnings import warn  # pylint: disable=E0401
+
+from ladybug_geometry.bounding import bounding_rectangle
+from ladybug_geometry.geometry3d import Plane, Point3D, Vector3D, Mesh3D
+from ladybug_geometry.geometry2d import Mesh2D, Point2D, Polygon2D
+from scipy.spatial.distance import cdist
+
+
+def mesh3d_isplanar(mesh: Mesh3D) -> bool:
+    """Check if a mesh is planar.
+
+    Args:
+        mesh (Mesh3D): A ladybug-geometry Mesh3D.
+
+    Returns:
+        bool: True if the mesh is planar, False otherwise.
+    """
+
+    return len(set(mesh.vertex_normals)) == 1
+
+
+def mesh3d_get_plane(mesh: Mesh3D) -> Plane:
+    """Estimate the plane of a mesh.
+
+    Args:
+        mesh (Mesh3D): A ladybug-geometry Mesh3D.
+
+    Returns:
+        Plane: The estimated plane of the mesh.
+    """
+
+    if not mesh3d_isplanar(mesh=mesh):
+        warn(
+            "The mesh given is not planar. This method will return a planar mesh "
+            "based on a selection of 3-points from the first 3-faces of this mesh."
+        )
+
+    plane = Plane.from_three_points(
+        *[mesh.vertices[j] for j in [i[0] for i in mesh.faces[:3]]]
+    )
+
+    if plane.n.z < 0:
+        warn(
+            "The plane normal is pointing downwards. This method will return a plane with a normal pointing upwards."
+        )
+        return plane.flip()
+
+    return plane
+
+
+def pt_distances(base_point: Point2D, points: list[Point2D]) -> list[float]:
+    """Return the distance from each moisture_source pt to each other point in the input list"""
+
+    # for each emitter, get the distance to all "receiving" points
+    distances = cdist([base_point], points)
+
+    return distances
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/__init__.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/__init__.py
index 50664ac7..d741f20d 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/__init__.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/__init__.py
@@ -1,27 +1,27 @@
-from ._condensation_potential import condensation_potential
-from ._degree_days import cooling_degree_days, degree_days, heating_degree_days
-from ._diurnal import diurnal
-from ._heatmap import heatmap
-from ._monthly_histogram_proportion import monthly_histogram_proportion
-from ._psychrometric import (
-    PassiveStrategy,
-    PassiveStrategyParameters,
-    PsychrometricPolygons,
-    psychrometric,
-)
-from ._sunpath import sunpath
-from ._timeseries import timeseries
-from ._utci import (
-    utci_comfort_band_comparison,
-    utci_comparison_diurnal_day,
-    utci_day_comfort_metrics,
-    utci_distance_to_comfortable,
-    utci_heatmap,
-    utci_heatmap_difference,
-    utci_heatmap_histogram,
-    utci_histogram,
-    utci_journey,
-    utci_monthly_histogram,
-    utci_pie,
-)
-from ._wind import wind_matrix, windrose
+# from ._condensation_potential import condensation_potential
+# from ._degree_days import cooling_degree_days, degree_days, heating_degree_days
+# from ._diurnal import diurnal
+# from ._heatmap import heatmap
+# from ._monthly_histogram_proportion import monthly_histogram_proportion
+# from ._psychrometric import (
+#     PassiveStrategy,
+#     PassiveStrategyParameters,
+#     PsychrometricPolygons,
+#     psychrometric,
+# )
+# from ._sunpath import sunpath
+# from ._timeseries import timeseries
+# from ._utci import (
+#     utci_comfort_band_comparison,
+#     utci_comparison_diurnal_day,
+#     utci_day_comfort_metrics,
+#     utci_distance_to_comfortable,
+#     utci_heatmap,
+#     utci_heatmap_difference,
+#     utci_heatmap_histogram,
+#     utci_histogram,
+#     utci_journey,
+#     utci_monthly_histogram,
+#     utci_pie,
+# )
+# from ._wind import wind_matrix, windrose
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/__utci_prototypes.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/__utci_prototypes.py
index 3a7b1003..7cc2319c 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/__utci_prototypes.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/__utci_prototypes.py
@@ -1,668 +1,682 @@
-import calendar
-import textwrap
-import warnings
-from calendar import month_abbr
-from datetime import timedelta
-from typing import Tuple, Union
-
-import matplotlib.dates as mdates
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import matplotlib.ticker as mticker
-import numpy as np
-import pandas as pd
-from ladybug.datacollection import AnalysisPeriod, HourlyContinuousCollection
-from ladybug.datatype.temperature import UniversalThermalClimateIndex
-from ladybug.epw import EPW
-from ladybug.sunpath import Sunpath
-from matplotlib.colorbar import ColorbarBase
-from matplotlib.colors import BoundaryNorm, ListedColormap
-from matplotlib.figure import Figure
-from matplotlib.gridspec import GridSpec
-
-from ..external_comfort.utci import (
-    UTCICategories,
-    feasible_comfort_category,
-    utci_comfort_categories,
-)
-from ..ladybug_extension.analysis_period import describe_analysis_period as describe_ap
-from ..ladybug_extension.datacollection import collection_to_series
-from ..ladybug_extension.location import location_to_string
-from ._heatmap import heatmap
-
-
-def utci_shade_benefit(
-    utci_shade_benefit_categories: pd.Series, **kwargs
-) -> plt.Figure:
-    """Plot the shade benefit category.
-
-    Args:
-        utci_shade_benefit_categories (pd.Series):
-            A series containing shade benefit categories.
-        **kwargs:
-            Optional arguments to pass to the plot. These can include:
-                - title (str, optional):
-                    A title to add to the plot. Defaults to None.
-                - epw (EPW, optional):
-                    If included, plot the sun up hours. Defaults to None.
-
-    Returns:
-        plt.Figure:
-            A figure object.
-    """
-
-    warnings.warn(
-        "This method is mostly broken, though it *nearly* works. Included here just to inspire someone to fix it! Please."
-    )
-
-    if not isinstance(utci_shade_benefit_categories, pd.Series):
-        raise ValueError(
-            f"shade_benefit_categories must be of type pd.Series, it is currently {type(utci_shade_benefit_categories)}"
-        )
-
-    epw = kwargs.get("epw", None)
-    title = kwargs.get("title", None)
-
-    if epw is not None:
-        if not isinstance(epw, EPW):
-            raise ValueError(
-                f"include_sun must be of type EPW, it is currently {type(epw)}"
-            )
-        if len(epw.dry_bulb_temperature) != len(utci_shade_benefit_categories):
-            raise ValueError(
-                f"Input sizes do not match ({len(utci_shade_benefit_categories)} != {len(epw.dry_bulb_temperature)})"
-            )
-
-    # convert values into categories
-    cat = pd.Categorical(utci_shade_benefit_categories)
-
-    # get numeric values
-    numeric = pd.Series(cat.codes, index=utci_shade_benefit_categories.index)
-
-    # create colormap
-    colors = ["#00A499", "#5D822D", "#EE7837", "#585253"]
-    if len(colors) != len(cat.categories):
-        raise ValueError(
-            f"The number of categories does not match the number of colours in the colormap ({len(colors)} != {len(cat.categories)})."
-        )
-    cmap = ListedColormap(colors)
-
-    # create tcf_properties
-    imshow_properties = {
-        "cmap": cmap,
-    }
-
-    # create canvas
-    fig = plt.figure(constrained_layout=True)
-    spec = fig.add_gridspec(
-        ncols=1, nrows=3, width_ratios=[1], height_ratios=[4, 2, 0.5], hspace=0.0
-    )
-    heatmap_ax = fig.add_subplot(spec[0, 0])
-    histogram_ax = fig.add_subplot(spec[1, 0])
-    cb_ax = fig.add_subplot(spec[2, 0])
-
-    # Add heatmap
-    hmap = heatmap(numeric, ax=heatmap_ax, show_colorbar=False, **imshow_properties)
-
-    # add sun up indicator lines
-    if epw is not None:
-        sp = Sunpath.from_location(epw.location)
-        sun_up_down = pd.DataFrame(
-            [
-                sp.calculate_sunrise_sunset_from_datetime(i)
-                for i in utci_shade_benefit_categories.resample("D").count().index
-            ]
-        ).reset_index(drop=True)
-        sun_up_down.index = sun_up_down.index + mdates.date2num(numeric.index.min())
-        sunrise = pd.Series(
-            data=[
-                726449
-                + timedelta(hours=i.hour, minutes=i.minute, seconds=i.second).seconds
-                / 86400
-                for i in sun_up_down.sunrise
-            ],
-            index=sun_up_down.index,
-        )
-        sunrise = sunrise.reindex(
-            sunrise.index.tolist() + [sunrise.index[-1] + 1]
-        ).ffill()
-        sunset = pd.Series(
-            data=[
-                726449
-                + timedelta(hours=i.hour, minutes=i.minute, seconds=i.second).seconds
-                / 86400
-                for i in sun_up_down.sunset
-            ],
-            index=sun_up_down.index,
-        )
-        sunset = sunset.reindex(sunset.index.tolist() + [sunset.index[-1] + 1]).ffill()
-        for s in [sunrise, sunset]:
-            heatmap_ax.plot(s.index, s.values, zorder=9, c="#F0AC1B", lw=1)
-
-    # Add colorbar legend and text descriptors for comfort bands
-    ticks = np.linspace(0, len(cat.categories), (len(cat.categories) * 2) + 1)[1::2]
-    # cb = fig.colorbar(
-    #     hmap,
-    #     ax=heatmap_ax,
-    #     cax=cb_ax,
-    #     orientation="horizontal",
-    #     ticks=ticks,
-    #     drawedges=False,
-    # )
-    # cb.outline.set_visible(False)
-    # plt.setp(plt.getp(cb_ax, "xticklabels"), color="none")
-    # cb.set_ticks([])
-
-    # Add labels to the colorbar
-    tick_locs = np.linspace(0, len(cat.categories) - 1, len(cat.categories) + 1)
-    tick_locs = (tick_locs[1:] + tick_locs[:-1]) / 2
-    category_percentages = (
-        utci_shade_benefit_categories.value_counts()
-        / utci_shade_benefit_categories.count()
-    )
-    for n, (tick_loc, category) in enumerate(zip(*[tick_locs, cat.categories])):
-        cb_ax.text(
-            tick_loc,
-            1.05,
-            textwrap.fill(category, 15) + f"\n{category_percentages[n]:0.0%}",
-            ha="center",
-            va="bottom",
-            size="small",
-        )
-
-    # Add stacked plot
-    t = utci_shade_benefit_categories
-    t = t.groupby([t.index.month, t]).count().unstack().T
-    t = t / t.sum()
-    months = [calendar.month_abbr[i] for i in range(1, 13, 1)]
-    t.T.plot.bar(
-        ax=histogram_ax,
-        stacked=True,
-        color=colors,
-        legend=False,
-        width=1,
-    )
-    histogram_ax.set_xlabel(None)
-    histogram_ax.set_xlim(-0.5, 11.5)
-    histogram_ax.set_ylim(0, 1)
-    histogram_ax.set_xticklabels(months, ha="center", rotation=0)
-    for spine in ["top", "right"]:
-        histogram_ax.spines[spine].set_visible(False)
-    histogram_ax.yaxis.set_major_formatter(mticker.PercentFormatter(1))
-
-    # # Add header percentages for bar plot
-    for month, row in (
-        (
-            utci_shade_benefit_categories.groupby(
-                utci_shade_benefit_categories.index.month
-            )
-            .value_counts()
-            .unstack()
-            .T
-            / utci_shade_benefit_categories.groupby(
-                utci_shade_benefit_categories.index.month
-            ).count()
-        )
-        .T.fillna(0)
-        .iterrows()
-    ):
-        txt = ""
-        for n, val in enumerate(row.values[::-1]):
-            txtx = f"{val:0.0%}{txt}"
-            histogram_ax.text(
-                month - 1,
-                1.02,
-                txtx,
-                va="bottom",
-                ha="center",
-                color=colors[::-1][n],
-                fontsize="small",
-            )
-            txt += "\n"
-
-    title_base = "Shade benefit"
-    if title is None:
-        heatmap_ax.set_title(title_base, y=1, ha="left", va="bottom", x=0)
-    else:
-        heatmap_ax.set_title(
-            f"{title_base}\n{title}",
-            y=1,
-            ha="left",
-            va="bottom",
-            x=0,
-        )
-
-    return fig
-
-
-def utci_feasibility(
-    epw: EPW,
-    simplified: bool = False,
-    comfort_limits: tuple = (9, 26),
-    included_additional_moisture: bool = False,
-    analysis_periods: Union[AnalysisPeriod, Tuple[AnalysisPeriod]] = (AnalysisPeriod()),
-    met_rate_adjustment: float = None,
-) -> Figure:
-    """Plot the UTCI feasibility for each month of the year.
-
-    Args:
-        epw (EPW):
-            An EPW object.
-        simplified (bool, optional):
-            Default is False.
-        comfort_limits (tuple, optional):
-            Default is (9, 26). Only used if simplified is True.
-        included_additional_moisture (bool, optional):
-            Default is False. If True, then include evap cooling in this analysis.
-        analysis_periods (Union[AnalysisPeriod, Tuple[AnalysisPeriod]], optional):
-            An AnalysisPeriod or a tuple of AnalysisPeriods to be used for the analysis.
-            Defaults to (AnalysisPeriod(),).
-        met_rate_adjustment (float, optional):
-            A value to be added to the metabolic rate of the UTCI model. This can be used
-            to account for changes in metabolic rate due to clothing, exercise, etc.
-            Defaults to None.
-    Returns:
-        Figure:
-            A matplotlib Figure object.
-    """
-
-    df = feasible_comfort_category(
-        epw,
-        simplified=simplified,
-        comfort_limits=comfort_limits,
-        include_additional_moisture=included_additional_moisture,
-        analysis_periods=analysis_periods,
-        met_rate_adjustment_value=met_rate_adjustment,
-    )
-
-    labels, _ = utci_comfort_categories(
-        simplified=simplified,
-        comfort_limits=comfort_limits,
-        rtype="category",
-    )
-    colors, _ = utci_comfort_categories(
-        simplified=simplified,
-        comfort_limits=comfort_limits,
-        rtype="color",
-    )
-
-    fig, axes = plt.subplots(1, 12, figsize=(10, 4), sharey=True, sharex=False)
-
-    ypos = range(len(df))
-    for n, ax in enumerate(axes):
-        # get values
-        low = df.iloc[n].filter(regex="lowest")
-        high = df.iloc[n].filter(regex="highest")
-        ypos = range(len(low))
-
-        ax.barh(
-            ypos,
-            width=high.values - low.values,
-            left=low.values,
-            color=colors,
-            zorder=3,
-            alpha=0.8,
-        )
-
-        for rect in ax.patches:
-            width = rect.get_width()
-            height = rect.get_height()
-            _x = rect.get_x()
-            _y = rect.get_y()
-            if width == 0:
-                if _x == 1:
-                    # text saying 100% of hours are in this category
-                    ax.text(
-                        0.5,
-                        _y + (height / 2),
-                        textwrap.fill("All times", 15),
-                        ha="center",
-                        va="center",
-                        rotation=0,
-                        fontsize="xx-small",
-                        zorder=3,
-                    )
-                continue
-
-            ax.text(
-                _x - 0.03,
-                _y + (height),
-                f"{_x:0.1%}",
-                ha="right",
-                va="top",
-                rotation=90,
-                fontsize="xx-small",
-                zorder=3,
-            )
-            ax.text(
-                _x + width + 0.03,
-                _y + (height),
-                f"{_x + width:0.1%}",
-                ha="left",
-                va="top",
-                rotation=90,
-                fontsize="xx-small",
-                zorder=3,
-            )
-
-        if simplified:
-            for nn, i in enumerate(colors):
-                ax.axhspan(ymin=nn - 0.5, ymax=nn + 0.5, fc=i, alpha=0.2, zorder=1)
-        else:
-            for nn, i in enumerate(UTCICategories):
-                ax.axhspan(
-                    ymin=nn - 0.5, ymax=nn + 0.5, fc=i.color, alpha=0.2, zorder=1
-                )
-
-        ax.set_xlim(-0.1, 1.1)
-        ax.set_ylim(-0.5, len(ypos) - 0.5)
-        for spine in ["left", "bottom"]:
-            ax.spines[spine].set_visible(False)
-        ax.tick_params(labelleft=False, left=False)
-        ax.set_xticks([-0.1, 0.5, 1.1])
-        ax.set_xticklabels(["", month_abbr[n + 1], ""])
-        ax.grid(False)
-
-        if n == 5:
-            handles = []
-            if simplified:
-                for col, lab in list(zip(*[["#3C65AF", "#2EB349", "#C31F25"], labels])):
-                    handles.append(mpatches.Patch(color=col, label=lab, alpha=0.3))
-            else:
-                for i in UTCICategories:
-                    handles.append(
-                        mpatches.Patch(color=i.color, label=i.value, alpha=0.3)
-                    )
-
-            ax.legend(
-                handles=handles,
-                bbox_to_anchor=(0.5, -0.1),
-                loc="upper center",
-                ncol=3 if simplified else 4,
-                borderaxespad=0,
-                frameon=False,
-            )
-
-        ti = f"{location_to_string(epw.location)}\nFeasible ranges of UTCI temperatures ({describe_ap(analysis_periods)})"
-        if met_rate_adjustment:
-            ti += f" with MET rate adjustment to {met_rate_adjustment} MET"
-        plt.suptitle(
-            textwrap.fill(ti, 90),
-            x=0.075,
-            y=0.9,
-            ha="left",
-            va="bottom",
-        )
-
-    plt.tight_layout()
-    return fig
-
-
-def utci_distance_to_comfortable(
-    collection: HourlyContinuousCollection,
-    title: str = None,
-    comfort_thresholds: Tuple[float] = (9, 26),
-    low_limit: float = 15,
-    high_limit: float = 25,
-) -> Figure:
-    """Plot the distance (in C) to comfortable for a given Ladybug HourlyContinuousCollection
-        containing UTCI values.
-
-    Args:
-        collection (HourlyContinuousCollection):
-            A Ladybug Universal Thermal Climate Index HourlyContinuousCollection object.
-        title (str, optional):
-            A title to place at the top of the plot. Defaults to None.
-        comfort_thresholds (List[float], optional):
-            The comfortable band of UTCI temperatures. Defaults to [9, 26].
-        low_limit (float, optional):
-            The distance from the lower edge of the comfort threshold to include in the "too cold"
-            part of the heatmap. Defaults to 15.
-        high_limit (float, optional):
-            The distance from the upper edge of the comfort threshold to include in the "too hot"
-            part of the heatmap. Defaults to 25.
-    Returns:
-        Figure:
-            A matplotlib Figure object.
-    """
-
-    if not isinstance(collection.header.data_type, UniversalThermalClimateIndex):
-        raise ValueError("This method only works for UTCI data.")
-
-    if not len(comfort_thresholds) == 2:
-        raise ValueError("comfort_thresholds must be a list of length 2.")
-
-    # Create matrices containing the above/below/within UTCI distances to comfortable
-    series = collection_to_series(collection)
-
-    low, high = comfort_thresholds
-    midpoint = np.mean([low, high])
-
-    distance_above_comfortable = (series[series > high] - high).to_frame()
-    distance_above_comfortable_matrix = (
-        distance_above_comfortable.set_index(
-            [
-                distance_above_comfortable.index.dayofyear,
-                distance_above_comfortable.index.hour,
-            ]
-        )["Universal Thermal Climate Index (C)"]
-        .astype(np.float64)
-        .unstack()
-        .T.reindex(range(24), axis=0)
-        .reindex(range(365), axis=1)
-    )
-
-    distance_below_comfortable = (low - series[series < low]).to_frame()
-    distance_below_comfortable_matrix = (
-        distance_below_comfortable.set_index(
-            [
-                distance_below_comfortable.index.dayofyear,
-                distance_below_comfortable.index.hour,
-            ]
-        )["Universal Thermal Climate Index (C)"]
-        .astype(np.float64)
-        .unstack()
-        .T.reindex(range(24), axis=0)
-        .reindex(range(365), axis=1)
-    )
-
-    distance_below_midpoint = (
-        midpoint - series[(series >= low) & (series <= midpoint)]
-    ).to_frame()
-    distance_below_midpoint_matrix = (
-        distance_below_midpoint.set_index(
-            [
-                distance_below_midpoint.index.dayofyear,
-                distance_below_midpoint.index.hour,
-            ]
-        )["Universal Thermal Climate Index (C)"]
-        .astype(np.float64)
-        .unstack()
-        .T.reindex(range(24), axis=0)
-        .reindex(range(365), axis=1)
-    )
-
-    distance_above_midpoint = (
-        series[(series <= high) & (series > midpoint)] - midpoint
-    ).to_frame()
-    distance_above_midpoint_matrix = (
-        distance_above_midpoint.set_index(
-            [
-                distance_above_midpoint.index.dayofyear,
-                distance_above_midpoint.index.hour,
-            ]
-        )["Universal Thermal Climate Index (C)"]
-        .astype(np.float64)
-        .unstack()
-        .T.reindex(range(24), axis=0)
-        .reindex(range(365), axis=1)
-    )
-
-    distance_above_comfortable_cmap = plt.get_cmap("YlOrRd")  # Reds
-    distance_above_comfortable_lims = [0, high_limit]
-    distance_above_comfortable_norm = BoundaryNorm(
-        np.linspace(
-            distance_above_comfortable_lims[0], distance_above_comfortable_lims[1], 100
-        ),
-        ncolors=distance_above_comfortable_cmap.N,
-        clip=True,
-    )
-
-    distance_below_comfortable_cmap = plt.get_cmap("YlGnBu")  # Blues
-    distance_below_comfortable_lims = [0, low_limit]
-    distance_below_comfortable_norm = BoundaryNorm(
-        np.linspace(
-            distance_below_comfortable_lims[0], distance_below_comfortable_lims[1], 100
-        ),
-        ncolors=distance_below_comfortable_cmap.N,
-        clip=True,
-    )
-
-    distance_below_midpoint_cmap = plt.get_cmap("YlGn_r")  # Greens_r
-    distance_below_midpoint_lims = [0, midpoint - low]
-    distance_below_midpoint_norm = BoundaryNorm(
-        np.linspace(
-            distance_below_midpoint_lims[0], distance_below_midpoint_lims[1], 100
-        ),
-        ncolors=distance_below_midpoint_cmap.N,
-        clip=True,
-    )
-
-    distance_above_midpoint_cmap = plt.get_cmap("YlGn_r")  # Greens_r
-    distance_above_midpoint_lims = [0, high - midpoint]
-    distance_above_midpoint_norm = BoundaryNorm(
-        np.linspace(
-            distance_above_midpoint_lims[0], distance_above_midpoint_lims[1], 100
-        ),
-        ncolors=distance_above_midpoint_cmap.N,
-        clip=True,
-    )
-
-    extent = [
-        mdates.date2num(series.index.min()),
-        mdates.date2num(series.index.max()),
-        726449,
-        726450,
-    ]
-
-    fig = plt.figure(constrained_layout=False, figsize=(15, 5))
-    gs = GridSpec(2, 3, figure=fig, width_ratios=[1, 1, 1], height_ratios=[20, 1])
-    hmap_ax = fig.add_subplot(gs[0, :])
-    cb_low_ax = fig.add_subplot(gs[1, 0])
-    cb_mid_ax = fig.add_subplot(gs[1, 1])
-    cb_high_ax = fig.add_subplot(gs[1, 2])
-
-    hmap_ax.imshow(
-        np.ma.array(
-            distance_below_comfortable_matrix,
-            mask=np.isnan(distance_below_comfortable_matrix),
-        ),
-        extent=extent,
-        aspect="auto",
-        cmap=distance_below_comfortable_cmap,
-        norm=distance_below_comfortable_norm,
-        interpolation="none",
-    )
-    hmap_ax.imshow(
-        np.ma.array(
-            distance_below_midpoint_matrix,
-            mask=np.isnan(distance_below_midpoint_matrix),
-        ),
-        extent=extent,
-        aspect="auto",
-        cmap=distance_below_midpoint_cmap,
-        norm=distance_below_midpoint_norm,
-        interpolation="none",
-    )
-    hmap_ax.imshow(
-        np.ma.array(
-            distance_above_comfortable_matrix,
-            mask=np.isnan(distance_above_comfortable_matrix),
-        ),
-        extent=extent,
-        aspect="auto",
-        cmap=distance_above_comfortable_cmap,
-        norm=distance_above_comfortable_norm,
-        interpolation="none",
-    )
-    hmap_ax.imshow(
-        np.ma.array(
-            distance_above_midpoint_matrix,
-            mask=np.isnan(distance_above_midpoint_matrix),
-        ),
-        extent=extent,
-        aspect="auto",
-        cmap=distance_above_midpoint_cmap,
-        norm=distance_above_midpoint_norm,
-        interpolation="none",
-    )
-
-    # Axis formatting
-    hmap_ax.invert_yaxis()
-    hmap_ax.xaxis_date()
-    hmap_ax.xaxis.set_major_formatter(mdates.DateFormatter("%b"))
-    hmap_ax.yaxis_date()
-    hmap_ax.yaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
-    hmap_ax.tick_params(labelleft=True, labelright=True, labelbottom=True)
-    plt.setp(hmap_ax.get_xticklabels(), ha="left", color="k")
-    plt.setp(hmap_ax.get_yticklabels(), color="k")
-
-    # Spine formatting
-    for spine in ["top", "bottom", "left", "right"]:
-        hmap_ax.spines[spine].set_visible(False)
-
-    # Grid formatting
-    hmap_ax.grid(visible=True, which="major", color="white", linestyle=":", alpha=1)
-
-    # Colorbars
-    low_cb = ColorbarBase(
-        cb_low_ax,
-        cmap=distance_below_comfortable_cmap,
-        orientation="horizontal",
-        norm=distance_below_comfortable_norm,
-        label='Degrees below "comfortable"',
-        extend="max",
-    )
-    low_cb.outline.set_visible(False)
-    cb_low_ax.xaxis.set_major_locator(mticker.MaxNLocator(5))
-
-    mid_cb = ColorbarBase(
-        cb_mid_ax,
-        cmap=distance_below_midpoint_cmap,
-        orientation="horizontal",
-        norm=distance_below_midpoint_norm,
-        label='Degrees about "comfortable"',
-        extend="neither",
-    )
-    mid_cb.outline.set_visible(False)
-    cb_mid_ax.xaxis.set_major_locator(mticker.MaxNLocator(5))
-
-    high_cb = ColorbarBase(
-        cb_high_ax,
-        cmap=distance_above_comfortable_cmap,
-        orientation="horizontal",
-        norm=distance_above_comfortable_norm,
-        label='Degrees above "comfortable"',
-        extend="max",
-    )
-    high_cb.outline.set_visible(False)
-    cb_high_ax.xaxis.set_major_locator(mticker.MaxNLocator(5))
-
-    if title is None:
-        hmap_ax.set_title(
-            'Distance to "comfortable"', color="k", y=1, ha="left", va="bottom", x=0
-        )
-    else:
-        hmap_ax.set_title(
-            f"Distance to comfortable - {title}",
-            color="k",
-            y=1,
-            ha="left",
-            va="bottom",
-            x=0,
-        )
-
-    # Tidy plot
-    plt.tight_layout()
-
-    return fig
+"""Protoype UTCI plotting methods."""
+# pylint: disable=line-too-long
+# # pylint: disable=E0401
+# import calendar
+# import textwrap
+# import warnings
+# from calendar import month_abbr
+# from datetime import timedelta
+
+# # pylint: enable=E0401
+
+# import matplotlib.dates as mdates
+# import matplotlib.patches as mpatches
+# import matplotlib.pyplot as plt
+# import matplotlib.ticker as mticker
+# import numpy as np
+# import pandas as pd
+# from ladybug.datacollection import HourlyContinuousCollection
+# from ladybug.datatype.temperature import UniversalThermalClimateIndex
+# from ladybug.epw import EPW
+# from ladybug.sunpath import Sunpath
+# from matplotlib.colorbar import ColorbarBase
+# from matplotlib.colors import BoundaryNorm, ListedColormap
+# from matplotlib.figure import Figure
+# from matplotlib.gridspec import GridSpec
+# from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+# from ..categorical.categories import Categorical
+
+# # from ..external_comfort.utci import (
+# #     UTCICategories,
+# #     feasible_comfort_category,
+# #     utci_comfort_categories,
+# # )
+# from ..external_comfort.utci import shade_benefit_category
+# from ..helpers import sunrise_sunset
+# from ..ladybug_extension.analysisperiod import describe_analysis_period
+# from ..ladybug_extension.datacollection import collection_to_series
+# from ._heatmap import heatmap
+
+
+# def utci_shade_benefit(
+#     unshaded_utci: HourlyContinuousCollection | pd.Series,
+#     shaded_utci: HourlyContinuousCollection | pd.Series,
+#     comfort_limits: tuple[float] = (9, 26),
+#     **kwargs,
+# ) -> plt.Figure:
+#     """Plot the shade benefit category.
+
+#     Args:
+#         unshaded_utci (HourlyContinuousCollection | pd.Series):
+#             A dataset containing unshaded UTCI values.
+#         shaded_utci (HourlyContinuousCollection | pd.Series):
+#             A dataset containing shaded UTCI values.
+#         comfort_limits (tuple[float], optional):
+#             The range within which "comfort" is achieved. Defaults to (9, 26).
+
+#     Keyword Args:
+#         - title (str, optional):
+#             A title to add to the plot. Defaults to None.
+#         - epw (EPW, optional):
+#             If included, plot the sun up hours. Defaults to None.
+
+#     Returns:
+#         plt.Figure:
+#             A figure object.
+#     """
+
+#     warnings.warn(
+#         "This method is mostly broken, though it *nearly* works. Included here just to inspire someone to fix it! Please."
+#     )
+
+#     utci_shade_benefit_categories = shade_benefit_category(
+#         unshaded_utci,
+#         shaded_utci,
+#         comfort_limits=comfort_limits,
+#     )
+
+#     epw = kwargs.get("epw", None)
+#     title = kwargs.get("title", None)
+
+#     if epw is not None:
+#         if not isinstance(epw, EPW):
+#             raise ValueError(f"epw must be of type EPW, it is currently {type(epw)}")
+#         if len(epw.dry_bulb_temperature) != len(utci_shade_benefit_categories):
+#             raise ValueError(
+#                 f"Input sizes do not match ({len(utci_shade_benefit_categories)} != {len(epw.dry_bulb_temperature)})"
+#             )
+
+#     # convert values into categories
+#     cat = pd.Categorical(utci_shade_benefit_categories)
+
+#     # get numeric values
+#     numeric = pd.Series(cat.codes, index=utci_shade_benefit_categories.index)
+
+#     # create colormap
+#     colors = ["#00A499", "#5D822D", "#EE7837", "#585253"]
+#     if len(colors) != len(cat.categories):
+#         raise ValueError(
+#             f"The number of categories does not match the number of colours in the colormap ({len(colors)} != {len(cat.categories)})."
+#         )
+#     cmap = ListedColormap(colors)
+
+#     # create tcf_properties
+#     imshow_properties = {
+#         "cmap": cmap,
+#     }
+
+#     # create canvas
+#     fig = plt.figure(constrained_layout=True)
+#     spec = fig.add_gridspec(
+#         ncols=1, nrows=3, width_ratios=[1], height_ratios=[4, 2, 0.5], hspace=0.0
+#     )
+#     heatmap_ax = fig.add_subplot(spec[0, 0])
+#     histogram_ax = fig.add_subplot(spec[1, 0])
+#     cb_ax = fig.add_subplot(spec[2, 0])
+
+#     # Add heatmap
+#     hmap = heatmap(numeric, ax=heatmap_ax, show_colorbar=False, **imshow_properties)
+
+#     # add sun up indicator lines
+#     if epw is not None:
+#         sp = Sunpath.from_location(epw.location)
+#         sun_up_down = pd.DataFrame(
+#             [
+#                 sp.calculate_sunrise_sunset_from_datetime(i)
+#                 for i in utci_shade_benefit_categories.resample("D").count().index
+#             ]
+#         ).reset_index(drop=True)
+#         sun_up_down.index = sun_up_down.index + mdates.date2num(numeric.index.min())
+#         sunrise = pd.Series(
+#             data=[
+#                 726449
+#                 + timedelta(hours=i.hour, minutes=i.minute, seconds=i.second).seconds
+#                 / 86400
+#                 for i in sun_up_down.sunrise
+#             ],
+#             index=sun_up_down.index,
+#         )
+#         sunrise = sunrise.reindex(
+#             sunrise.index.tolist() + [sunrise.index[-1] + 1]
+#         ).ffill()
+#         sunset = pd.Series(
+#             data=[
+#                 726449
+#                 + timedelta(hours=i.hour, minutes=i.minute, seconds=i.second).seconds
+#                 / 86400
+#                 for i in sun_up_down.sunset
+#             ],
+#             index=sun_up_down.index,
+#         )
+#         sunset = sunset.reindex(sunset.index.tolist() + [sunset.index[-1] + 1]).ffill()
+#         for s in [sunrise, sunset]:
+#             heatmap_ax.plot(s.index, s.values, zorder=9, c="#F0AC1B", lw=1)
+
+#     # Add colorbar legend and text descriptors for comfort bands
+#     ticks = np.linspace(0, len(cat.categories), (len(cat.categories) * 2) + 1)[1::2]
+#     # cb = fig.colorbar(
+#     #     hmap,
+#     #     ax=heatmap_ax,
+#     #     cax=cb_ax,
+#     #     orientation="horizontal",
+#     #     ticks=ticks,
+#     #     drawedges=False,
+#     # )
+#     # cb.outline.set_visible(False)
+#     # plt.setp(plt.getp(cb_ax, "xticklabels"), color="none")
+#     # cb.set_ticks([])
+
+#     # Add labels to the colorbar
+#     tick_locs = np.linspace(0, len(cat.categories) - 1, len(cat.categories) + 1)
+#     tick_locs = (tick_locs[1:] + tick_locs[:-1]) / 2
+#     category_percentages = (
+#         utci_shade_benefit_categories.value_counts()
+#         / utci_shade_benefit_categories.count()
+#     )
+#     for n, (tick_loc, category) in enumerate(zip(*[tick_locs, cat.categories])):
+#         cb_ax.text(
+#             tick_loc,
+#             1.05,
+#             textwrap.fill(category, 15) + f"\n{category_percentages[n]:0.0%}",
+#             ha="center",
+#             va="bottom",
+#             size="small",
+#         )
+
+#     # Add stacked plot
+#     t = utci_shade_benefit_categories
+#     t = t.groupby([t.index.month, t]).count().unstack().T
+#     t = t / t.sum()
+#     months = [calendar.month_abbr[i] for i in range(1, 13, 1)]
+#     t.T.plot.bar(
+#         ax=histogram_ax,
+#         stacked=True,
+#         color=colors,
+#         legend=False,
+#         width=1,
+#     )
+#     histogram_ax.set_xlabel(None)
+#     histogram_ax.set_xlim(-0.5, 11.5)
+#     histogram_ax.set_ylim(0, 1)
+#     histogram_ax.set_xticklabels(months, ha="center", rotation=0)
+#     for spine in ["top", "right"]:
+#         histogram_ax.spines[spine].set_visible(False)
+#     histogram_ax.yaxis.set_major_formatter(mticker.PercentFormatter(1))
+
+#     # # Add header percentages for bar plot
+#     for month, row in (
+#         (
+#             utci_shade_benefit_categories.groupby(
+#                 utci_shade_benefit_categories.index.month
+#             )
+#             .value_counts()
+#             .unstack()
+#             .T
+#             / utci_shade_benefit_categories.groupby(
+#                 utci_shade_benefit_categories.index.month
+#             ).count()
+#         )
+#         .T.fillna(0)
+#         .iterrows()
+#     ):
+#         txt = ""
+#         for n, val in enumerate(row.values[::-1]):
+#             txtx = f"{val:0.0%}{txt}"
+#             histogram_ax.text(
+#                 month - 1,
+#                 1.02,
+#                 txtx,
+#                 va="bottom",
+#                 ha="center",
+#                 color=colors[::-1][n],
+#                 fontsize="small",
+#             )
+#             txt += "\n"
+
+#     title_base = "Shade benefit"
+#     if title is None:
+#         heatmap_ax.set_title(title_base, y=1, ha="left", va="bottom", x=0)
+#     else:
+#         heatmap_ax.set_title(
+#             f"{title_base}\n{title}",
+#             y=1,
+#             ha="left",
+#             va="bottom",
+#             x=0,
+#         )
+
+#     return fig
+
+
+# # def utci_feasibility(
+# #     epw: EPW,
+# #     simplified: bool = False,
+# #     comfort_limits: tuple = (9, 26),
+# #     included_additional_moisture: bool = False,
+# #     analysis_periods: Union[AnalysisPeriod, tuple[AnalysisPeriod]] = (AnalysisPeriod()),
+# #     met_rate_adjustment: float = None,
+# # ) -> Figure:
+# #     """Plot the UTCI feasibility for each month of the year.
+
+# #     Args:
+# #         epw (EPW):
+# #             An EPW object.
+# #         simplified (bool, optional):
+# #             Default is False.
+# #         comfort_limits (tuple, optional):
+# #             Default is (9, 26). Only used if simplified is True.
+# #         included_additional_moisture (bool, optional):
+# #             Default is False. If True, then include evap cooling in this analysis.
+# #         analysis_periods (Union[AnalysisPeriod, tuple[AnalysisPeriod]], optional):
+# #             An AnalysisPeriod or a tuple of AnalysisPeriods to be used for the analysis.
+# #             Defaults to (AnalysisPeriod(),).
+# #         met_rate_adjustment (float, optional):
+# #             A value to be added to the metabolic rate of the UTCI model. This can be used
+# #             to account for changes in metabolic rate due to clothing, exercise, etc.
+# #             Defaults to None.
+# #     Returns:
+# #         Figure:
+# #             A matplotlib Figure object.
+# #     """
+
+# #     df = feasible_comfort_category(
+# #         epw,
+# #         simplified=simplified,
+# #         comfort_limits=comfort_limits,
+# #         include_additional_moisture=included_additional_moisture,
+# #         analysis_periods=analysis_periods,
+# #         met_rate_adjustment_value=met_rate_adjustment,
+# #     )
+
+# #     labels, _ = utci_comfort_categories(
+# #         simplified=simplified,
+# #         comfort_limits=comfort_limits,
+# #         rtype="category",
+# #     )
+# #     colors, _ = utci_comfort_categories(
+# #         simplified=simplified,
+# #         comfort_limits=comfort_limits,
+# #         rtype="color",
+# #     )
+
+# #     fig, axes = plt.subplots(1, 12, figsize=(10, 4), sharey=True, sharex=False)
+
+# #     ypos = range(len(df))
+# #     for n, ax in enumerate(axes):
+# #         # get values
+# #         low = df.iloc[n].filter(regex="lowest")
+# #         high = df.iloc[n].filter(regex="highest")
+# #         ypos = range(len(low))
+
+# #         ax.barh(
+# #             ypos,
+# #             width=high.values - low.values,
+# #             left=low.values,
+# #             color=colors,
+# #             zorder=3,
+# #             alpha=0.8,
+# #         )
+
+# #         for rect in ax.patches:
+# #             width = rect.get_width()
+# #             height = rect.get_height()
+# #             _x = rect.get_x()
+# #             _y = rect.get_y()
+# #             if width == 0:
+# #                 if _x == 1:
+# #                     # text saying 100% of hours are in this category
+# #                     ax.text(
+# #                         0.5,
+# #                         _y + (height / 2),
+# #                         textwrap.fill("All times", 15),
+# #                         ha="center",
+# #                         va="center",
+# #                         rotation=0,
+# #                         fontsize="xx-small",
+# #                         zorder=3,
+# #                     )
+# #                 continue
+
+# #             ax.text(
+# #                 _x - 0.03,
+# #                 _y + (height),
+# #                 f"{_x:0.1%}",
+# #                 ha="right",
+# #                 va="top",
+# #                 rotation=90,
+# #                 fontsize="xx-small",
+# #                 zorder=3,
+# #             )
+# #             ax.text(
+# #                 _x + width + 0.03,
+# #                 _y + (height),
+# #                 f"{_x + width:0.1%}",
+# #                 ha="left",
+# #                 va="top",
+# #                 rotation=90,
+# #                 fontsize="xx-small",
+# #                 zorder=3,
+# #             )
+
+# #         if simplified:
+# #             for nn, i in enumerate(colors):
+# #                 ax.axhspan(ymin=nn - 0.5, ymax=nn + 0.5, fc=i, alpha=0.2, zorder=1)
+# #         else:
+# #             for nn, i in enumerate(UTCICategories):
+# #                 ax.axhspan(
+# #                     ymin=nn - 0.5, ymax=nn + 0.5, fc=i.color, alpha=0.2, zorder=1
+# #                 )
+
+# #         ax.set_xlim(-0.1, 1.1)
+# #         ax.set_ylim(-0.5, len(ypos) - 0.5)
+# #         for spine in ["left", "bottom"]:
+# #             ax.spines[spine].set_visible(False)
+# #         ax.tick_params(labelleft=False, left=False)
+# #         ax.set_xticks([-0.1, 0.5, 1.1])
+# #         ax.set_xticklabels(["", month_abbr[n + 1], ""])
+# #         ax.grid(False)
+
+# #         if n == 5:
+# #             handles = []
+# #             if simplified:
+# #                 for col, lab in list(zip(*[["#3C65AF", "#2EB349", "#C31F25"], labels])):
+# #                     handles.append(mpatches.Patch(color=col, label=lab, alpha=0.3))
+# #             else:
+# #                 for i in UTCICategories:
+# #                     handles.append(
+# #                         mpatches.Patch(color=i.color, label=i.value, alpha=0.3)
+# #                     )
+
+# #             ax.legend(
+# #                 handles=handles,
+# #                 bbox_to_anchor=(0.5, -0.1),
+# #                 loc="upper center",
+# #                 ncol=3 if simplified else 4,
+# #                 borderaxespad=0,
+# #                 frameon=False,
+# #             )
+
+# #         ti = f"{location_to_string(epw.location)}\nFeasible ranges of UTCI temperatures ({describe_ap(analysis_periods)})"
+# #         if met_rate_adjustment:
+# #             ti += f" with MET rate adjustment to {met_rate_adjustment} MET"
+# #         plt.suptitle(
+# #             textwrap.fill(ti, 90),
+# #             x=0.075,
+# #             y=0.9,
+# #             ha="left",
+# #             va="bottom",
+# #         )
+
+# #     plt.tight_layout()
+# #     return fig
+
+
+# def utci_distance_to_comfortable(
+#     collection: HourlyContinuousCollection,
+#     title: str = None,
+#     comfort_thresholds: tuple[float] = (9, 26),
+#     low_limit: float = 15,
+#     high_limit: float = 25,
+# ) -> Figure:
+#     """Plot the distance (in C) to comfortable for a given Ladybug HourlyContinuousCollection
+#         containing UTCI values.
+
+#     Args:
+#         collection (HourlyContinuousCollection):
+#             A Ladybug Universal Thermal Climate Index HourlyContinuousCollection object.
+#         title (str, optional):
+#             A title to place at the top of the plot. Defaults to None.
+#         comfort_thresholds (list[float], optional):
+#             The comfortable band of UTCI temperatures. Defaults to [9, 26].
+#         low_limit (float, optional):
+#             The distance from the lower edge of the comfort threshold to include in the "too cold"
+#             part of the heatmap. Defaults to 15.
+#         high_limit (float, optional):
+#             The distance from the upper edge of the comfort threshold to include in the "too hot"
+#             part of the heatmap. Defaults to 25.
+#     Returns:
+#         Figure:
+#             A matplotlib Figure object.
+#     """
+
+#     if not isinstance(collection.header.data_type, UniversalThermalClimateIndex):
+#         raise ValueError("This method only works for UTCI data.")
+
+#     if not len(comfort_thresholds) == 2:
+#         raise ValueError("comfort_thresholds must be a list of length 2.")
+
+#     # Create matrices containing the above/below/within UTCI distances to comfortable
+#     series = collection_to_series(collection)
+
+#     low, high = comfort_thresholds
+#     midpoint = np.mean([low, high])
+
+#     distance_above_comfortable = (series[series > high] - high).to_frame()
+#     distance_above_comfortable_matrix = (
+#         distance_above_comfortable.set_index(
+#             [
+#                 distance_above_comfortable.index.dayofyear,
+#                 distance_above_comfortable.index.hour,
+#             ]
+#         )["Universal Thermal Climate Index (C)"]
+#         .astype(np.float64)
+#         .unstack()
+#         .T.reindex(range(24), axis=0)
+#         .reindex(range(365), axis=1)
+#     )
+
+#     distance_below_comfortable = (low - series[series < low]).to_frame()
+#     distance_below_comfortable_matrix = (
+#         distance_below_comfortable.set_index(
+#             [
+#                 distance_below_comfortable.index.dayofyear,
+#                 distance_below_comfortable.index.hour,
+#             ]
+#         )["Universal Thermal Climate Index (C)"]
+#         .astype(np.float64)
+#         .unstack()
+#         .T.reindex(range(24), axis=0)
+#         .reindex(range(365), axis=1)
+#     )
+
+#     distance_below_midpoint = (
+#         midpoint - series[(series >= low) & (series <= midpoint)]
+#     ).to_frame()
+#     distance_below_midpoint_matrix = (
+#         distance_below_midpoint.set_index(
+#             [
+#                 distance_below_midpoint.index.dayofyear,
+#                 distance_below_midpoint.index.hour,
+#             ]
+#         )["Universal Thermal Climate Index (C)"]
+#         .astype(np.float64)
+#         .unstack()
+#         .T.reindex(range(24), axis=0)
+#         .reindex(range(365), axis=1)
+#     )
+
+#     distance_above_midpoint = (
+#         series[(series <= high) & (series > midpoint)] - midpoint
+#     ).to_frame()
+#     distance_above_midpoint_matrix = (
+#         distance_above_midpoint.set_index(
+#             [
+#                 distance_above_midpoint.index.dayofyear,
+#                 distance_above_midpoint.index.hour,
+#             ]
+#         )["Universal Thermal Climate Index (C)"]
+#         .astype(np.float64)
+#         .unstack()
+#         .T.reindex(range(24), axis=0)
+#         .reindex(range(365), axis=1)
+#     )
+
+#     distance_above_comfortable_cmap = plt.get_cmap("YlOrRd")  # Reds
+#     distance_above_comfortable_lims = [0, high_limit]
+#     distance_above_comfortable_norm = BoundaryNorm(
+#         np.linspace(
+#             distance_above_comfortable_lims[0], distance_above_comfortable_lims[1], 100
+#         ),
+#         ncolors=distance_above_comfortable_cmap.N,
+#         clip=True,
+#     )
+
+#     distance_below_comfortable_cmap = plt.get_cmap("YlGnBu")  # Blues
+#     distance_below_comfortable_lims = [0, low_limit]
+#     distance_below_comfortable_norm = BoundaryNorm(
+#         np.linspace(
+#             distance_below_comfortable_lims[0], distance_below_comfortable_lims[1], 100
+#         ),
+#         ncolors=distance_below_comfortable_cmap.N,
+#         clip=True,
+#     )
+
+#     distance_below_midpoint_cmap = plt.get_cmap("YlGn_r")  # Greens_r
+#     distance_below_midpoint_lims = [0, midpoint - low]
+#     distance_below_midpoint_norm = BoundaryNorm(
+#         np.linspace(
+#             distance_below_midpoint_lims[0], distance_below_midpoint_lims[1], 100
+#         ),
+#         ncolors=distance_below_midpoint_cmap.N,
+#         clip=True,
+#     )
+
+#     distance_above_midpoint_cmap = plt.get_cmap("YlGn_r")  # Greens_r
+#     distance_above_midpoint_lims = [0, high - midpoint]
+#     distance_above_midpoint_norm = BoundaryNorm(
+#         np.linspace(
+#             distance_above_midpoint_lims[0], distance_above_midpoint_lims[1], 100
+#         ),
+#         ncolors=distance_above_midpoint_cmap.N,
+#         clip=True,
+#     )
+
+#     extent = [
+#         mdates.date2num(series.index.min()),
+#         mdates.date2num(series.index.max()),
+#         726449,
+#         726450,
+#     ]
+
+#     fig = plt.figure(constrained_layout=False, figsize=(15, 5))
+#     gs = GridSpec(2, 3, figure=fig, width_ratios=[1, 1, 1], height_ratios=[20, 1])
+#     hmap_ax = fig.add_subplot(gs[0, :])
+#     cb_low_ax = fig.add_subplot(gs[1, 0])
+#     cb_mid_ax = fig.add_subplot(gs[1, 1])
+#     cb_high_ax = fig.add_subplot(gs[1, 2])
+
+#     hmap_ax.imshow(
+#         np.ma.array(
+#             distance_below_comfortable_matrix,
+#             mask=np.isnan(distance_below_comfortable_matrix),
+#         ),
+#         extent=extent,
+#         aspect="auto",
+#         cmap=distance_below_comfortable_cmap,
+#         norm=distance_below_comfortable_norm,
+#         interpolation="none",
+#     )
+#     hmap_ax.imshow(
+#         np.ma.array(
+#             distance_below_midpoint_matrix,
+#             mask=np.isnan(distance_below_midpoint_matrix),
+#         ),
+#         extent=extent,
+#         aspect="auto",
+#         cmap=distance_below_midpoint_cmap,
+#         norm=distance_below_midpoint_norm,
+#         interpolation="none",
+#     )
+#     hmap_ax.imshow(
+#         np.ma.array(
+#             distance_above_comfortable_matrix,
+#             mask=np.isnan(distance_above_comfortable_matrix),
+#         ),
+#         extent=extent,
+#         aspect="auto",
+#         cmap=distance_above_comfortable_cmap,
+#         norm=distance_above_comfortable_norm,
+#         interpolation="none",
+#     )
+#     hmap_ax.imshow(
+#         np.ma.array(
+#             distance_above_midpoint_matrix,
+#             mask=np.isnan(distance_above_midpoint_matrix),
+#         ),
+#         extent=extent,
+#         aspect="auto",
+#         cmap=distance_above_midpoint_cmap,
+#         norm=distance_above_midpoint_norm,
+#         interpolation="none",
+#     )
+
+#     # Axis formatting
+#     hmap_ax.invert_yaxis()
+#     hmap_ax.xaxis_date()
+#     hmap_ax.xaxis.set_major_formatter(mdates.DateFormatter("%b"))
+#     hmap_ax.yaxis_date()
+#     hmap_ax.yaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
+#     hmap_ax.tick_params(labelleft=True, labelright=True, labelbottom=True)
+#     plt.setp(hmap_ax.get_xticklabels(), ha="left", color="k")
+#     plt.setp(hmap_ax.get_yticklabels(), color="k")
+
+#     # Spine formatting
+#     for spine in ["top", "bottom", "left", "right"]:
+#         hmap_ax.spines[spine].set_visible(False)
+
+#     # Grid formatting
+#     hmap_ax.grid(visible=True, which="major", color="white", linestyle=":", alpha=1)
+
+#     # Colorbars
+#     low_cb = ColorbarBase(
+#         cb_low_ax,
+#         cmap=distance_below_comfortable_cmap,
+#         orientation="horizontal",
+#         norm=distance_below_comfortable_norm,
+#         label='Degrees below "comfortable"',
+#         extend="max",
+#     )
+#     low_cb.outline.set_visible(False)
+#     cb_low_ax.xaxis.set_major_locator(mticker.MaxNLocator(5))
+
+#     mid_cb = ColorbarBase(
+#         cb_mid_ax,
+#         cmap=distance_below_midpoint_cmap,
+#         orientation="horizontal",
+#         norm=distance_below_midpoint_norm,
+#         label='Degrees about "comfortable"',
+#         extend="neither",
+#     )
+#     mid_cb.outline.set_visible(False)
+#     cb_mid_ax.xaxis.set_major_locator(mticker.MaxNLocator(5))
+
+#     high_cb = ColorbarBase(
+#         cb_high_ax,
+#         cmap=distance_above_comfortable_cmap,
+#         orientation="horizontal",
+#         norm=distance_above_comfortable_norm,
+#         label='Degrees above "comfortable"',
+#         extend="max",
+#     )
+#     high_cb.outline.set_visible(False)
+#     cb_high_ax.xaxis.set_major_locator(mticker.MaxNLocator(5))
+
+#     if title is None:
+#         hmap_ax.set_title(
+#             'Distance to "comfortable"', color="k", y=1, ha="left", va="bottom", x=0
+#         )
+#     else:
+#         hmap_ax.set_title(
+#             f"Distance to comfortable - {title}",
+#             color="k",
+#             y=1,
+#             ha="left",
+#             va="bottom",
+#             x=0,
+#         )
+
+#     # Tidy plot
+#     plt.tight_layout()
+
+#     return fig
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_condensation_potential.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_condensation_potential.py
index 604a3458..00773321 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_condensation_potential.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_condensation_potential.py
@@ -1,13 +1,17 @@
-import calendar
+"""Plotting methods for condensation potential."""
+
+import calendar  # pylint: disable=E0401
 
 import matplotlib.pyplot as plt
 import matplotlib.ticker as mticker
 import numpy as np
 import pandas as pd
 
+from ..bhom.analytics import bhom_analytics
 from .utilities import create_title
 
 
+@bhom_analytics()
 def condensation_potential(
     dry_bulb_temperature: pd.Series,
     dew_point_temperature: pd.Series,
@@ -16,7 +20,8 @@ def condensation_potential(
     dpt_quantile: float = 0.9,
     **kwargs,
 ) -> plt.Axes:
-    """Create a plot of the condensation potential for a given set of timeseries dry bulb temperature and dew point temperature.
+    """Create a plot of the condensation potential for a given set of
+    timeseries dry bulb temperature and dew point temperature.
 
     Args:
         dry_bulb_temperature (pd.Series):
@@ -24,11 +29,14 @@ def condensation_potential(
         dew_point_temperature (pd.Series):
             The dew point temperature dataset.
         ax (plt.Axes, optional):
-            An optional plt.Axes object to populate. Defaults to None, which creates a new plt.Axes object.
+            An optional plt.Axes object to populate. Defaults to None,
+            which creates a new plt.Axes object.
         dbt_quantile (float, optional):
-            The quantile of the dry bulb temperature to use for the condensation potential calculation. Defaults to 0.1.
+            The quantile of the dry bulb temperature to use for the
+            condensation potential calculation. Defaults to 0.1.
         dpt_quantile (float, optional):
-            The quantile of the dew point temperature to use for the condensation potential calculation. Defaults to 0.9.
+            The quantile of the dew point temperature to use for the
+            condensation potential calculation. Defaults to 0.9.
         **kwargs:
             A set of kwargs to pass to plt.plot.
 
@@ -38,7 +46,7 @@ def condensation_potential(
     """
 
     # check that the series are the same length and have the same index and are both indexes of pd.DatetimeIndex
-    if not (len(dry_bulb_temperature) == len(dew_point_temperature)):
+    if not len(dry_bulb_temperature) == len(dew_point_temperature):
         raise ValueError(
             "The dry bulb temperature and dew point temperature must have the same length"
         )
@@ -68,7 +76,7 @@ def condensation_potential(
     ax.set_title(
         create_title(
             kwargs.pop("title", None),
-            f"Condensation potential (for values between {min(dry_bulb_temperature.index):%b %Y} and {max(dry_bulb_temperature.index):%b %Y})",
+            "Condensation potential",
         )
     )
 
@@ -126,7 +134,11 @@ def condensation_potential(
     ax.text(
         1,
         1,
-        f"{(dbt.values < dpt.values).sum() / len(dbt):0.1%} of annual hours with potential for condensation\n(using {dbt_quantile:0.0%}-ile DBT and {dpt_quantile:0.0%}-ile DPT)",
+        (
+            f"{(dbt.values < dpt.values).sum() / len(dbt):0.1%} of annual hours "
+            f"with potential for condensation\n(using {dbt_quantile:0.0%}-ile DBT "
+            f"and {dpt_quantile:0.0%}-ile DPT)"
+        ),
         ha="right",
         va="top",
         transform=ax.transAxes,
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_degree_days.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_degree_days.py
index 6ea158ff..08d56359 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_degree_days.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_degree_days.py
@@ -1,15 +1,22 @@
-import calendar
+"""Methods for plotting degree days from EPW files."""
+import calendar  # pylint: disable=E0401
 
 import matplotlib.pyplot as plt
 from ladybug.epw import EPW
+from .utilities import contrasting_color
 
+from ..bhom.analytics import bhom_analytics
 from ..ladybug_extension.epw import EPW, degree_time
-
-# from .utilities import add_bar_labels
+from ..ladybug_extension.location import location_to_string
 
 
+@bhom_analytics()
 def cooling_degree_days(
-    epw: EPW, ax: plt.Axes = None, cool_base: float = 23, **kwargs
+    epw: EPW,
+    ax: plt.Axes = None,
+    cool_base: float = 23,
+    show_labels: bool = True,
+    **kwargs,
 ) -> plt.Axes:
     """Plot the cooling degree days from a given EPW object.
 
@@ -20,6 +27,8 @@ def cooling_degree_days(
             A matplotlib Axes object. Defaults to None.
         cool_base (float, optional):
             The temperature at which cooling kicks in. Defaults to 23.
+        show_labels (bool, optional):
+            Whether to show the labels on the bars. Defaults to True.
         **kwargs:
             Additional keyword arguments to pass to the matplotlib bar plot.
 
@@ -36,34 +45,67 @@ def cooling_degree_days(
 
     temp = degree_time([epw], return_type="days", cool_base=cool_base)
 
-    location_name = temp.columns.get_level_values(0).unique()[0]
     temp = temp.droplevel(0, axis=1).resample("MS").sum()
     temp.index = [calendar.month_abbr[i] for i in temp.index.month]
     clg = temp.filter(regex="Cooling")
 
-    title = kwargs.pop("title", f"{location_name}\nCooling degree days")
-    color = kwargs.pop("color", "blue")
+    color = kwargs.pop("color", "#00A9E0")
 
     clg.plot(ax=ax, kind="bar", color=color, **kwargs)
     ax.set_ylabel(clg.columns[0])
     plt.setp(ax.get_xticklabels(), rotation=0, ha="center")
     ax.grid(visible=True, which="major", axis="both", ls="--", lw=1, alpha=0.2)
-    # add_bar_labels(ax, orientation="vertical", threshold=)
 
     ax.text(
         1,
         1,
-        f"Annual: {sum(rect.get_height() for rect in ax.patches):0.0f}",
+        f"Annual: {sum(rect.get_height() for rect in ax.patches):0.0f} days",
         transform=ax.transAxes,
         ha="right",
     )
-    ax.set_title(title, x=0, ha="left")
+
+    if show_labels:
+        max_height = max(v.get_height() for v in ax.containers[0])
+        for i, c in enumerate(ax.containers):
+            label_colors = [contrasting_color(i.get_facecolor()) for i in c.patches]
+            labels = [
+                f"{v.get_height():0.0f}" if v.get_height() > 0.1 * max_height else ""
+                for v in c
+            ]
+            ax.bar_label(
+                c,
+                labels=labels,
+                label_type="edge",
+                color=label_colors[i],
+                # fontsize="small",
+                fontweight="bold",
+                padding=-15,
+            )
+            labels = [
+                f"{v.get_height():0.0f}" if v.get_height() <= 0.1 * max_height else ""
+                for v in c
+            ]
+            ax.bar_label(
+                c,
+                labels=labels,
+                label_type="edge",
+                # color=label_colors[i],
+                # fontsize="small",
+                fontweight="bold",
+                # padding=-15,
+            )
+
     plt.tight_layout()
     return ax
 
 
+@bhom_analytics()
 def heating_degree_days(
-    epw: EPW, ax: plt.Axes = None, heat_base: float = 18, **kwargs
+    epw: EPW,
+    ax: plt.Axes = None,
+    heat_base: float = 18,
+    show_labels: bool = True,
+    **kwargs,
 ) -> plt.Axes:
     """Plot the heating/cooling degree days from a given EPW
     object.
@@ -75,6 +117,8 @@ def heating_degree_days(
             A matplotlib Axes object. Defaults to None.
         heat_base (float, optional):
             The temperature at which heating kicks in. Defaults to 18.
+        show_labels (bool, optional):
+            Whether to show the labels on the bars. Defaults to True.
         **kwargs:
             Additional keyword arguments to pass to the matplotlib
             bar plot.
@@ -92,32 +136,61 @@ def heating_degree_days(
 
     temp = degree_time([epw], return_type="days", heat_base=heat_base)
 
-    location_name = temp.columns.get_level_values(0).unique()[0]
     temp = temp.droplevel(0, axis=1).resample("MS").sum()
     temp.index = [calendar.month_abbr[i] for i in temp.index.month]
     data = temp.filter(regex="Heating")
 
-    title = kwargs.pop("title", f"{location_name}\nHeating degree days")
-    color = kwargs.pop("color", "orange")
+    color = kwargs.pop("color", "#D50032")
 
     data.plot(ax=ax, kind="bar", color=color, **kwargs)
     ax.set_ylabel(data.columns[0])
     plt.setp(ax.get_xticklabels(), rotation=0, ha="center")
     ax.grid(visible=True, which="major", axis="both", ls="--", lw=1, alpha=0.2)
-    # add_bar_labels(ax, orientation="vertical")
 
     ax.text(
         1,
         1,
-        f"Annual: {sum(rect.get_height() for rect in ax.patches):0.0f}",
+        f"Annual: {sum(rect.get_height() for rect in ax.patches):0.0f} days",
         transform=ax.transAxes,
         ha="right",
     )
-    ax.set_title(title, x=0, ha="left")
+
+    if show_labels:
+        max_height = max(v.get_height() for v in ax.containers[0])
+        for i, c in enumerate(ax.containers):
+            label_colors = [contrasting_color(i.get_facecolor()) for i in c.patches]
+            labels = [
+                f"{v.get_height():0.0f}" if v.get_height() > 0.1 * max_height else ""
+                for v in c
+            ]
+            ax.bar_label(
+                c,
+                labels=labels,
+                label_type="edge",
+                color=label_colors[i],
+                # fontsize="small",
+                fontweight="bold",
+                padding=-15,
+            )
+            labels = [
+                f"{v.get_height():0.0f}" if v.get_height() <= 0.1 * max_height else ""
+                for v in c
+            ]
+            ax.bar_label(
+                c,
+                labels=labels,
+                label_type="edge",
+                # color=label_colors[i],
+                # fontsize="small",
+                fontweight="bold",
+                # padding=-15,
+            )
+
     plt.tight_layout()
     return ax
 
 
+@bhom_analytics()
 def degree_days(epw: EPW, heat_base: float = 18, cool_base: float = 23, **kwargs):
     """Plot the heating/cooling degree days from a given EPW object.
 
@@ -129,21 +202,26 @@ def degree_days(epw: EPW, heat_base: float = 18, cool_base: float = 23, **kwargs
         cool_base (float, optional):
             The temperature at which cooling kicks in. Defaults to 23.
         **kwargs:
-            Additional keyword arguments to pass to the matplotlib bar plot.
-            heat_color: The color of the heating degree days bars.
-            cool_color: The color of the cooling degree days bars.
-            figsize: The size of the figure.
+            Additional keyword arguments to pass to the plot. These can include:
+            heat_color (str):
+                The color of the heating degree days bars.
+            cool_color (str):
+                The color of the cooling degree days bars.
+            figsize (Tuple[float]):
+                The size of the figure.
 
     Returns:
         Figure:
             A matplotlib Figure object.
     """
 
-    figsize = kwargs.pop("figsize", (8, 6))
-    heat_color = kwargs.pop("heat_color", "orange")
-    cool_color = kwargs.pop("cool_color", "blue")
+    figsize = kwargs.pop("figsize", (15, 6))
+    heat_color = kwargs.pop("heat_color", "#D50032")
+    cool_color = kwargs.pop("cool_color", "#00A9E0")
 
     fig, ax = plt.subplots(nrows=2, figsize=figsize)
     heating_degree_days(epw, ax=ax[0], heat_base=heat_base, color=heat_color, **kwargs)
     cooling_degree_days(epw, ax=ax[1], cool_base=cool_base, color=cool_color, **kwargs)
+
+    ax[0].set_title(f"{location_to_string(epw.location)} degree days", x=0, ha="left")
     return fig
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_diurnal.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_diurnal.py
index 7131be37..ffe477f8 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_diurnal.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_diurnal.py
@@ -1,6 +1,10 @@
+"""Methods for plotting diurnal profiles from time-indexed data."""
+
+# pylint: disable=E0401
 import calendar
 import textwrap
-from typing import List
+
+# pylint: enable=E0401
 
 import matplotlib.collections as mcollections
 import matplotlib.lines as mlines
@@ -10,9 +14,11 @@
 import numpy as np
 import pandas as pd
 
+from ..bhom.analytics import bhom_analytics
 from .utilities import create_title
 
 
+@bhom_analytics()
 def diurnal(
     series: pd.Series,
     ax: plt.Axes = None,
@@ -104,7 +110,7 @@ def diurnal(
             if i[1] == 0:
                 major_ticklabels.append(f"{calendar.month_abbr[i[0]]}")
             elif i[1] == 12:
-                major_ticklabels.append(f"")
+                major_ticklabels.append("")
     else:
         raise ValueError("period must be one of 'daily', 'weekly', or 'monthly'")
 
@@ -112,7 +118,7 @@ def diurnal(
     ax.set_title(
         create_title(
             kwargs.pop("title", None),
-            f"Average {period} diurnal profile (~{samples_per_timestep:0.0f} samples per timestep)",
+            f"Average {period} diurnal profile (≈{samples_per_timestep:0.0f} samples per timestep)",
         )
     )
 
@@ -206,7 +212,7 @@ def diurnal(
     )
     if show_legend:
         ax.legend(
-            bbox_to_anchor=(0.5, -0.15),
+            bbox_to_anchor=(0.5, -0.2),
             loc=8,
             ncol=6,
             borderaxespad=0,
@@ -217,19 +223,20 @@ def diurnal(
     return ax
 
 
+@bhom_analytics()
 def stacked_diurnals(
-    datasets: List[pd.Series], period: str = "monthly", **kwargs
+    datasets: list[pd.Series], period: str = "monthly", **kwargs
 ) -> plt.Figure:
     """Create a matplotlib figure with stacked diurnal profiles.
 
     Args:
-        datasets (List[pd.Series]):
+        datasets (list[pd.Series]):
             A list of time-indexed Pandas Series objects.
         period (str, optional):
             The period to aggregate over. Must be one of "dailyy", "weekly", or "monthly". Defaults to "monthly".
         **kwargs (Dict[str, Any], optional):
             Additional keyword arguments to pass to the matplotlib plotting function.
-            colors (List[str], optional):
+            colors (list[str], optional):
                 A list of colors to use for the plots. Defaults to None, which uses the default diurnal color.
 
     Returns:
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_evaporative_cooling_potential.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_evaporative_cooling_potential.py
new file mode 100644
index 00000000..c0092c4d
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_evaporative_cooling_potential.py
@@ -0,0 +1,50 @@
+"""Method for plotting evaporative cooling potential."""
+
+import matplotlib.pyplot as plt
+from pathlib import Path
+from ladybug.epw import EPW
+
+from ._heatmap import heatmap
+from ..ladybug_extension.datacollection import collection_to_series
+from ..ladybug_extension.location import location_to_string
+
+
+def evaporative_cooling_potential(epw: EPW, ax: plt.Axes = None, **kwargs) -> plt.Axes:
+    """Plot evaporative cooling potential (DBT - DPT).
+
+    Args:
+        epw (EPW):
+            An EPW object.
+        ax (plt.Axes, optional):
+            The matplotlib axes to plot the figure on. Defaults to None.
+        **kwargs:
+            Additional keyword arguments to pass to the heatmap function.
+
+    Returns:
+        plt.Axes: The matplotlib axes.
+    """
+
+    if ax is None:
+        ax = plt.gca()
+
+    dpt = collection_to_series(epw.dew_point_temperature)
+    dbt = collection_to_series(epw.dry_bulb_temperature)
+    ecp = (dbt - dpt).clip(lower=0).rename("Evaporative Cooling Potential (C)")
+
+    if "cmap" not in kwargs:
+        kwargs["cmap"] = "GnBu"
+
+    heatmap(series=ecp, ax=ax, **kwargs)
+    ax.text(
+        1,
+        1,
+        "*value shown indicate cooling effect from saturating air with moisture (DBT - DPT)",
+        transform=ax.transAxes,
+        ha="right",
+        va="top",
+        fontsize="small",
+    )
+
+    ax.set_title(Path(epw.file_path).name)
+
+    return ax
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_heatmap.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_heatmap.py
index e613e950..bcff46ba 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_heatmap.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_heatmap.py
@@ -1,10 +1,15 @@
+"""Methods for plotting heatmaps from time-indexed data."""
+
 import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
 
+from ..bhom.analytics import bhom_analytics
 from ..helpers import validate_timeseries
 
 
+@bhom_analytics()
 def heatmap(
     series: pd.Series,
     ax: plt.Axes = None,
@@ -23,6 +28,8 @@ def heatmap(
                 If True, show the colorbar. Defaults to True.
             title (str, optional):
                 The title of the plot. Defaults to None.
+            mask (List[bool], optional):
+                A list of booleans to mask the data. Defaults to None.
 
     Returns:
         plt.Axes:
@@ -45,6 +52,13 @@ def heatmap(
     )
     z = day_time_matrix.values
 
+    if "mask" in kwargs:
+        if len(kwargs["mask"]) != len(series):
+            raise ValueError(
+                f"Length of mask ({len(kwargs['mask'])}) must match length of data ({len(series)})."
+            )
+        z = np.ma.masked_array(z, mask=kwargs.pop("mask"))
+
     # handle non-standard kwargs
     extend = kwargs.pop("extend", "neither")
     title = kwargs.pop("title", series.name)
@@ -68,7 +82,7 @@ def heatmap(
     ax.yaxis_date()
     ax.yaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
 
-    ax.tick_params(labelleft=True, labelright=True, labelbottom=True)
+    ax.tick_params(labelleft=True, labelbottom=True)
     plt.setp(ax.get_xticklabels(), ha="left")
 
     for spine in ["top", "bottom", "left", "right"]:
@@ -89,6 +103,7 @@ def heatmap(
             aspect=100,
             pad=0.075,
             extend=extend,
+            label=series.name,
         )
         cb.outline.set_visible(False)
 
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_misc.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_misc.py
new file mode 100644
index 00000000..e1a16a75
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_misc.py
@@ -0,0 +1,507 @@
+"""Miscellaneous plots that don't really fit anywhere else."""
+# pylint: disable=line-too-long
+from calendar import month_abbr  # pylint: disable=E0401
+
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mtick
+
+import numpy as np
+import pandas as pd
+
+from ladybug.epw import EPW, Location
+from ladybug.sunpath import Sunpath
+
+from ..ladybug_extension.datacollection import collection_to_series
+from ..helpers import sunrise_sunset
+from ._heatmap import heatmap
+from ..categorical.categories import Categorical
+
+
+def cloud_cover_categories(epw: EPW, ax: plt.Axes = None) -> plt.Axes:
+    """Plot cloud cover categories from an EPW file.
+
+    Args:
+        epw (EPW):
+            The EPW file to plot.
+        ax (plt.Axes, optional):
+            A matploltib axes to plot on. Defaults to None.
+
+    Returns:
+        plt.Axes:
+            The matplotlib axes.
+    """
+    if ax is None:
+        ax = plt.gca()
+
+    s = collection_to_series(epw.opaque_sky_cover)
+    mtx = s.groupby([s.index.month, s.index.day]).value_counts().unstack().fillna(0)
+    mapper = {
+        0: "clear",
+        1: "clear",
+        2: "mostly clear",
+        3: "mostly clear",
+        4: "partly cloudy",
+        5: "partly cloudy",
+        6: "mostly cloudy",
+        7: "mostly cloudy",
+        8: "overcast",
+        9: "overcast",
+        10: "overcast",
+    }
+    mtx = mtx.T.groupby(mapper).sum()[
+        ["clear", "mostly clear", "partly cloudy", "mostly cloudy", "overcast"]
+    ]
+    mtx = (mtx.T / mtx.sum(axis=1)).T
+
+    mtx.plot.area(ax=ax, color=["#95B5DF", "#B1C4DD", "#C9D0D9", "#ACB0B6", "#989CA1"])
+    ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))
+    ax.set_xticks(
+        ticks=[n for n, i in enumerate(mtx.index) if i[1] == 1],
+        labels=[month_abbr[i] for i in range(1, 13, 1)],
+        ha="left",
+    )
+    ax.set_xlim(0, len(mtx))
+    ax.set_ylim(0, 1)
+    ax.legend(
+        bbox_to_anchor=(0.5, -0.05),
+        loc="upper center",
+        ncol=5,
+        title="Cloud cover categories",
+    )
+
+    return ax
+
+
+def hours_sunlight(location: Location, ax: plt.Axes = None) -> plt.Axes:
+    """Plot the hours of sunlight for a location.
+
+    Args:
+        location (Location):
+            The location to plot.
+        ax (plt.Axes, optional):
+            A matploltib axes to plot on. Defaults to None.
+
+    Returns:
+        plt.Axes:
+            The matplotlib axes.
+    """
+    srss_df = sunrise_sunset(location)
+    #
+    ## hours of daylight
+    daylight = pd.Series(
+        [i.seconds / (60 * 60) for i in (srss_df["sunset"] - srss_df["sunrise"])],
+        name="daylight",
+        index=srss_df.index,
+    )
+    civil_twilight = (
+        [
+            i.seconds / (60 * 60)
+            for i in (srss_df["civil twilight end"] - srss_df["civil twilight start"])
+        ]
+        - daylight
+    ).rename("civil twilight")
+    nautical_twilight = (
+        [
+            i.seconds / (60 * 60)
+            for i in (
+                srss_df["nautical twilight end"] - srss_df["nautical twilight start"]
+            )
+        ]
+        - daylight
+        - civil_twilight
+    ).rename("nautical twilight")
+    astronomical_twilight = (
+        [
+            i.seconds / (60 * 60)
+            for i in (
+                srss_df["astronomical twilight end"]
+                - srss_df["astronomical twilight start"]
+            )
+        ]
+        - daylight
+        - civil_twilight
+        - nautical_twilight
+    ).rename("astronomical twilight")
+    night = (
+        24 - (daylight + civil_twilight + nautical_twilight + astronomical_twilight)
+    ).rename("night")
+
+    temp = pd.concat(
+        [daylight, civil_twilight, nautical_twilight, astronomical_twilight, night],
+        axis=1,
+    )
+    ax = plt.gca()
+    ax.stackplot(
+        daylight.index,
+        temp.values.T,
+        colors=["#FCE49D", "#B9AC86", "#908A7A", "#817F76", "#717171"],
+        labels=temp.columns,
+    )
+    ax.set_title("Hours of daylight and twilight")
+    ax.set_ylim(0, 24)
+    ax.set_xlim(temp.index.min(), temp.index.max())
+    ax.set_ylabel("Hours")
+
+    # plot min/max/med days
+    ax.plot(daylight, c="k")
+
+    ax.scatter(temp.daylight.idxmax(), temp.daylight.max(), c="k", s=10)
+    ax.text(
+        temp.daylight.idxmax(),
+        temp.daylight.max() - 0.5,
+        f"Summer solstice\n{np.floor(temp.daylight.max()):0.0f} hrs, {(temp.daylight.max() % 1) * 60:0.0f} mins\n{temp.daylight.idxmax():%d %b}",
+        ha="center",
+        va="top",
+    )
+
+    ax.scatter(temp.daylight.idxmin(), temp.daylight.min(), c="k", s=10)
+    ax.text(
+        temp.daylight.idxmin(),
+        temp.daylight.min() - 0.5,
+        f"Winter solstice\n{np.floor(temp.daylight.min()):0.0f} hrs, {(temp.daylight.min() % 1) * 60:0.0f} mins\n{temp.daylight.idxmin():%d %b}",
+        ha="right" if temp.daylight.idxmin().month > 6 else "left",
+        va="top",
+    )
+
+    equionoosss = abs((temp.daylight - temp.daylight.median())).sort_values()
+    ax.scatter(equionoosss.index[0], temp.daylight[equionoosss.index[0]], c="k", s=10)
+    ax.text(
+        equionoosss.index[0],
+        temp.daylight[equionoosss.index[0]] - 0.5,
+        f"Equinox\n{np.floor(temp.daylight[equionoosss.index[0]]):0.0f} hrs, {(temp.daylight[equionoosss.index[0]] % 1) * 60:0.0f} mins\n{equionoosss.index[0]:%d %b}",
+        ha="right" if equionoosss.index[0].month > 6 else "left",
+        va="top",
+    )
+
+    ix = None
+    for ix in equionoosss.index:
+        if ix.month != equionoosss.index[0].month:
+            break
+    ax.scatter(ix, temp.daylight[ix], c="k", s=10)
+    ax.text(
+        ix,
+        temp.daylight[ix] - 0.5,
+        f"Equinox\n{np.floor(temp.daylight[ix]):0.0f} hrs, {(temp.daylight[ix] % 1) * 60:0.0f} mins\n{ix:%d %b}",
+        ha="right" if ix.month > 6 else "left",
+        va="top",
+    )
+    ax.legend(
+        bbox_to_anchor=(0.5, -0.05),
+        loc="upper center",
+        ncol=5,
+        title="Day period",
+    )
+
+    return ax
+
+
+def hours_sunrise_sunset(location: Location, ax: plt.Axes = None) -> plt.Axes:
+    """Plot the hours of sunrise and sunset for a location.
+
+    Args:
+        location (Location):
+            The location to plot.
+        ax (plt.Axes, optional):
+            A matploltib axes to plot on. Defaults to None.
+
+    Returns:
+        plt.Axes:
+            The matplotlib axes.
+    """
+    if ax is None:
+        ax = plt.gca()
+
+    srss_df = sunrise_sunset(location)
+    seconds = srss_df.map(lambda a: ((a - a.normalize()) / pd.Timedelta("1 second")))
+    hours = seconds / (60 * 60)
+
+    ## hours of daylight
+    daylight = pd.Series(
+        [i.seconds / (60 * 60) for i in (srss_df["sunset"] - srss_df["sunrise"])],
+        name="daylight",
+        index=srss_df.index,
+    )
+    civil_twilight = (
+        [
+            i.seconds / (60 * 60)
+            for i in (srss_df["civil twilight end"] - srss_df["civil twilight start"])
+        ]
+        - daylight
+    ).rename("civil twilight")
+    nautical_twilight = (
+        [
+            i.seconds / (60 * 60)
+            for i in (
+                srss_df["nautical twilight end"] - srss_df["nautical twilight start"]
+            )
+        ]
+        - daylight
+        - civil_twilight
+    ).rename("nautical twilight")
+    astronomical_twilight = (
+        [
+            i.seconds / (60 * 60)
+            for i in (
+                srss_df["astronomical twilight end"]
+                - srss_df["astronomical twilight start"]
+            )
+        ]
+        - daylight
+        - civil_twilight
+        - nautical_twilight
+    ).rename("astronomical twilight")
+    night = (
+        24 - (daylight + civil_twilight + nautical_twilight + astronomical_twilight)
+    ).rename("night")
+
+    temp = pd.concat(
+        [daylight, civil_twilight, nautical_twilight, astronomical_twilight, night],
+        axis=1,
+    )
+
+    ax = plt.gca()
+    ax.fill_between(
+        hours.index,
+        np.zeros_like(hours["astronomical twilight start"]),
+        hours["astronomical twilight start"],
+        fc="#717171",
+        label="Night",
+    )
+    ax.fill_between(
+        hours.index,
+        hours["astronomical twilight start"],
+        hours["nautical twilight start"],
+        fc="#817F76",
+        label="Astonomical twilight",
+    )
+    ax.fill_between(
+        hours.index,
+        hours["nautical twilight start"],
+        hours["civil twilight start"],
+        fc="#908A7A",
+        label="Nautical twilight",
+    )
+    ax.fill_between(
+        hours.index,
+        hours["civil twilight start"],
+        hours["sunrise"],
+        fc="#B9AC86",
+        label="Civil twilight",
+    )
+    ax.fill_between(
+        hours.index, hours["sunrise"], hours["sunset"], fc="#FCE49D", label="Day"
+    )
+    ax.fill_between(
+        hours.index,
+        hours["sunset"],
+        hours["civil twilight end"],
+        fc="#B9AC86",
+        label="__nolegend__",
+    )
+    ax.fill_between(
+        hours.index,
+        hours["civil twilight end"],
+        hours["nautical twilight end"],
+        fc="#908A7A",
+        label="__nolegend__",
+    )
+    ax.fill_between(
+        hours.index,
+        hours["nautical twilight end"],
+        hours["astronomical twilight end"],
+        fc="#817F76",
+        label="__nolegend__",
+    )
+    ax.fill_between(
+        hours.index,
+        hours["astronomical twilight end"],
+        np.ones_like(hours["astronomical twilight start"]) * 24,
+        fc="#717171",
+        label="__nolegend__",
+    )
+
+    [ax.plot(hours.index, hours[i], c="k", lw=1) for i in ["sunrise", "noon", "sunset"]]
+
+    ax.axvline(temp.daylight.idxmax(), c="k", ls=":", alpha=0.5)
+    ax.text(
+        temp.daylight.idxmax(),
+        hours.noon.loc[temp.daylight.idxmax()] + 0.5,
+        f"Summer solstice ({temp.daylight.idxmax():%d %b})\n{np.floor(temp.daylight.max()):02.0f} hrs, {(temp.daylight.max() % 1) * 60:02.0f} mins",
+        ha="center",
+        va="bottom",
+    )
+    ax.text(
+        temp.daylight.idxmax(),
+        hours.sunrise.loc[temp.daylight.idxmax()] + 0.5,
+        f"Sunrise\n{int(np.floor((hours.sunrise.loc[temp.daylight.idxmax()]))):02.0f}:{(hours.sunrise.loc[temp.daylight.idxmax()] % 1) * 60:02.0f}",
+        ha="center",
+        va="bottom",
+    )
+    ax.text(
+        temp.daylight.idxmax(),
+        hours.sunset.loc[temp.daylight.idxmax()] + 0.5,
+        f"Sunset\n{int(np.floor((hours.sunset.loc[temp.daylight.idxmax()]))):02.0f}:{(hours.sunset.loc[temp.daylight.idxmax()] % 1) * 60:02.0f}",
+        ha="center",
+        va="bottom",
+    )
+
+    ax.axvline(temp.daylight.idxmin(), c="k", ls=":", alpha=0.5)
+    ax.text(
+        temp.daylight.idxmin(),
+        hours.noon.loc[temp.daylight.idxmin()] + 0.5,
+        f"Winter solstice ({temp.daylight.idxmin():%d %b})\n{np.floor(temp.daylight.min()):02.0f} hrs, {(temp.daylight.min() % 1) * 60:02.0f} mins",
+        ha="right" if temp.daylight.idxmin().month > 6 else "left",
+        va="bottom",
+    )
+    ax.text(
+        temp.daylight.idxmin(),
+        hours.sunrise.loc[temp.daylight.idxmin()] + 0.5,
+        f"Sunrise\n{int(np.floor((hours.sunrise.loc[temp.daylight.idxmin()]))):02.0f}:{(hours.sunrise.loc[temp.daylight.idxmin()] % 1) * 60:02.0f}",
+        ha="right" if temp.daylight.idxmin().month > 6 else "left",
+        va="bottom",
+    )
+    ax.text(
+        temp.daylight.idxmin(),
+        hours.sunset.loc[temp.daylight.idxmin()] + 0.5,
+        f"Sunset\n{int(np.floor((hours.sunset.loc[temp.daylight.idxmin()]))):02.0f}:{(hours.sunset.loc[temp.daylight.idxmin()] % 1) * 60:02.0f}",
+        ha="right" if temp.daylight.idxmin().month > 6 else "left",
+        va="bottom",
+    )
+
+    equionoosss = abs((temp.daylight - temp.daylight.median())).sort_values()
+    ax.axvline(equionoosss.index[0], c="k", ls=":", alpha=0.5)
+    ax.text(
+        equionoosss.index[0],
+        hours.noon.loc[temp.daylight.idxmin()] + 0.5,
+        f"Equinox ({equionoosss.index[0]:%d %b})\n{np.floor(temp.daylight[equionoosss.index[0]]):0.0f} hrs, {(temp.daylight[equionoosss.index[0]] % 1) * 60:0.0f} mins",
+        ha="right" if equionoosss.index[0].month > 6 else "left",
+        va="bottom",
+    )
+
+    ix = None
+    for ix in equionoosss.index:
+        if ix.month != equionoosss.index[0].month:
+            break
+    ax.axvline(ix, c="k", ls=":", alpha=0.5)
+    ax.text(
+        ix,
+        hours.noon.loc[ix] + 0.5,
+        f"Equinox ({ix:%d %b})\n{np.floor(temp.daylight[ix]):0.0f} hrs, {(temp.daylight[ix] % 1) * 60:0.0f} mins",
+        ha="right" if ix.month > 6 else "left",
+        va="bottom",
+    )
+
+    ax.set_title("Sunrise and sunset")
+    ax.set_ylim(0, 24)
+    ax.set_xlim(hours.index.min(), hours.index.max())
+    ax.set_ylabel("Hours")
+    ax.legend(
+        bbox_to_anchor=(0.5, -0.05),
+        loc="upper center",
+        ncol=5,
+        title="Day period",
+    )
+
+    return ax
+
+
+def solar_elevation_azimuth(location: Location, ax: plt.Axes = None) -> plt.Axes:
+    """Plot the solar elevation and azimuth for a location.
+
+    Args:
+        location (Location):
+            The location to plot.
+        ax (plt.Axes, optional):
+            A matploltib axes to plot on. Defaults to None.
+
+    Returns:
+        plt.Axes:
+            The matplotlib axes.
+    """
+
+    cat = Categorical(
+        colors=(
+            "#809FB4",
+            "#90ACBE",
+            "#9FC7A2",
+            "#90BF94",
+            "#9FC7A2",
+            "#CF807A",
+            "#C86C65",
+            "#CF807A",
+            "#C6ACA0",
+            "#BD9F92",
+            "#C6ACA0",
+            "#90ACBE",
+            "#809FB4",
+        ),
+        bins=[
+            0,
+            22.5,
+            45,
+            67.5,
+            112.5,
+            135,
+            157.5,
+            202.5,
+            225,
+            247.5,
+            292.5,
+            315,
+            337.5,
+            360,
+        ],
+        bin_names=[
+            "North",
+            "NNE",
+            "ENE",
+            "East",
+            "ESE",
+            "SSE",
+            "South",
+            "SSW",
+            "WSW",
+            "West",
+            "WNW",
+            "NNW",
+            "North",
+        ],
+        name="directions",
+    )
+
+    if ax is None:
+        ax = plt.gca()
+
+    sp = Sunpath.from_location(location)
+    idx = pd.date_range("2017-01-01 00:00:00", "2018-01-01 00:00:00", freq="10T")
+    suns = [sp.calculate_sun_from_date_time(i) for i in idx]
+    a = pd.DataFrame(index=idx)
+    a["altitude"] = [i.altitude for i in suns]
+    a["azimuth"] = [i.azimuth for i in suns]
+
+    ax = plt.gca()
+    heatmap(
+        a.azimuth,
+        ax=ax,
+        cmap=cat.cmap,
+        norm=cat.norm,
+        title="Solar Elevation and Azimuth",
+    )
+    cb = ax.collections[-1].colorbar
+    cb.set_ticks(
+        [0, 45, 90, 135, 180, 225, 270, 315, 360],
+        labels=["N", "NE", "E", "SE", "S", "SW", "W", "NW", "N"],
+    )
+    # create matrix of monthday/hour for pcolormesh
+    pvt = a.pivot_table(columns=a.index.date, index=a.index.time)
+
+    # plot the contours for sun positions
+    x = mdates.date2num(pvt["altitude"].columns)
+    y = mdates.date2num(pd.to_datetime([f"2017-01-01 {i}" for i in pvt.index]))
+    z = pvt["altitude"].values
+    # z = np.ma.masked_array(z, mask=z < 0)
+    ct = ax.contour(x, y, z, colors="k", levels=np.arange(0, 91, 10))
+    ax.clabel(ct, inline=1, fontsize="small")
+
+    return ax
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_monthly_histogram_proportion.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_monthly_histogram_proportion.py
index 9f78691a..6a78fbc9 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_monthly_histogram_proportion.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_monthly_histogram_proportion.py
@@ -1,21 +1,24 @@
-import calendar
-from typing import List
+"""Methods for plotting binned data per-month."""
+import calendar  # pylint: disable=E0401
 
 import matplotlib.pyplot as plt
 import matplotlib.ticker as mticker
 import pandas as pd
 
+from ..bhom.analytics import bhom_analytics
 from ..helpers import validate_timeseries
 from .utilities import contrasting_color
 
 
+@bhom_analytics()
 def monthly_histogram_proportion(
     series: pd.Series,
-    bins: List[float],
+    bins: list[float],
     ax: plt.Axes = None,
-    labels: List[str] = None,
+    labels: list[str] = None,
     show_year_in_label: bool = False,
     show_labels: bool = False,
+    show_legend: bool = False,
     **kwargs,
 ) -> plt.Axes:
     """Create a monthly histogram of a pandas Series.
@@ -23,16 +26,18 @@ def monthly_histogram_proportion(
     Args:
         series (pd.Series):
             The pandas Series to plot. Must have a datetime index.
-        bins (List[float]):
+        bins (list[float]):
             The bins to use for the histogram.
         ax (plt.Axes, optional):
             An optional plt.Axes object to populate. Defaults to None, which creates a new plt.Axes object.
-        labels (List[str], optional):
+        labels (list[str], optional):
             The labels to use for the histogram. Defaults to None, which uses the bin edges.
         show_year_in_label (bool, optional):
             Whether to show the year in the x-axis label. Defaults to False.
         show_labels (bool, optional):
             Whether to show the labels on the bars. Defaults to False.
+        show_legend (bool, optional):
+            Whether to show the legend. Defaults to False.
         **kwargs:
             Additional keyword arguments to pass to plt.bar.
 
@@ -47,7 +52,7 @@ def monthly_histogram_proportion(
         ax = plt.gca()
 
     t = pd.cut(series, bins=bins, labels=labels)
-    t = t.groupby([t.index.year, t.index.month, t]).count().unstack().T
+    t = t.groupby([t.index.year, t.index.month, t], observed=True).count().unstack().T
     t = t / t.sum()
 
     # adjust column labels
@@ -72,6 +77,14 @@ def monthly_histogram_proportion(
         ax.spines[spine].set_visible(False)
     ax.yaxis.set_major_formatter(mticker.PercentFormatter(1))
 
+    if show_legend:
+        ax.legend(
+            bbox_to_anchor=(1, 1),
+            loc="upper left",
+            borderaxespad=0.0,
+            frameon=False,
+        )
+
     if show_labels:
         for i, c in enumerate(ax.containers):
             label_colors = [contrasting_color(i.get_facecolor()) for i in c.patches]
@@ -83,7 +96,7 @@ def monthly_histogram_proportion(
                 labels=labels,
                 label_type="center",
                 color=label_colors[i],
-                fontsize="xx-small",
+                fontsize="x-small",
             )
 
     return ax
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_psychrometric.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_psychrometric.py
index b46850d4..53c810b7 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_psychrometric.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_psychrometric.py
@@ -1,9 +1,13 @@
+"""Methods for plotting psychrometric charts."""
+# pylint: disable=E1101
+# pylint: disable=E0401
 import textwrap
 from dataclasses import dataclass, field
 from enum import Enum
-from typing import List, Union
 from warnings import warn
 
+# pylint: disable=E0401
+
 import matplotlib.pyplot as plt
 import numpy as np
 from ladybug.analysisperiod import AnalysisPeriod
@@ -15,7 +19,8 @@
 from matplotlib.colors import Colormap
 from matplotlib.patches import Polygon
 
-from ..ladybug_extension.analysis_period import (
+from ..bhom.analytics import bhom_analytics
+from ..ladybug_extension.analysisperiod import (
     analysis_period_to_datetimes,
     describe_analysis_period,
 )
@@ -23,9 +28,13 @@
 from ..ladybug_extension.location import location_to_string
 
 
+@bhom_analytics()
 def strategy_warning(polygon_name):
     """Give a warning about a polygon not fitting on the chart."""
-    msg = f'Polygon "{polygon_name}" could not fit on the chart.\nTry moving the comfort polygon(s) by changingits criteria.'
+    msg = (
+        f"Polygon '{polygon_name}' could not fit on the chart.\n"
+        "Try moving the comfort polygon(s) by changingits criteria."
+    )
     warn(msg)
 
 
@@ -99,7 +108,7 @@ class PsychrometricPolygons:
     """A collection of polygons for use in a psychrometric chart.
 
     Args:
-        strategies (List[PassiveStrategy], optional):
+        strategies (list[PassiveStrategy], optional):
             A list of passive strategies to include in the chart. Default is None.
         strategy_parameters (PassiveStrategyParameters, optional):
             A PassiveStrategyParameters object to use for the passive strategies.
@@ -118,7 +127,7 @@ class PsychrometricPolygons:
         PsychrometricPolygons: A collection of polygons for use in a psychrometric chart.
     """
 
-    strategies: List[PassiveStrategy] = field(init=True, default_factory=list)
+    strategies: list[PassiveStrategy] = field(init=True, default_factory=list)
     strategy_parameters: PassiveStrategyParameters = field(
         init=True, default=PassiveStrategyParameters()
     )
@@ -135,6 +144,7 @@ def __post_init__(self):
             ), f"PassiveStrategy not of correct type. Use {PassiveStrategy}"
 
 
+@bhom_analytics()
 def psychrometric(
     epw: EPW,
     cmap: Colormap = "viridis",
@@ -164,11 +174,9 @@ def psychrometric(
     if analysis_period is None:
         analysis_period = AnalysisPeriod()
 
-    df = (
-        epw_to_dataframe(epw, include_additional=True)
-        .droplevel([0, 1], axis=1)
-        .loc[analysis_period_to_datetimes(analysis_period)]
-    )
+    df = epw_to_dataframe(epw, include_additional=True).loc[
+        analysis_period_to_datetimes(analysis_period)
+    ]
 
     # create mesh for rendering on chart
     psychart = PsychrometricChart(
@@ -182,7 +190,7 @@ def psychrometric(
     )
 
     def lb_mesh_to_patch_collection(
-        lb_mesh: Mesh2D, values: List[float] = None, cmap=cmap
+        lb_mesh: Mesh2D, values: list[float] = None, cmap=cmap
     ) -> PatchCollection:
         """Convert a Ladybug Mesh2D to a matplotlib PatchCollection."""
         patch_collection = PatchCollection(
@@ -269,7 +277,17 @@ def lb_mesh_to_patch_collection(
 
     # Generate peak cooling summary
     clg_vals = df.loc[df.idxmax()["Dry Bulb Temperature (C)"]]
-    max_dbt_table = f'Peak cooling {clg_vals.name:%b %d %H:%M}\nWS:  {clg_vals["Wind Speed (m/s)"]:>6.1f} m/s\nWD:  {clg_vals["Wind Direction (degrees)"]:>6.1f} deg\nDBT: {clg_vals["Dry Bulb Temperature (C)"]:>6.1f} °C\nWBT: {clg_vals["Wet Bulb Temperature (C)"]:>6.1f} °C\nRH:  {clg_vals["Relative Humidity (%)"]:>6.1f} %\nDPT: {clg_vals["Dew Point Temperature (C)"]:>6.1f} °C\nh:   {clg_vals["Enthalpy (kJ/kg)"]:>6.1f} kJ/kg\nHR:  {clg_vals["Humidity Ratio (fraction)"]:<5.4f} kg/kg'
+    max_dbt_table = (
+        f"Peak cooling {clg_vals.name:%b %d %H:%M}\n"
+        f'WS:  {clg_vals["Wind Speed (m/s)"]:>6.1f} m/s\n'
+        f'WD:  {clg_vals["Wind Direction (degrees)"]:>6.1f} deg\n'
+        f'DBT: {clg_vals["Dry Bulb Temperature (C)"]:>6.1f} °C\n'
+        f'WBT: {clg_vals["Wet Bulb Temperature (C)"]:>6.1f} °C\n'
+        f'RH:  {clg_vals["Relative Humidity (%)"]:>6.1f} %\n'
+        f'DPT: {clg_vals["Dew Point Temperature (C)"]:>6.1f} °C\n'
+        f'h:   {clg_vals["Enthalpy (kJ/kg)"]:>6.1f} kJ/kg\n'
+        f'HR:  {clg_vals["Humidity Ratio (fraction)"]:<5.4f} kg/kg'
+    )
     ax.text(
         0,
         0.98,
@@ -285,7 +303,17 @@ def lb_mesh_to_patch_collection(
 
     # Generate peak heating summary
     htg_vals = df.loc[df.idxmin()["Dry Bulb Temperature (C)"]]
-    min_dbt_table = f'Peak heating {htg_vals.name:%b %d %H:%M}\nWS:  {htg_vals["Wind Speed (m/s)"]:>6.1f} m/s\nWD:  {htg_vals["Wind Direction (degrees)"]:>6.1f} deg\nDBT: {htg_vals["Dry Bulb Temperature (C)"]:>6.1f} °C\nWBT: {htg_vals["Wet Bulb Temperature (C)"]:>6.1f} °C\nRH:  {htg_vals["Relative Humidity (%)"]:>6.1f} %\nDPT: {htg_vals["Dew Point Temperature (C)"]:>6.1f} °C\nh:   {htg_vals["Enthalpy (kJ/kg)"]:>6.1f} kJ/kg\nHR:  {htg_vals["Humidity Ratio (fraction)"]:<5.4f} kg/kg'
+    min_dbt_table = (
+        f"Peak heating {htg_vals.name:%b %d %H:%M}\n"
+        f'WS:  {htg_vals["Wind Speed (m/s)"]:>6.1f} m/s\n'
+        f'WD:  {htg_vals["Wind Direction (degrees)"]:>6.1f} deg\n'
+        f'DBT: {htg_vals["Dry Bulb Temperature (C)"]:>6.1f} °C\n'
+        f'WBT: {htg_vals["Wet Bulb Temperature (C)"]:>6.1f} °C\n'
+        f'RH:  {htg_vals["Relative Humidity (%)"]:>6.1f} %\n'
+        f'DPT: {htg_vals["Dew Point Temperature (C)"]:>6.1f} °C\n'
+        f'h:   {htg_vals["Enthalpy (kJ/kg)"]:>6.1f} kJ/kg\n'
+        f'HR:  {htg_vals["Humidity Ratio (fraction)"]:<5.4f} kg/kg'
+    )
     ax.text(
         0,
         0.8,
@@ -301,7 +329,17 @@ def lb_mesh_to_patch_collection(
 
     # Generate max HumidityRatio summary
     hr_vals = df.loc[df.idxmin()["Humidity Ratio (fraction)"]]
-    max_hr_table = f'Peak humidity ratio {hr_vals.name:%b %d %H:%M}\nWS:  {hr_vals["Wind Speed (m/s)"]:>6.1f} m/s\nWD:  {hr_vals["Wind Direction (degrees)"]:>6.1f} deg\nDBT: {hr_vals["Dry Bulb Temperature (C)"]:>6.1f} °C\nWBT: {hr_vals["Wet Bulb Temperature (C)"]:>6.1f} °C\nRH:  {hr_vals["Relative Humidity (%)"]:>6.1f} %\nDPT: {hr_vals["Dew Point Temperature (C)"]:>6.1f} °C\nh:   {hr_vals["Enthalpy (kJ/kg)"]:>6.1f} kJ/kg\nHR:  {hr_vals["Humidity Ratio (fraction)"]:<5.4f} kg/kg'
+    max_hr_table = (
+        f"Peak heating {hr_vals.name:%b %d %H:%M}\n"
+        f'WS:  {hr_vals["Wind Speed (m/s)"]:>6.1f} m/s\n'
+        f'WD:  {hr_vals["Wind Direction (degrees)"]:>6.1f} deg\n'
+        f'DBT: {hr_vals["Dry Bulb Temperature (C)"]:>6.1f} °C\n'
+        f'WBT: {hr_vals["Wet Bulb Temperature (C)"]:>6.1f} °C\n'
+        f'RH:  {hr_vals["Relative Humidity (%)"]:>6.1f} %\n'
+        f'DPT: {hr_vals["Dew Point Temperature (C)"]:>6.1f} °C\n'
+        f'h:   {hr_vals["Enthalpy (kJ/kg)"]:>6.1f} kJ/kg\n'
+        f'HR:  {hr_vals["Humidity Ratio (fraction)"]:<5.4f} kg/kg'
+    )
     ax.text(
         0,
         0.62,
@@ -317,7 +355,17 @@ def lb_mesh_to_patch_collection(
 
     # Generate max enthalpy summary
     enth_vals = df.loc[df.idxmin()["Enthalpy (kJ/kg)"]]
-    max_enthalpy_table = f'Peak enthalpy {enth_vals.name:%b %d %H:%M}\nWS:  {enth_vals["Wind Speed (m/s)"]:>6.1f} m/s\nWD:  {enth_vals["Wind Direction (degrees)"]:>6.1f} deg\nDBT: {enth_vals["Dry Bulb Temperature (C)"]:>6.1f} °C\nWBT: {enth_vals["Wet Bulb Temperature (C)"]:>6.1f} °C\nRH:  {enth_vals["Relative Humidity (%)"]:>6.1f} %\nDPT: {enth_vals["Dew Point Temperature (C)"]:>6.1f} °C\nh:   {enth_vals["Enthalpy (kJ/kg)"]:>6.1f} kJ/kg\nHR:  {enth_vals["Humidity Ratio (fraction)"]:<5.4f} kg/kg'
+    max_enthalpy_table = (
+        f"Peak enthalpy {enth_vals.name:%b %d %H:%M}\n"
+        f'WS:  {enth_vals["Wind Speed (m/s)"]:>6.1f} m/s\n'
+        f'WD:  {enth_vals["Wind Direction (degrees)"]:>6.1f} deg\n'
+        f'DBT: {enth_vals["Dry Bulb Temperature (C)"]:>6.1f} °C\n'
+        f'WBT: {enth_vals["Wet Bulb Temperature (C)"]:>6.1f} °C\n'
+        f'RH:  {enth_vals["Relative Humidity (%)"]:>6.1f} %\n'
+        f'DPT: {enth_vals["Dew Point Temperature (C)"]:>6.1f} °C\n'
+        f'h:   {enth_vals["Enthalpy (kJ/kg)"]:>6.1f} kJ/kg\n'
+        f'HR:  {enth_vals["Humidity Ratio (fraction)"]:<5.4f} kg/kg'
+    )
     ax.text(
         0,
         0.44,
@@ -332,7 +380,10 @@ def lb_mesh_to_patch_collection(
     )
 
     # add legend
-    keys = "WS: Wind speed | WD: Wind direction | DBT: Dry-bulb temperature | WBT: Wet-bulb temperature\nRH: Relative humidity | DPT: Dew-point temperature | h: Enthalpy | HR: Humidity ratio"
+    keys = (
+        "WS: Wind speed | WD: Wind direction | DBT: Dry-bulb temperature | WBT: Wet-bulb temperature\n"
+        "RH: Relative humidity | DPT: Dew-point temperature | h: Enthalpy | HR: Humidity ratio"
+    )
     ax.text(
         1,
         -0.05,
@@ -356,8 +407,8 @@ def lb_mesh_to_patch_collection(
         polygon_names = []
 
         def line_objs_to_vertices(
-            lines: List[Union[Polyline2D, LineSegment2D]]
-        ) -> List[List[float]]:
+            lines: list[Polyline2D | LineSegment2D],
+        ) -> list[list[float]]:
             # ensure input list is flat
             lines = [
                 v
@@ -399,12 +450,12 @@ def process_polygon(polygon_name, polygon):
 
             return polygon_name, strategy_poly, dat
 
-        def polygon_area(xs: List[float], ys: List[float]) -> List[float]:
+        def polygon_area(xs: list[float], ys: list[float]) -> list[float]:
             """https://en.wikipedia.org/wiki/Centroid#Of_a_polygon"""
             # https://stackoverflow.com/a/30408825/7128154
             return 0.5 * (np.dot(xs, np.roll(ys, 1)) - np.dot(ys, np.roll(xs, 1)))
 
-        def polygon_centroid(xs: List[float], ys: List[float]) -> List[float]:
+        def polygon_centroid(xs: list[float], ys: list[float]) -> list[float]:
             """https://en.wikipedia.org/wiki/Centroid#Of_a_polygon"""
             xy = np.array([xs, ys])
             c = np.dot(
@@ -600,7 +651,8 @@ def merge_polygon_data(poly_data):
 
         if PassiveStrategy.PASSIVE_SOLAR_HEATING in psychro_polygons.strategies:
             warn(
-                f"{PassiveStrategy.PASSIVE_SOLAR_HEATING} assumes radiation from skylights only, using global horizontal radiation."
+                f"{PassiveStrategy.PASSIVE_SOLAR_HEATING} assumes radiation from "
+                "skylights only, using global horizontal radiation."
             )
             bal_t = (
                 psychro_polygons.strategy_parameters.balance_temperature
@@ -668,10 +720,12 @@ def merge_polygon_data(poly_data):
         comfort_text = "\n".join(comfort_text)
         settings_text = "\n".join(
             [
+                # pylint: disable=C0301
                 f'MRT: {"DBT" if psychro_polygons.mean_radiant_temperature is None else psychro_polygons.mean_radiant_temperature}',
                 f" WS: {psychro_polygons.air_speed}m/s",
                 f"CLO: {psychro_polygons.clo_value}",
                 f"MET: {psychro_polygons.metabolic_rate}",
+                # pylint: enable=C0301
             ]
         )
         ax.text(
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_radiant_cooling_potential.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_radiant_cooling_potential.py
new file mode 100644
index 00000000..a22b0dcb
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_radiant_cooling_potential.py
@@ -0,0 +1,92 @@
+"""Method for plotting radiant cooling potential."""
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from ._diurnal import diurnal, textwrap
+
+
+def radiant_cooling_potential(
+    dpt: pd.Series, ax: plt.Axes = None, **kwargs
+) -> plt.Axes:
+    """Plot radiant cooling potential.
+
+    Args:
+        dpt (pd.Series):
+            A pandas series of dew point temperature.
+        ax (plt.Axes, optional):
+            The matplotlib axes to plot the figure on. Defaults to None.
+        **kwargs:
+            Additional keyword arguments to pass to matplotlib.pyplot.plot.
+
+    Returns:
+        plt.Axes: The matplotlib axes.
+    """
+
+    if ax is None:
+        ax = plt.gca()
+
+    diurnal(series=dpt, ax=ax, period="monthly", zorder=2, **kwargs)
+
+    # ad vertical lines
+    for i in range(12):
+        ax.axvline(i * 24, ls=":", c="k")
+
+    _temp = (
+        dpt.groupby([dpt.index.month, dpt.index.day, dpt.index.hour])
+        .mean()
+        .reorder_levels([0, 2, 1])
+        .unstack()
+        .reset_index(drop=True)
+        .T
+    )
+    ax.scatter(
+        [_temp.columns.values] * 31,
+        _temp.values,
+        s=0.5,
+        c=kwargs.get("color", "#907090"),
+        zorder=1,
+        alpha=0.3,
+    )
+
+    ax.axhline(20, ls="--", c="k", lw=1)
+    ax.annotate(
+        textwrap.fill("Underfloor cooling (20°C)", 16),
+        xy=(288, 20),
+        xycoords="data",
+        xytext=(10, 10),
+        textcoords="offset points",
+        arrowprops={
+            "arrowstyle": "->",
+            "connectionstyle": "angle,angleA=0,angleB=90,rad=10",
+        },
+    )
+
+    ax.axhline(18, ls="--", c="k", lw=1)
+    ax.annotate(
+        textwrap.fill("Thermally activated building structure (TABS) (18°C)", 20),
+        xy=(288, 18),
+        xycoords="data",
+        xytext=(10, -45),
+        textcoords="offset points",
+        arrowprops={
+            "arrowstyle": "->",
+            "connectionstyle": "angle,angleA=90,angleB=0,rad=10",
+        },
+    )
+
+    ax.axhline(17, ls="--", c="k", lw=1)
+    ax.annotate(
+        textwrap.fill("Chilled beams/ ceiling (17°C)", 16),
+        xy=(288, 17),
+        xycoords="data",
+        xytext=(10, -90),
+        textcoords="offset points",
+        arrowprops={
+            "arrowstyle": "->",
+            "connectionstyle": "angle,angleA=0,angleB=90,rad=10",
+        },
+    )
+    ax.set_title("Radiant cooling potential")
+
+    return ax
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_skymatrix.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_skymatrix.py
index dc37e52e..bbf6837b 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_skymatrix.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_skymatrix.py
@@ -1,7 +1,10 @@
+"""Methods for plotting sky matrices."""
+# pylint: disable=E0401
 import subprocess
 import tempfile
 from pathlib import Path
-from typing import List
+
+# pylint: enable=E0401
 
 import matplotlib.patches as mpatches
 import matplotlib.pyplot as plt
@@ -12,12 +15,14 @@
 from ladybug.wea import Wea
 from matplotlib.collections import PatchCollection
 
-from ..external_comfort import HBR_FOLDERS
-from ..ladybug_extension.analysis_period import describe_analysis_period
+from honeybee_radiance.config import folders as hbr_folders
+from ..bhom.analytics import bhom_analytics
+from ..ladybug_extension.analysisperiod import describe_analysis_period
 from ..ladybug_extension.epw import EPW
 from ..ladybug_extension.location import location_to_string
 
 
+@bhom_analytics()
 def skymatrix(
     epw: EPW,
     ax: plt.Axes = None,
@@ -65,7 +70,7 @@ def skymatrix(
     wea_file = wea.write(wea_path.as_posix())
 
     # run gendaymtx
-    gendaymtx_exe = (Path(HBR_FOLDERS.radbin_path) / "gendaymtx.exe").as_posix()
+    gendaymtx_exe = (Path(hbr_folders.radbin_path) / "gendaymtx.exe").as_posix()
     cmds = [gendaymtx_exe, "-m", str(density), "-d", "-O1", "-A", wea_file]
     with subprocess.Popen(cmds, stdout=subprocess.PIPE, shell=True) as process:
         stdout = process.communicate()
@@ -75,7 +80,7 @@ def skymatrix(
         stdout = process.communicate()
     diff_data_str = stdout[0].decode("ascii")
 
-    def _broadband_rad(data_str: str) -> List[float]:
+    def _broadband_rad(data_str: str) -> list[float]:
         _ = data_str.split("\r\n")[:8]
         data = np.array(
             [[float(j) for j in i.split()] for i in data_str.split("\r\n")[8:]][1:-1]
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_sunpath.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_sunpath.py
index 281e4175..09441d8f 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_sunpath.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_sunpath.py
@@ -1,5 +1,4 @@
-from typing import Union
-
+"""Methods for plotting sun-paths."""
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -10,7 +9,8 @@
 from ladybug.sunpath import Sunpath
 from matplotlib.colors import BoundaryNorm, Colormap
 
-from ..ladybug_extension.analysis_period import (
+from ..bhom.analytics import bhom_analytics
+from ..ladybug_extension.analysisperiod import (
     analysis_period_to_datetimes,
     describe_analysis_period,
 )
@@ -18,12 +18,13 @@
 from ..ladybug_extension.location import location_to_string
 
 
+@bhom_analytics()
 def sunpath(
     location: Location,
     ax: plt.Axes = None,
     analysis_period: AnalysisPeriod = AnalysisPeriod(),
     data_collection: HourlyContinuousCollection = None,
-    cmap: Union[Colormap, str] = "viridis",
+    cmap: Colormap | str = "viridis",
     norm: BoundaryNorm = None,
     sun_size: float = 10,
     show_grid: bool = True,
@@ -68,9 +69,7 @@ def sunpath(
     day_suns = []
     for month in [6, 9, 12]:
         date = pd.to_datetime(f"2017-{month:02d}-21")
-        day_idx = pd.date_range(
-            date, date + pd.Timedelta(hours=24), freq="1T", closed="left"
-        )
+        day_idx = pd.date_range(date, date + pd.Timedelta(hours=24), freq="1T")
         _ = []
         for idx in day_idx:
             s = sunpath_obj.calculate_sun_from_date_time(idx)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_timeseries.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_timeseries.py
index 5a54d134..a99e02cb 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_timeseries.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_timeseries.py
@@ -1,17 +1,21 @@
-from datetime import datetime
-from typing import Tuple
+"""Mwethods for plotting time-indexed data."""
+
+from datetime import datetime  # pylint: disable=E0401
+
 
 import matplotlib.pyplot as plt
 import pandas as pd
 
+from ..bhom.analytics import bhom_analytics
 from ..helpers import validate_timeseries
 
 
+@bhom_analytics()
 def timeseries(
     series: pd.Series,
     ax: plt.Axes = None,
-    xlims: Tuple[datetime] = None,
-    ylims: Tuple[datetime] = None,
+    xlims: tuple[datetime] = None,
+    ylims: tuple[datetime] = None,
     **kwargs,
 ) -> plt.Axes:
     """Create a timeseries plot of a pandas Series.
@@ -21,9 +25,9 @@ def timeseries(
             The pandas Series to plot. Must have a datetime index.
         ax (plt.Axes, optional):
             An optional plt.Axes object to populate. Defaults to None, which creates a new plt.Axes object.
-        xlims (Tuple[datetime], optional):
+        xlims (tuple[datetime], optional):
             Set the x-limits. Defaults to None.
-        ylims (Tuple[datetime], optional):
+        ylims (tuple[datetime], optional):
             Set the y-limits. Defaults to None.
         **kwargs:
             Additional keyword arguments to pass to the plt.plot() function.
@@ -40,7 +44,8 @@ def timeseries(
 
     ax.plot(series.index, series.values, **kwargs)  ## example plot here
 
-    # TODO - add cmap arg to color line by y value -  https://matplotlib.org/stable/gallery/lines_bars_and_markers/multicolored_line.html
+    # TODO - add cmap arg to color line by y value -
+    # https://matplotlib.org/stable/gallery/lines_bars_and_markers/multicolored_line.html
 
     if xlims is None:
         ax.set_xlim(series.index.min(), series.index.max())
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_utci.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_utci.py
index 917ffec9..55b1766a 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_utci.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_utci.py
@@ -1,8 +1,10 @@
+"""Plot methods for UTCI datasets"""
+# pylint: disable=C0302
+# pylint: disable=E0401
 import calendar
 import textwrap
-import warnings
-from calendar import month_abbr
-from typing import List, Tuple
+
+# pylint enable=E0401
 
 import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
@@ -17,95 +19,35 @@
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 from scipy.interpolate import make_interp_spline
 
-from ..categorical.categories import UTCI_DEFAULT_CATEGORIES, Categorical, ComfortClass
-from ..external_comfort.utci.postprocess import distance_to_comfortable
+from ..bhom.analytics import bhom_analytics
+from ..categorical.categories import (
+    UTCI_DEFAULT_CATEGORIES,
+    CategoricalComfort,
+)
 from ..ladybug_extension.datacollection import collection_to_series
 from ._heatmap import heatmap
 from .colormaps import UTCI_DIFFERENCE_COLORMAP
-from .utilities import (
-    add_bar_labels,
-    average_color,
-    colormap_sequential,
-    contrasting_color,
-    lighten_color,
-)
-
-
-def utci_distance_to_comfortable(
-    utci_collection: HourlyContinuousCollection,
-    ax: plt.Axes = None,
-    comfort_thresholds: Tuple[float] = (9, 26),
-    distance_to_comfort_band_centroid: bool = False,
-    **kwargs,
-) -> plt.Axes:
-    """Plot the distance (in C) to comfortable for a given Ladybug HourlyContinuousCollection
-        containing UTCI values - using a different, less funky colour-scheme.
-
-    Args:
-        utci_collection (HourlyContinuousCollection):
-            A Ladybug Universal Thermal Climate Index HourlyContinuousCollection object.
-        ax (plt.Axes, optional):
-            The matplotlib Axes to plot on. Defaults to None which uses the current Axes.
-        comfort_thresholds (List[float], optional):
-            The comfortable band of UTCI temperatures. Defaults to [9, 26].
-        distance_to_comfort_band_centroid (bool, optional):
-            If True, the distance to the centroid of the comfort band is plotted. If False, the
-            distance to the edge of the comfort band is plotted. Defaults to False.
-        **kwargs (Dict[str, Any], optional):
-            Additional keyword arguments to pass to the matplotlib plotting function.
-    Returns:
-        plt.Axes:
-            A matplotlib Axes object.
-    """
-
-    if len(comfort_thresholds) != 2:
-        raise ValueError("comfort_thresholds must be a list of length 2.")
-
-    if len(set(comfort_thresholds)) != 2:
-        raise ValueError("comfort_thresholds must contain two unique values.")
-
-    if comfort_thresholds[0] > comfort_thresholds[1]:
-        warnings.warn("comfort_thresholds are not increasing. Swapping the values.")
-
-    if not isinstance(
-        utci_collection.header.data_type, LB_UniversalThermalClimateIndex
-    ):
-        raise ValueError("This method only works for UTCI data.")
-
-    if ax is None:
-        ax = plt.gca()
-
-    new_collection = distance_to_comfortable(
-        utci_values=utci_collection,
-        comfort_thresholds=comfort_thresholds,
-        distance_to_comfort_band_centroid=distance_to_comfort_band_centroid,
-    )
-    ti = collection_to_series(new_collection).name
-    return heatmap(
-        collection_to_series(new_collection),
-        cmap=kwargs.get("cmap", colormap_sequential("#00A9E0", "w", "#ba000d")),
-        title="\n".join([i for i in [kwargs.pop("title", None), ti] if i is not None]),
-        vmin=kwargs.get("vmin", -20),
-        vmax=kwargs.get("vmax", 20),
-    )
+from .utilities import contrasting_color, lighten_color
 
 
+@bhom_analytics()
 def utci_comfort_band_comparison(
-    utci_collections: Tuple[HourlyContinuousCollection],
+    utci_collections: tuple[HourlyContinuousCollection],
     ax: plt.Axes = None,
-    identifiers: Tuple[str] = None,
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
+    identifiers: tuple[str] = None,
+    utci_categories: CategoricalComfort = UTCI_DEFAULT_CATEGORIES,
     density: bool = True,
     **kwargs,
 ) -> plt.Axes:
-    """Create a proportional bar chart showing how different UTCI collections compare in terms of time within each comfort band.
+    """Create a proportional bar chart showing how different UTCI collections
+    compare in terms of time within each comfort band.
 
     Args:
-        utci_collections (List[HourlyContinuousCollection]):
+        utci_collections (list[HourlyContinuousCollection]):
             A list of UTCI collections.
         ax (plt.Axes, optional):
             The matplotlib Axes to plot on. Defaults to None which uses the current Axes.
-        identifiers (List[str], optional):
+        identifiers (list[str], optional):
             A list of names to give each collection. Defaults to None.
         utci_categories (Categories, optional):
             The UTCI categories to use. Defaults to UTCI_DEFAULT_CATEGORIES.
@@ -197,6 +139,7 @@ def utci_comfort_band_comparison(
     return ax
 
 
+@bhom_analytics()
 def utci_day_comfort_metrics(
     utci: pd.Series,
     dbt: pd.Series,
@@ -265,12 +208,16 @@ def utci_day_comfort_metrics(
     axes[0].set_ylabel("UTCI")
     (b,) = axes[1].plot(dbt.loc[dt], c="red", alpha=0.75, label="DBT", ls="--")
     axes[1].set_ylabel("DBT")
+    axes[1].grid(False)
     (c,) = axes[2].plot(mrt.loc[dt], c="orange", alpha=0.75, label="MRT", ls="--")
     axes[2].set_ylabel("MRT")
+    axes[2].grid(False)
     (d,) = axes[3].plot(rh.loc[dt], c="blue", alpha=0.75, label="RH", ls="--")
     axes[3].set_ylabel("RH")
+    axes[3].grid(False)
     (e,) = axes[4].plot(ws.loc[dt], c="green", alpha=0.75, label="WS", ls="--")
     axes[4].set_ylabel("WS")
+    axes[4].grid(False)
 
     axes[0].spines["right"].set_visible(False)
 
@@ -292,13 +239,14 @@ def utci_day_comfort_metrics(
     return ax
 
 
+@bhom_analytics()
 def utci_comparison_diurnal_day(
-    utci_collections: List[HourlyContinuousCollection],
+    utci_collections: list[HourlyContinuousCollection],
     ax: plt.Axes = None,
     month: int = 6,
-    collection_ids: List[str] = None,
+    collection_ids: list[str] = None,
     agg: str = "mean",
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
+    utci_categories: CategoricalComfort = UTCI_DEFAULT_CATEGORIES,
     show_legend: bool = True,
     categories_in_legend: bool = True,
     **kwargs,
@@ -306,13 +254,13 @@ def utci_comparison_diurnal_day(
     """Plot a set of UTCI collections on a single figure for monthly diurnal periods.
 
     Args:
-        utci_collections (List[HourlyContinuousCollection]):
+        utci_collections (list[HourlyContinuousCollection]):
             A list of UTCI collections.
         ax (plt.Axes, optional):
             The matplotlib Axes to plot on. Defaults to None which uses the current Axes.
         month (int, optional):
             The month to get the typical day from. Default is 6.
-        collection_ids (List[str], optional):
+        collection_ids (list[str], optional):
             A list of descriptions for each of the input collections. Defaults to None.
         agg (str, optional):
             How to generate the "typical" day. Defualt is "mean" which uses the mean for each timestep in that month.
@@ -324,7 +272,7 @@ def utci_comparison_diurnal_day(
             If True, add the UTCI categories to the legend. Defaults to True.
         **kwargs:
             Additional keyword arguments to pass to the matplotlib plot function.
-            ylims (List[float], optional):
+            ylims (list[float], optional):
                 The y-axis limits. Defaults to None which just uses the min/ax of the fiven collections.
 
     Returns:
@@ -360,7 +308,7 @@ def utci_comparison_diurnal_day(
         [collection_to_series(i) for i in utci_collections], axis=1, keys=collection_ids
     )
     ylim = kwargs.pop("ylim", [df.min().min(), df.max().max()])
-    df_agg = df.groupby([df.index.month, df.index.hour], axis=0).agg(agg).loc[month]
+    df_agg = df.groupby([df.index.month, df.index.hour]).agg(agg).loc[month]
     df_agg.index = range(24)
     # add a final value to close the day
     df_agg.loc[24] = df_agg.loc[0]
@@ -420,6 +368,7 @@ def utci_comparison_diurnal_day(
     return ax
 
 
+@bhom_analytics()
 def utci_heatmap_difference(
     utci_collection1: HourlyContinuousCollection,
     utci_collection2: HourlyContinuousCollection,
@@ -470,12 +419,13 @@ def utci_heatmap_difference(
     )
 
 
+@bhom_analytics()
 def utci_pie(
     utci_collection: HourlyContinuousCollection,
     ax: plt.Axes = None,
     show_legend: bool = True,
     show_values: bool = False,
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
+    utci_categories: CategoricalComfort = UTCI_DEFAULT_CATEGORIES,
     **kwargs,
 ) -> plt.Axes:
     """Create a figure showing the UTCI proportion for the given analysis period.
@@ -546,14 +496,15 @@ def func(pct, _):
     return ax
 
 
+@bhom_analytics()
 def utci_journey(
-    utci_values: Tuple[float],
+    utci_values: tuple[float],
     ax: plt.Axes = None,
-    names: Tuple[str] = None,
+    names: tuple[str] = None,
     curve: bool = False,
     show_legend: bool = False,
     show_grid: bool = False,
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
+    utci_categories: CategoricalComfort = UTCI_DEFAULT_CATEGORIES,
     **kwargs,
 ) -> plt.Axes:
     """Create a figure showing the pseudo-journey between different UTCI conditions at a
@@ -564,7 +515,7 @@ def utci_journey(
             A list of UTCI values.
         ax (plt.Axes, optional):
             The matplotlib Axes to plot on. Defaults to None which uses the current Axes.
-        names (List[str], optional):
+        names (list[str], optional):
             A list of names to label each value with. Defaults to None.
         curve (bool, optional):
             Whether to plot the pseudo-journey as a spline. Defaults to False.
@@ -680,217 +631,10 @@ def utci_journey(
     return ax
 
 
-def utci_heatmap(
-    utci_collection: HourlyContinuousCollection,
-    ax: plt.Axes = None,
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
-    **kwargs,
-) -> plt.Axes:
-    """Create a heatmap showing the annual hourly UTCI for this HourlyContinuousCollection.
-
-    Args:
-        utci_collection (HourlyContinuousCollection):
-            An HourlyContinuousCollection containing UTCI.
-        ax (plt.Axes, optional):
-            A matplotlib Axes object to plot on. Defaults to None.
-        utci_categories (Categories, optional):
-            A Categories object with colors, ranges and limits. Defaults to UTCI_DEFAULT_CATEGORIES.
-        **kwargs:
-            Additional keyword arguments to pass to the heatmap function.
-
-    Returns:
-        plt.Axes:
-            A matplotlib Axes object.
-    """
-
-    return heatmap(
-        collection_to_series(utci_collection),
-        ax=ax,
-        cmap=utci_categories.cmap,
-        norm=utci_categories.norm,
-        extend="both",
-        **kwargs,
-    )
-
-
-def utci_monthly_histogram(
-    utci_collection: HourlyContinuousCollection,
-    ax: plt.Axes = None,
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
-    show_labels: bool = False,
-    show_summary: bool = False,
-    show_legend: bool = False,
-) -> plt.Axes:
-    """Create a stacked bar chart showing the monthly distribution of UTCI values.
-
-    Args:
-        utci_collection (HourlyContinuousCollection):
-            An HourlyContinuousCollection containing UTCI.
-        ax (plt.Axes, optional):
-            A matplotlib Axes object to plot on. Defaults to None.
-        utci_categories (Categories, optional):
-            A Categories object with colors, ranges and limits. Defaults to UTCI_CATEGORIES.
-        show_labels (bool, optional):
-            Set to True to show the UTCI category labels on the plot. Defaults to False.
-        show_summary (bool, optional):
-            Set to True to show the UTCI category summary on the plot. Defaults to False.
-        show_legend (bool, optional):
-            Set to True to show the UTCI category legend on the plot. Defaults to False.
-
-    Returns:
-        plt.Axes:
-            A matplotlib Axes object.
-    """
-
-    if not isinstance(
-        utci_collection.header.data_type, LB_UniversalThermalClimateIndex
-    ):
-        raise ValueError("Input collection is not a UTCI collection.")
-
-    series = collection_to_series(utci_collection)
-
-    if ax is None:
-        ax = plt.gca()
-
-    t = utci_categories.timeseries_summary_monthly(series, density=True).T
-    t.columns = [month_abbr[i] for i in t.columns]
-    t.T.plot.bar(
-        ax=ax,
-        stacked=True,
-        color=utci_categories.colors,
-        legend=False,
-        width=1,
-    )
-    ax.set_xlabel(None)
-    ax.set_xlim(-0.5, 11.5)
-    ax.set_ylim(0, 1)
-    ax.set_xticklabels(t.columns, ha="center", rotation=0)
-    ax.yaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0))
-
-    if show_summary:
-        # Add header percentages for bar plot - and check that ComfortClass is available on object using that if it is
-        try:
-            if len(utci_categories.comfort_classes) != len(
-                utci_categories.interval_index
-            ):
-                raise ValueError(
-                    "Monkey-patched comfort_class attributes are not the same length as the categories within this Category object."
-                )
-            if len(set(utci_categories.comfort_classes)) != 3:
-                raise ValueError(
-                    "utci_categories.comfort_classes must have exactly 3 unique values to be used in this method."
-                )
-            # get avg "cold", "hot", "comfortable" colors
-            cold_color = average_color(
-                [
-                    utci_categories.colors[n]
-                    for n, i in enumerate(utci_categories.comfort_classes)
-                    if i.value == ComfortClass.TOO_COLD.value
-                ]
-            )
-            hot_color = average_color(
-                [
-                    utci_categories.colors[n]
-                    for n, i in enumerate(utci_categories.comfort_classes)
-                    if i.value == ComfortClass.TOO_HOT.value
-                ]
-            )
-            comfortable_color = average_color(
-                [
-                    utci_categories.colors[n]
-                    for n, i in enumerate(utci_categories.comfort_classes)
-                    if i.value == ComfortClass.COMFORTABLE.value
-                ]
-            )
-
-            cold_stress = []
-            no_stress = []
-            heat_stress = []
-            for n, col in t.iteritems():
-                cs = 0
-                ns = 0
-                hs = 0
-                for nn, i in enumerate(col):
-                    if (
-                        utci_categories.comfort_classes[nn].value
-                        == ComfortClass.TOO_COLD.value
-                    ):
-                        cs += i
-                    elif (
-                        utci_categories.comfort_classes[nn].value
-                        == ComfortClass.COMFORTABLE.value
-                    ):
-                        ns += i
-                    elif (
-                        utci_categories.comfort_classes[nn].value
-                        == ComfortClass.TOO_HOT.value
-                    ):
-                        hs += i
-                    else:
-                        raise ValueError("How'd you get here?")
-                cold_stress.append(cs)
-                no_stress.append(ns)
-                heat_stress.append(hs)
-        except AttributeError:
-            warnings.warn(
-                'No ComfortClass found on given "utci_categories", defaulting to regex lookup of hot/cold names for categories to determine comfort.'
-            )
-            cold_stress = t.T.filter(regex="(?i)cool|cold|chill").sum(axis=1)
-            heat_stress = t.T.filter(regex="(?i)heat|hot").sum(axis=1)
-            no_stress = 1 - cold_stress - heat_stress
-
-            cold_color = "#3C65AF"
-            hot_color = "#C31F25"
-            comfortable_color = "#2EB349"
-
-        for n, (i, j, k) in enumerate(zip(*[cold_stress, no_stress, heat_stress])):
-            ax.text(
-                n,
-                1.02,
-                f"{i:0.1%}",
-                va="bottom",
-                ha="center",
-                color=cold_color,
-                fontsize="small",
-            )
-            ax.text(
-                n,
-                1.02,
-                f"{j:0.1%}\n",
-                va="bottom",
-                ha="center",
-                color=comfortable_color,
-                fontsize="small",
-            )
-            ax.text(
-                n,
-                1.02,
-                f"{k:0.1%}\n\n",
-                va="bottom",
-                ha="center",
-                color=hot_color,
-                fontsize="small",
-            )
-
-    if show_labels:
-        add_bar_labels(ax, orientation="vertical", threshold=0.05)
-
-    if show_legend:
-        handles, labels = ax.get_legend_handles_labels()
-        ax.legend(
-            handles[::-1],
-            labels[::-1],
-            title=utci_categories.name,
-            bbox_to_anchor=(1, 0.5),
-            loc="center left",
-        )
-
-    return ax
-
-
+@bhom_analytics()
 def utci_heatmap_histogram(
     utci_collection: HourlyContinuousCollection,
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
+    utci_categories: CategoricalComfort = UTCI_DEFAULT_CATEGORIES,
     show_colorbar: bool = True,
     **kwargs,
 ) -> Figure:
@@ -932,29 +676,17 @@ def utci_heatmap_histogram(
     histogram_ax = fig.add_subplot(spec[1, 0])
 
     # Add heatmap
-    utci_heatmap(
-        utci_collection,
-        ax=heatmap_ax,
-        utci_categories=utci_categories,
-        show_colorbar=False,
-        **kwargs,
-    )
+    utci_categories.annual_heatmap(series, ax=heatmap_ax, show_colorbar=False, **kwargs)
 
     # Add stacked plot
-    utci_monthly_histogram(
-        utci_collection,
-        ax=histogram_ax,
-        utci_categories=utci_categories,
-        show_labels=False,
-        show_legend=False,
-        show_summary=True,
-        **kwargs,
+    utci_categories.annual_monthly_histogram(
+        series=series, ax=histogram_ax, show_labels=True
     )
 
     if show_colorbar:
         # add colorbar
         divider = make_axes_locatable(histogram_ax)
-        colorbar_ax = divider.append_axes("bottom", size="20%", pad=0.65)
+        colorbar_ax = divider.append_axes("bottom", size="20%", pad=0.7)
         cb = fig.colorbar(
             mappable=heatmap_ax.get_children()[0],
             cax=colorbar_ax,
@@ -978,11 +710,11 @@ def utci_heatmap_histogram(
 
             colorbar_ax.text(
                 position,
-                1,
+                1.05,
                 textwrap.fill(bin_name, 11),
                 ha=ha,
                 va="bottom",
-                size="small",
+                fontsize="x-small",
                 # transform=colorbar_ax.transAxes,
             )
 
@@ -992,10 +724,11 @@ def utci_heatmap_histogram(
     return fig
 
 
+@bhom_analytics()
 def utci_histogram(
     utci_collection: HourlyContinuousCollection,
     ax: plt.Axes = None,
-    utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
+    utci_categories: CategoricalComfort = UTCI_DEFAULT_CATEGORIES,
     show_labels: bool = False,
     **kwargs,
 ) -> plt.Axes:
@@ -1080,6 +813,13 @@ def utci_histogram(
     ax.set_xlim(xlim)
     ax.set_xlabel(series.name)
 
+    # set ylims
+    ylim = kwargs.pop(
+        "ylim",
+        ax.get_ylim(),
+    )
+    ax.set_ylim(ylim)
+
     # get positions for percentage labels
     if show_labels:
         counts = utci_categories.value_counts(series, density=False)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_wind.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_wind.py
deleted file mode 100644
index 7ae56947..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_wind.py
+++ /dev/null
@@ -1,797 +0,0 @@
-import calendar
-import warnings
-from typing import List, Tuple, Union
-
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import matplotlib.ticker as mticker
-import numpy as np
-import pandas as pd
-from matplotlib.cm import ScalarMappable
-from matplotlib.colors import Colormap, Normalize
-from scipy import stats
-
-from ..helpers import (
-    angle_from_north,
-    cardinality,
-    rolling_window,
-    wind_direction_average,
-)
-from ..wind.direction_bins import DirectionBins
-from .utilities import annotate_imshow, contrasting_color
-
-# TODO - add Climate Consultant-style "wind wheel" plot here (http://2.bp.blogspot.com/-F27rpZL4VSs/VngYxXsYaTI/AAAAAAAACAc/yoGXmk13uf8/s1600/CC-graphics%2B-%2BWind%2BWheel.jpg)
-
-
-def windrose(
-    wind_directions: List[float],
-    data: List[float] = None,
-    ax: plt.Axes = None,
-    direction_bins: DirectionBins = DirectionBins(),
-    data_bins: Union[int, List[float]] = 11,
-    include_legend: bool = True,
-    **kwargs,
-) -> plt.Axes:
-    """Plot a windrose for a collection of wind speeds and directions.
-
-    Args:
-        wind_directions (List[float]):
-            A collection of wind-directions.
-        data (List[float]):
-            A collection of direction-associated data.
-        ax (plt.Axes, optional):
-            The matplotlib Axes to plot on. Defaults to None which uses the current Axes.
-        direction_bins (DirectionBins, optional):
-            A DirectionBins object.
-        data_bins (Union[int, List[float]], optional):
-            Bins to sort data into. Defaults to 11 bins between the min/max data values.
-        include_legend (bool, optional):
-            Set to True to include the legend. Defaults to True.
-        **kwargs:
-            Additional keyword arguments to pass to the function.
-            data_unit (str, optional):
-                The unit of the data to add to the legend. Defaults to None.
-            ylim (Tuple[float], optional):
-                The minimum and maximum values for the y-axis. Defaults to None.
-            cmap (str, optional):
-                The name of the colormap to use. Defaults to "viridis".
-
-    Returns:
-        plt.Figure:
-            A Figure object.
-    """
-
-    if ax is None:
-        _, ax = plt.subplots(subplot_kw={"projection": "polar"})
-
-    title = [
-        kwargs.pop("title", None),
-    ]
-    ax.set_title("\n".join([i for i in title if i is not None]))
-
-    # set data binning defaults
-    _data_bins: List[float] = data_bins
-    if isinstance(data_bins, int):
-        _data_bins = np.linspace(min(data), max(data), data_bins + 1)
-
-    # get colormap
-    cmap = plt.get_cmap(
-        kwargs.pop(
-            "cmap",
-            "viridis",
-        )
-    )
-
-    # bin input data
-    thetas = np.deg2rad(direction_bins.midpoints)
-    width = np.deg2rad(direction_bins.bin_width)
-    binned_data = direction_bins.bin_data(wind_directions, data)
-    radiis = []
-    for _, values in binned_data.items():
-        radiis.append(np.histogram(a=values, bins=_data_bins)[0])
-    bottoms = np.vstack(
-        [[0] * len(direction_bins.midpoints), np.array(radiis).cumsum(axis=1).T]
-    )[:-1].T
-    colors = [cmap(i) for i in np.linspace(0, 1, len(_data_bins) - 1)]
-
-    # create figure
-    ax.set_theta_zero_location("N")
-    ax.set_theta_direction(-1)
-
-    # plot binned data
-    for theta, radii, bottom in zip(*[thetas, radiis, bottoms]):
-        _ = ax.bar(theta, radii, width=width, bottom=bottom, color=colors, zorder=2)
-
-    # format plot area
-    ax.spines["polar"].set_visible(False)
-    ax.grid(True, which="both", ls="--", zorder=0, alpha=0.5)
-    ax.yaxis.set_major_locator(plt.MaxNLocator(6))
-    plt.setp(ax.get_yticklabels(), fontsize="small")
-    ax.set_xticks(np.radians((0, 90, 180, 270)), minor=False)
-    ax.set_xticklabels(("N", "E", "S", "W"), minor=False, **{"fontsize": "medium"})
-    ax.set_xticks(
-        np.radians(
-            (22.5, 45, 67.5, 112.5, 135, 157.5, 202.5, 225, 247.5, 292.5, 315, 337.5)
-        ),
-        minor=True,
-    )
-    ax.set_xticklabels(
-        (
-            "NNE",
-            "NE",
-            "ENE",
-            "ESE",
-            "SE",
-            "SSE",
-            "SSW",
-            "SW",
-            "WSW",
-            "WNW",
-            "NW",
-            "NNW",
-        ),
-        minor=True,
-        **{"fontsize": "x-small"},
-    )
-
-    ax.set_ylim(kwargs.pop("ylim", None))
-
-    # construct legend
-    if include_legend:
-        handles = [
-            mpatches.Patch(color=colors[n], label=f"{i} to {j}")
-            for n, (i, j) in enumerate(rolling_window(_data_bins, 2))
-        ]
-        _ = ax.legend(
-            handles=handles,
-            bbox_to_anchor=(1.1, 0.5),
-            loc="center left",
-            ncol=1,
-            borderaxespad=0,
-            frameon=False,
-            fontsize="small",
-            title=kwargs.pop("data_unit", None),
-        )
-
-    plt.tight_layout()
-
-    return ax
-
-
-def wind_matrix(
-    wind_speeds: pd.Series,
-    wind_directions: pd.Series,
-    ax: plt.Axes = None,
-    additional_data: pd.Series = None,
-    show_values: bool = False,
-    **kwargs,
-) -> plt.Axes:
-    """Generate a wind-speed/direction matrix from a set of time-indexed speed and direction values.
-
-    Args:
-        wind_speeds (pd.Series):
-            A list of time-indexed wind speeds.
-        wind_directions (pd.Series):
-            A list of time-indexed wind directions (in degrees from north clockwise).
-        ax (plt.Axes, optional):
-            The matplotlib Axes to plot on. Defaults to None which uses the current Axes.
-        additional_data (pd.Series, optional):
-            A list of time-indexed additional data values to plot as a heatmap, aligning with the wind speeds and directions. Defaults to None.
-        show_values (bool, optional):
-            Show the values in the matrix. Defaults to False.
-        **kwargs:
-            Additional keyword arguments to pass to the matplotlib pcolor function.
-            vmin (float, optional):
-                The minimum value to use for the colorbar. Defaults to None.
-            vmax (float, optional):
-                The maximum value to use for the colorbar. Defaults to None.
-            cmap (str, optional):
-                The name of the colormap to use. Defaults to "Spectral_r".
-            cbar_title (str, optional):
-                The title to use for the colorbar. Defaults to None.
-            edgecolor (str, optional):
-                The color to use for the grid lines. Defaults to None.
-    Returns:
-        Figure:
-            A matplotlib figure object.
-    """
-
-    if ax is None:
-        ax = plt.gca()
-
-    title = [
-        kwargs.pop("title", None),
-        "Wind speed/direction matrix",
-    ]
-    ax.set_title("\n".join([i for i in title if i is not None]))
-
-    # convert time-indexed data to 24*12 matrix
-    _wind_speeds = (
-        wind_speeds.groupby([wind_speeds.index.hour, wind_speeds.index.month], axis=0)
-        .mean()
-        .unstack()
-    )
-    _wind_directions = (
-        (
-            (
-                wind_directions.groupby(
-                    [wind_directions.index.month, wind_directions.index.hour], axis=0
-                ).apply(wind_direction_average)
-                # + 90
-            )
-            % 360
-        )
-        .unstack()
-        .T
-    )
-
-    if any(
-        [
-            _wind_speeds.shape != (24, 12),
-            _wind_directions.shape != (24, 12),
-            _wind_directions.shape != _wind_speeds.shape,
-            not np.array_equal(_wind_directions.index, _wind_speeds.index),
-            not np.array_equal(_wind_directions.columns, _wind_speeds.columns),
-        ]
-    ):
-        raise ValueError(
-            "The wind_speeds and wind_directions must cover all months of the year, and all hours of the day, and align with each other."
-        )
-
-    if additional_data is None:
-        _additional_data = _wind_speeds
-    else:
-        _additional_data = (
-            additional_data.groupby(
-                [additional_data.index.hour, additional_data.index.month], axis=0
-            )
-            .mean()
-            .unstack()
-        )
-    if any(
-        [
-            _additional_data.shape != _wind_speeds.shape,
-            not np.array_equal(_additional_data.index, _wind_speeds.index),
-            not np.array_equal(_additional_data.columns, _wind_speeds.columns),
-        ]
-    ):
-        raise ValueError(
-            "The additional_data must align with the wind_speeds and wind_directions."
-        )
-
-    # get kwargs
-    if (kwargs.get("norm", None) is None) and additional_data is not None:
-        warnings.warn(
-            "vmin and vmax should be set when using a normalised colorbar, otherwise color mapping may look odd."
-        )
-
-    cmap = kwargs.pop("cmap", "Spectral_r")
-    vmin = kwargs.pop("vmin", _additional_data.values.min())
-    vmax = kwargs.pop("vmax", _additional_data.values.max())
-    cbar_title = kwargs.pop("cbar_title", None)
-    norm = kwargs.pop("norm", Normalize(vmin=vmin, vmax=vmax, clip=True))
-    mapper = kwargs.pop("mapper", ScalarMappable(norm=norm, cmap=cmap))
-
-    pc = ax.pcolor(_additional_data, vmin=vmin, vmax=vmax, cmap=cmap, **kwargs)
-
-    _x = -np.sin(np.deg2rad(_wind_directions.values))
-    _y = -np.cos(np.deg2rad(_wind_directions.values))
-    direction_matrix = angle_from_north([_x, _y])
-    ax.quiver(
-        np.arange(1, 13, 1) - 0.5,
-        np.arange(0, 24, 1) + 0.5,
-        _x * _wind_speeds.values / 2,
-        _y * _wind_speeds.values / 2,
-        pivot="mid",
-        fc="white",
-        ec="black",
-        lw=0.5,
-        alpha=0.5,
-    )
-
-    if show_values:
-        for _xx, col in enumerate(_wind_directions.values.T):
-            for _yy, _ in enumerate(col.T):
-                local_value = _additional_data.values[_yy, _xx]
-                # print(local_value)
-                cell_color = mapper.to_rgba(local_value)
-                text_color = contrasting_color(cell_color)
-                # direction text
-                ax.text(
-                    _xx,
-                    _yy,
-                    f"{direction_matrix[_yy][_xx]:0.0f}°",
-                    color=text_color,
-                    ha="left",
-                    va="bottom",
-                    fontsize="xx-small",
-                )
-                # speed text
-                ax.text(
-                    _xx + 1,
-                    _yy + 1,
-                    f"{_wind_speeds.values[_yy][_xx]:0.1f}m/s",
-                    color=text_color,
-                    ha="right",
-                    va="top",
-                    fontsize="xx-small",
-                )
-    ax.set_xticks(np.arange(1, 13, 1) - 0.5)
-    ax.set_xticklabels([calendar.month_abbr[i] for i in np.arange(1, 13, 1)])
-    ax.set_yticks(np.arange(0, 24, 1) + 0.5)
-    ax.set_yticklabels([f"{i:02d}:00" for i in np.arange(0, 24, 1)])
-    for label in ax.yaxis.get_ticklabels()[1::2]:
-        label.set_visible(False)
-    cb = plt.colorbar(pc, label=cbar_title, pad=0.01)
-    cb.outline.set_visible(False)
-
-    return ax
-
-
-def wind_speed_direction_frequency(
-    wind_speed: List[float],
-    wind_direction: List[float],
-    ax: plt.Axes = None,
-    speed_bins: List[float] = None,
-    n_directions: float = 8,
-    **kwargs,
-) -> plt.Axes:
-    """Create an image containing a matrix of wind speed/direction frequencies.
-
-    Args:
-        wind_speed (List[float]):
-            A list if wind speeds to process.
-        wind_direction (List[float]):
-            A list of wind directions to process.
-        ax (plt.Axes, optional):
-            The matplotlib Axes to plot on. Defaults to None which uses the current Axes.
-        speed_bins (List[float], optional):
-            A set of bins into which wind speed will be sorted. Defaults to None which divides the
-            range 0-MAX into 10 bins.
-        n_directions (float, optional):
-            A number of cardinal directions into which wind_direction will be grouped.
-            Default is 8.
-        **kwargs:
-            Additional keyword arguments to pass to the function.
-    Returns:
-        plt.Axes:
-            A matplotlib Axes object.
-    """
-
-    cmap = kwargs.pop("cmap", "Purples")
-
-    if ax is None:
-        ax = plt.gca()
-
-    if len(wind_speed) != len(wind_direction):
-        raise ValueError("The given wind speeds and directions do not align.")
-
-    if speed_bins is None:
-        speed_bins = np.linspace(0, max(wind_speed), 11)
-
-    # create "bins" of direction based on number of directions to bin into
-    direction_bin_width = 360 / n_directions
-    direction_bins = (
-        [0]
-        + (np.linspace(0, 360, n_directions + 1) + (direction_bin_width / 2)).tolist()[
-            :-1
-        ]
-        + [360]
-    )
-
-    # bin data into table
-    binned, speed_bins, direction_bins = np.histogram2d(
-        wind_speed, wind_direction, bins=[speed_bins, direction_bins], normed=False
-    )
-    df = pd.DataFrame(binned)
-    # combine first and last column, and drop last column (to get North winds)
-    df.iloc[:, 0] = df.iloc[:, 0] + df.iloc[:, -1]
-    df = df.iloc[:, :-1]
-
-    speed_index = [(i[0], i[1]) for i in rolling_window(speed_bins, 2)]
-    df.index = speed_index
-
-    if n_directions in [4, 8, 16, 32]:
-        direction_index = [
-            cardinality(i, n_directions)
-            for i in [0] + rolling_window(direction_bins[1:-1], 2).mean(axis=1).tolist()
-        ]
-    else:
-        direction_index = [
-            (i[0], i[1])
-            for i in np.roll(
-                rolling_window(direction_bins[1:-1].tolist() + [direction_bins[1]], 2),
-                1,
-                axis=0,
-            )
-        ]
-    df.columns = direction_index
-    df = df / len(wind_speed)
-
-    ax.set_aspect("auto")
-    im = ax.imshow(df.values, cmap=cmap)
-
-    annotate_imshow(im, valfmt="{x:.02%}", fontsize="x-small", exclude_vals=[0])
-
-    ax.xaxis.set_major_locator(mticker.FixedLocator(range(len(df.columns))))
-
-    if isinstance(df.columns[0], str):
-        xticklabels = df.columns
-    else:
-        xticklabels = [f"{i[0]:0.1f}-\n{i[1]:0.1f}" for i in df.columns]
-    ax.set_xticklabels(
-        xticklabels,
-        rotation=0,
-        ha="center",
-        va="top",
-        fontsize=8,
-    )
-    ax.set_xlabel("Wind direction (degrees)")
-
-    ax.yaxis.set_major_locator(mticker.FixedLocator(range(len(df.index))))
-    yticklabels = [f"{i[0]}-{i[1]}" for i in df.index]
-    ax.set_yticklabels(yticklabels, rotation=0, ha="right", va="center", fontsize=8)
-    ax.set_ylabel("Wind speed (m/s)")
-
-    for spine in ["top", "bottom", "left", "right"]:
-        ax.spines[spine].set_visible(False)
-
-    ti = "Wind speed/direction frequency"
-    ax.set_title(ti, x=0, ha="left")
-
-    plt.tight_layout()
-
-    return ax
-
-
-def wind_cumulative_probability(
-    wind_speeds: Tuple[float],
-    ax: plt.Axes = None,
-    speed_bins: Tuple[float] = None,
-    percentiles: Tuple[float] = None,
-    **kwargs,
-) -> plt.Axes:
-    """Plot a cumulative probability graph, showing binned wind speeds in a cumulative
-        frequency histogram.
-
-    Args:
-        wind_speeds (List[float]):
-            A list of wind speeds.
-        ax (plt.Axes, optional):
-            A matplotlib Axes object. Defaults to None.
-        speed_bins (List[float], optional):
-            A set of bin edges to categorise the wind speeds into. Defaults to None.
-        percentiles (List[float], optional):
-            A list of percentiles to show on the chart. Defaults to None.
-        **kwargs:
-            Additional keyword arguments to pass to the plotting function.
-
-    Returns:
-        plt.Figure:
-            A Figure object.
-    """
-    if ax is None:
-        ax = plt.gca()
-
-    if percentiles is None:
-        percentiles = [0.5, 0.95]
-    if (min(percentiles) < 0) or (max(percentiles) > 1):
-        raise ValueError("percentiles must fall within the range 0-1.")
-
-    if speed_bins is None:
-        speed_bins = np.linspace(0, 25, 50)
-    if min(speed_bins) < 0:
-        raise ValueError("Minimum bin value must be >= 0")
-
-    x = speed_bins
-    y = [stats.percentileofscore(wind_speeds, i) / 100 for i in speed_bins]
-
-    # remove all but one element from lists where probability is == 1
-    if len([i for i in y if i == 1]) > 2:
-        idxmax = np.where([i == 1 for i in y])[0][0]
-        x = x[: idxmax + 1]
-        y = y[: idxmax + 1]
-
-    ax.plot(x, y, c="grey")
-    ax.set_xlim(0, max(x))
-    ax.set_xlabel("Wind Speed (m/s)")
-    ax.set_ylabel("Frequency")
-    ax.yaxis.set_major_formatter(mticker.PercentFormatter(1, decimals=0))
-
-    for spine in ["top", "right"]:
-        ax.spines[spine].set_visible(False)
-    ax.grid(visible=True, which="major", axis="both", ls="--", lw=1, alpha=0.25)
-
-    for percentile in percentiles:
-        val = np.quantile(wind_speeds, percentile)
-        ax.hlines(percentile, 0, val, ls="--", lw=1, colors="black")
-        ax.vlines(val, 0, percentile, ls="--", lw=1, colors="black")
-        ax.text(val + 0.1, 0, f"{val:0.1f}m/s", ha="left", va="bottom")
-        ax.text(
-            0.1,
-            percentile + 0.02 if percentile < 0.1 else percentile - 0.02,
-            f"{percentile:0.0%}",
-            ha="left",
-            va="bottom" if percentile < 0.1 else "top",
-        )
-
-    ax.set_ylim(0, 1.01)
-
-    plt.tight_layout()
-
-    return ax
-
-
-def seasonal_speed(
-    wind_speeds: pd.Series,
-    ax: plt.Axes = None,
-    percentiles: Tuple[float] = (0.1, 0.25, 0.75, 0.9),  # type: ignore
-    color: str = "black",
-    title: str = "",
-    **kwargs,
-) -> plt.Axes:  # type: ignore
-    """Plot the wind-speed/frequency histogram for collection of wind speeds.
-
-    Args:
-        wind_speeds (pd.Series):
-            A time-indexed collection of wind speeds.
-        percentiles (Tuple[float], optional):
-            A list of percentiles to show on the chart. Defaults to (0.1, 0.25, 0.75, 0.9).
-        color (str, optional):
-            The color of the plot. Defaults to "black".
-        title (str, optional):
-            A title to add to the resulting plot. Defaults to None.
-
-    Returns:
-        plt.Figure:
-            A Figure object.
-    """
-
-    # if ax is None:
-    #     ax = plt.gca()
-
-    # if speed_bins is None:
-    #     speed_bins = np.linspace(min(wind_speeds), np.quantile(wind_speeds, 0.999), 16)
-
-    # if percentiles and ((min(percentiles) < 0) or (max(percentiles) > 1)):
-    #     raise ValueError("percentiles must fall within the range 0-1.")
-
-    # ax.hist(wind_speeds, bins=speed_bins, density=True, color="grey")
-
-    # if weibull != False:
-    #     if isinstance(weibull, bool):
-    #         params = weibull_pdf(wind_speeds)
-    #     elif not isinstance(weibull, bool):
-    #         params = weibull
-    #     new_x = np.linspace(min(speed_bins), max(speed_bins), 100)
-    #     new_y = exponweib.pdf(new_x, *params)
-    #     ax.plot(new_x, new_y, label="Weibull (PDF)", c="k")
-
-    # low, _ = ax.get_ylim()
-    # for percentile in percentiles:
-    #     x = np.quantile(wind_speeds, percentile)
-    #     ax.axvline(x, 0, 1, ls="--", lw=1, c="k")
-    #     ax.text(x - 0.1, low, f"{percentile:0.0%}", ha="right", va="bottom")
-    #     ax.text(x + 0.1, low, f"{x:0.1f}m/s", ha="left", va="bottom")
-
-    # ax.set_xlim(0, max(speed_bins))
-    # ax.set_xlabel("Wind Speed (m/s)")
-    # ax.set_ylabel("Frequency")
-    # ax.yaxis.set_major_formatter(mtick.PercentFormatter(1, decimals=1))
-
-    # for spine in ["top", "right"]:
-    #     ax.spines[spine].set_visible(False)
-    # ax.grid(visible=True, which="major", axis="both", c="k", ls="--", lw=1, alpha=0.25)
-
-    # ax.legend(
-    #     bbox_to_anchor=(1, 1),
-    #     ncol=1,
-    #     loc="upper right",
-    #     borderaxespad=0.0,
-    #     frameon=False,
-    #     fontsize="small",
-    # )
-
-    # if title is not None:
-    #     ax.set_title(title, x=0, ha="left", va="bottom")
-
-    # plt.tight_layout()
-
-    # TODO - reimplement figure and kwargs usage.
-    raise NotImplementedError("This function is not fully implemented.")
-
-
-def wind_speed_frequency(
-    wind_speeds: List[float],
-    ax: plt.Axes = None,
-    speed_bins: List[float] = None,
-    # weibull: Union[bool, Tuple[float]] = False,
-    percentiles: Tuple[float] = (),
-    **kwargs,
-) -> plt.Axes:
-    """Plot the wind-speed/frequency histogram for collection of wind speeds.
-
-    Args:
-        wind_speeds (List[float]):
-            A collection of wind speeds.
-        ax (plt.Axes, optional):
-            A matplotlib Axes object to plot on. Defaults to None.
-        speed_bins (List[float], optional):
-            A set of bins to fit the input wind speeds into. Defaults to None.
-        weibull (Union[bool, Tuple[float]], optional):
-            Include the weibull curve on the plot. Defaults to False.
-        percentiles (Tuple[float], optional):
-            A list of percentiles to show on the chart. Defaults to None.
-        **kwargs:
-            Additional keyword arguments to pass to the matplotlib hist function.
-
-    Returns:
-        plt.Figure:
-            A Figure object.
-    """
-
-    if ax is None:
-        ax = plt.gca()
-
-    if speed_bins is None:
-        speed_bins = np.linspace(min(wind_speeds), np.quantile(wind_speeds, 0.999), 16)
-
-    if percentiles and ((min(percentiles) < 0) or (max(percentiles) > 1)):
-        raise ValueError("percentiles must fall within the range 0-1.")
-
-    ax.hist(wind_speeds, bins=speed_bins, density=True, color="grey")
-
-    # if weibull != False:
-    #     if isinstance(weibull, bool):
-    #         params = weibull_pdf(wind_speeds)
-    #     elif not isinstance(weibull, bool):
-    #         params = weibull
-    #     new_x = np.linspace(min(speed_bins), max(speed_bins), 100)
-    #     new_y = weibull_min.pdf(new_x, *params)
-    #     ax.plot(new_x, new_y, label="Weibull (PDF)", c="k")
-
-    low, _ = ax.get_ylim()
-    for percentile in percentiles:
-        x = np.quantile(wind_speeds, percentile)
-        ax.axvline(x, 0, 1, ls="--", lw=1, c="black")
-        ax.text(x - 0.1, low, f"{percentile:0.0%}", ha="right", va="bottom")
-        ax.text(x + 0.1, low, f"{x:0.1f}m/s", ha="left", va="bottom")
-
-    ax.set_xlim(0, max(speed_bins))
-    ax.set_xlabel("Wind Speed (m/s)")
-    ax.set_ylabel("Frequency")
-    ax.yaxis.set_major_formatter(mticker.PercentFormatter(1, decimals=1))
-
-    for spine in ["top", "right"]:
-        ax.spines[spine].set_visible(False)
-    ax.grid(visible=True, which="major", axis="both", ls="--", lw=1, alpha=0.25)
-
-    ax.legend(
-        bbox_to_anchor=(1, 1),
-        ncol=1,
-        loc="upper right",
-        borderaxespad=0.0,
-        frameon=False,
-        fontsize="small",
-    )
-
-    plt.tight_layout()
-
-    return ax
-
-
-def radial_histogram(
-    radial_values: List[float],
-    radial_bins: List[float],
-    values: List[List[float]],
-    ax: plt.Axes = None,
-    cmap: Union[Colormap, str] = None,
-    include_labels: bool = False,
-    include_cbar: bool = True,
-    cmap_label: str = None,
-    cbar_freq: bool = False,
-    title: str = None,
-    **kwargs,
-) -> plt.Axes:
-    """Create a radial 2d heatmap-histogram.
-
-    Args:
-        radial_values (List[float]): _description_
-        radial_bins (List[float]): _description_
-        values (List[List[float]]): _description_
-        cmap (Union[Colormap, str], optional): _description_. Defaults to None.
-        include_labels (bool, optional): _description_. Defaults to False.
-        include_cbar (bool, optional): _description_. Defaults to True.
-        cmap_label (str, optional): _description_. Defaults to None.
-        cbar_freq (bool, optional): _description_. Defaults to False.
-        title (str, optional): _description_. Defaults to None.
-
-    Raises:
-        NotImplementedError: _description_
-
-    Returns:
-        plt.Axes: _description_
-    """
-
-    warnings.warn(f"This method is not fully formed!")
-
-    # TODO - deal with kwargs
-
-    if ax is None:
-        _, ax = plt.subplots(subplot_kw={"projection": "polar"})
-
-    # set cmap defaults
-    if isinstance(cmap, str):
-        cmap = plt.get_cmap(cmap)
-    if cmap is None:
-        cmap = plt.get_cmap("magma_r")
-
-    # plot figure
-    ax.set_theta_zero_location("N")
-    ax.set_theta_direction(-1)
-    pc = ax.pcolormesh(
-        radial_values,
-        radial_bins,
-        values,
-        cmap=cmap,
-        alpha=1,
-        ec="none",
-        lw=0,
-        zorder=0,
-    )
-
-    # format plot area
-    ax.spines["polar"].set_visible(False)
-    ax.grid(True, which="both", ls="--", zorder=0, alpha=0.25)
-    ax.yaxis.set_major_locator(plt.MaxNLocator(6))
-    plt.setp(ax.get_yticklabels(), fontsize="small")
-    ax.set_xticks(np.radians((0, 90, 180, 270)), minor=False)
-    ax.set_xticklabels(("N", "E", "S", "W"), minor=False, **{"fontsize": "medium"})
-    ax.set_xticks(
-        np.radians(
-            (22.5, 45, 67.5, 112.5, 135, 157.5, 202.5, 225, 247.5, 292.5, 315, 337.5)
-        ),
-        minor=True,
-    )
-    ax.set_xticklabels(
-        (
-            "NNE",
-            "NE",
-            "ENE",
-            "ESE",
-            "SE",
-            "SSE",
-            "SSW",
-            "SW",
-            "WSW",
-            "WNW",
-            "NW",
-            "NNW",
-        ),
-        minor=True,
-        **{"fontsize": "x-small"},
-    )
-
-    if include_cbar:
-        cb = plt.colorbar(
-            pc,
-            ax=ax,
-            pad=0.07,
-            drawedges=False,
-            label=cmap_label,
-            extend="max",
-            aspect=50,
-        )
-        cb.outline.set_visible(False)
-        if cbar_freq:
-            cb.ax.axes.yaxis.set_major_formatter(mticker.PercentFormatter(1))
-
-    if include_labels:
-        raise NotImplementedError("TODO - add functionality")
-
-    if title:
-        ax.set_title(title, x=0, ha="left", va="bottom")
-
-    plt.tight_layout()
-
-    return ax
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/colormaps.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/colormaps.py
index c4c8848c..b7bf494a 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/colormaps.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/colormaps.py
@@ -1,3 +1,5 @@
+"""Default colormaps"""
+
 from .utilities import colormap_sequential
 
 DBT_COLORMAP = colormap_sequential("white", "#bc204b")
@@ -10,3 +12,7 @@
     "#24135f",
 )
 UTCI_DIFFERENCE_COLORMAP = colormap_sequential("#00A9E0", "w", "#702F8A")
+UTCI_DISTANCE_TO_COMFORTABLE = colormap_sequential("#00A9E0", "w", "#ba000d")
+_ = colormap_sequential(
+    "#E40303", "#FF8C00", "#FFED00", "#008026", "#24408E", "#732982"
+)
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/fisheye_sky.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/fisheye_sky.py
index 6df3756c..d0c167ae 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/fisheye_sky.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/fisheye_sky.py
@@ -1,4 +1,5 @@
-import copy
+"""Methods for creating fisheye sky images."""
+import copy  # pylint: disable=E0401
 
 import numpy as np
 from cv2 import IMREAD_ANYDEPTH, imread
@@ -10,8 +11,10 @@
 from lbt_recipes.recipe import Recipe
 from lbt_recipes.settings import RecipeSettings
 from PIL import Image, ImageEnhance
+from ..bhom.analytics import bhom_analytics
 
 
+@bhom_analytics()
 def fisheye_sky(
     model: Model,
     sensor: Point3D,
@@ -101,7 +104,8 @@ def fisheye_sky(
 
     # img = img.filter(ImageFilter.SMOOTH_MORE)
 
-    # crop out most of floor from fisheye view. typically we see 210deg, so if the image is 360, crop 40$ off of the image (20% from each edge)
+    # crop out most of floor from fisheye view. typically we see
+    # 210deg, so if the image is 360, crop 40$ off of the image (20% from each edge)
     image_size = img.size[0]
     img = img.crop(
         (
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/lb_geometry.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/lb_geometry.py
index c7ebde50..29f30087 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/lb_geometry.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/lb_geometry.py
@@ -1,5 +1,6 @@
-import warnings
-from typing import Tuple, Union
+"""Methods for plotting Ladybug Geometry objects."""
+# TODO - add more to this module
+import warnings  # pylint: disable=E0401
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -14,24 +15,38 @@
     Vector2D,
 )
 
+from ..bhom.analytics import bhom_analytics
 
+
+@bhom_analytics()
 def plot_lb_geo_2d(
-    lb_geometry: Tuple[
-        Union[
-            Mesh2D,
-            Polygon2D,
-            Point2D,
-            Vector2D,
-            LineSegment2D,
-            Arc2D,
-            Polyline2D,
-            Ray2D,
-        ]
+    lb_geometry: tuple[
+        Mesh2D
+        | Polygon2D
+        | Point2D
+        | Vector2D
+        | LineSegment2D
+        | Arc2D
+        | Polyline2D
+        | Ray2D
     ],
     ax: plt.Axes = None,
     **kwargs,
 ) -> plt.Axes:
-    """Example method for creating 2D geometry from LB Geometry objects."""
+    """Example method for creating 2D geometry from LB Geometry objects.
+
+    Args:
+        lb_geometry (tuple[Mesh2D | Polygon2D | Point2D | Vector2D | LineSegment2D | Arc2D | Polyline2D | Ray2D]):
+            A tuple of LB Geometry objects.
+        ax (plt.Axes, optional):
+            A matplotlib Axes object to plot on. Defaults to None.
+        **kwargs:
+            Keyword arguments to pass to the matplotlib plotting methods.
+
+    Returns:
+        plt.Axes:
+            The matplotlib Axes object.
+    """
     warnings.warn(
         "This method is undeveloped and needs splitting into multiple methods."
     )
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/spatial_heatmap.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/spatial_heatmap.py
index 81bf9c1b..e8accd49 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/spatial_heatmap.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/spatial_heatmap.py
@@ -1,45 +1,49 @@
-from typing import Dict, List, Tuple, Union
+"""Methods for plotting spatial heatmaps."""
 
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.colors import BoundaryNorm, Colormap
 from matplotlib.figure import Figure
-from matplotlib.tri.triangulation import Triangulation
+from matplotlib.tri import Triangulation
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 
+from ..bhom.analytics import bhom_analytics
 
+
+@bhom_analytics()
 def spatial_heatmap(
-    triangulations: List[Triangulation],
-    values: List[List[float]],
-    levels: Union[List[float], int] = None,
-    contours: List[float] = None,
-    contour_colors: List[str] = None,
-    contour_widths: List[float] = None,
+    triangulations: list[Triangulation],
+    values: list[list[float]],
+    levels: list[float] | int = None,
+    contours: list[float] = None,
+    contour_colors: list[str] = None,
+    contour_widths: list[float] = None,
     cmap: Colormap = "viridis",
     extend: str = "neither",
     norm: BoundaryNorm = None,
-    xlims: List[float] = None,
-    ylims: List[float] = None,
+    xlims: list[float] = None,
+    ylims: list[float] = None,
     colorbar_label: str = "",
     title: str = "",
-    highlight_pts: Dict[str, Tuple[int]] = None,
+    highlight_pts: dict[str, tuple[int]] = None,
     show_legend_title: bool = True,
+    clabels: bool = False,
 ) -> Figure:
     """Plot a spatial map of a variable using a triangulation and associated values.
 
     Args:
-        triangulations (List[Triangulation]):
+        triangulations (list[Triangulation]):
             A list of triangulations to plot.
-        values (List[List[float]]):
+        values (list[list[float]]):
             A list of values, corresponding with the triangulations and their respective indices.
-        levels (Union[List[float], int], optional):
+        levels (list[float] | int, optional):
             The number of levels to include in the colorbar. Defaults to None which will use
             10-steps between the min/max for all given values.
-        contours (List[float], optional):
+        contours (list[float], optional):
             Add contours at the given values to the spatial plot. Defaults to None.
-        contour_colors (List[str], optional):
+        contour_colors (list[str], optional):
             Color each of the listed contours. Defaults to None.
-        contour_widths (List[float], optional):
+        contour_widths (list[float], optional):
             The width each of the listed contours. Defaults to None.
         cmap (Colormap, optional):
             The colormap to use for this plot. Defaults to "viridis".
@@ -48,19 +52,21 @@ def spatial_heatmap(
         norm (BoundaryNorm, optional):
             A matplotlib BoundaryNorm object containing colormap boundary mapping information.
             Defaults to None.
-        xlims (List[float], optional):
+        xlims (list[float], optional):
             The x-limit for the plot. Defaults to None.
-        ylims (List[float], optional):
+        ylims (list[float], optional):
             The y-limit for the plot. Defaults to None.
         colorbar_label (str, optional):
             A label to be placed next to the colorbar. Defaults to "".
         title (str, optional):
             The title to be placed on the plot. Defaults to "".
-        highlight_pts (Dict[str, int], optional):
+        highlight_pts (dict[str, int], optional):
             A set of points (and their names) to indicate on the spatial plot. Value is the int
             index of the highlighted point.
         show_legend_title (bool, optional):
             A convenient flag to hide the legend and title.
+        clabels (bool, optional):
+            A flag to show contour labels. Defaults to False.
 
     Returns:
         Figure: A matplotlib Figure object.
@@ -120,7 +126,8 @@ def spatial_heatmap(
                     colors=contour_colors,
                     linewidths=contour_widths,
                 )
-                ax.clabel(tcl, inline=1, fontsize=10, colors=["k"])
+                if clabels:
+                    ax.clabel(tcl, inline=1, fontsize="small", colors=contour_colors)
                 tcls.append(tcl)
 
     if highlight_pts is not None:
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/utilities.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/utilities.py
index e87880e1..879ff452 100644
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/utilities.py
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/utilities.py
@@ -1,9 +1,13 @@
+"""Color handling utilities"""
+# pylint: disable=E0401
 import base64
 import colorsys
 import copy
 import io
 from pathlib import Path
-from typing import Any, List, Tuple, Union
+from typing import Any
+
+# pylint: enable=E0401
 
 import matplotlib.image as mimage
 import matplotlib.pyplot as plt
@@ -16,50 +20,65 @@
     colorConverter,
     is_color_like,
     rgb2hex,
+    to_hex,
     to_rgb,
     to_rgba_array,
 )
 from matplotlib.tri import Triangulation
 from PIL import Image
 
+from ..bhom.analytics import bhom_analytics
+
 
+@bhom_analytics()
 def animation(
-    image_files: List[Union[str, Path]],
-    output_gif: Union[str, Path],
+    images: list[str | Path | Image.Image],
+    output_gif: str | Path,
     ms_per_image: int = 333,
+    transparency_idx: int = 0,
 ) -> Path:
     """Create an animated gif from a set of images.
 
     Args:
-        image_files (List[Union[str, Path]]):
-            A list of image files.
-        output_gif (Union[str, Path]):
+        images (list[str | Path | Image.Image]):
+            A list of image files or PIL Image objects.
+        output_gif (str | Path):
             The output gif file to be created.
         ms_per_image (int, optional):
-            NUmber of milliseconds per image. Default is 333, for 3 images per second.
+            Number of milliseconds per image. Default is 333, for 3 images per second.
+        transparency_idx (int, optional):
+            The index of the color to be used as the transparent color. Default is 0.
 
     Returns:
         Path:
             The animated gif.
 
     """
-
-    image_files = [Path(i) for i in image_files]
-
-    images = [Image.open(i) for i in image_files]
+    _images = []
+    for i in images:
+        if isinstance(i, (str, Path)):
+            _images.append(Image.open(i))
+        elif isinstance(i, Image.Image):
+            _images.append(i)
+        else:
+            raise ValueError(
+                f"images must be a list of strings, Paths or PIL Image objects - {i} is not valid."
+            )
 
     # create white background
-    background = Image.new("RGBA", images[0].size, (255, 255, 255))
+    background = Image.new("RGBA", _images[0].size, (255, 255, 255))
 
-    images = [Image.alpha_composite(background, i) for i in images]
+    _images = [Image.alpha_composite(background, i) for i in _images]
 
-    images[0].save(
+    _images[0].save(
         output_gif,
         save_all=True,
-        append_images=images[1:],
+        append_images=_images[1:],
         optimize=False,
         duration=ms_per_image,
         loop=0,
+        disposal=2,
+        transparency=transparency_idx,
     )
 
     return output_gif
@@ -69,12 +88,13 @@ def relative_luminance(color: Any):
     """Calculate the relative luminance of a color according to W3C standards
 
     Args:
+        color (Any):
+            matplotlib color or sequence of matplotlib colors - Hex code,
+            rgb-tuple, or html color name.
 
-    color : matplotlib color or sequence of matplotlib colors - Hex code,
-    rgb-tuple, or html color name.
-    Returns
-    -------
-    luminance : float(s) between 0 and 1
+    Returns:
+        float:
+            Luminance value between 0 and 1.
     """
     rgb = colorConverter.to_rgba_array(color)[:, :3]
     rgb = np.where(rgb <= 0.03928, rgb / 12.92, ((rgb + 0.055) / 1.055) ** 2.4)
@@ -89,60 +109,64 @@ def contrasting_color(color: Any):
     """Calculate the contrasting color for a given color.
 
     Args:
-        color (Any): matplotlib color or sequence of matplotlib colors - Hex code,
-        rgb-tuple, or html color name.
+        color (Any):
+            matplotlib color or sequence of matplotlib colors - Hex code,
+            rgb-tuple, or html color name.
+
     Returns:
-        str: String code of the contrasting color.
+        str:
+            String code of the contrasting color.
     """
     return ".15" if relative_luminance(color) > 0.408 else "w"
 
 
 def colormap_sequential(
-    *colors: Union[str, float, int, Tuple]
+    *colors: str | float | int | tuple, N: int = 256
 ) -> LinearSegmentedColormap:
     """
     Create a sequential colormap from a list of input colors.
 
     Args:
-        colors (Union[str, float, int, Tuple]):
+        *colors (str | float | int | tuple):
             A list of colors according to their hex-code, string name, character code or
             RGBA values.
+        N (int, optional):
+            The number of colors in the colormap. Defaults to 256.
 
     Returns:
         LinearSegmentedColormap:
             A matplotlib colormap.
 
     Examples:
-    >> colormap_sequential("green", "#F034A3", (0.5, 0.2, 0.8), "y")
+    >> colormap_sequential(
+        (0.89411764705, 0.01176470588, 0.01176470588),
+        "darkorange",
+        "#FFED00",
+        "#008026",
+        (36/255, 64/255, 142/255),
+        "#732982"
+    )
     """
-    for color in colors:
-        if not isinstance(color, (str, float, int, tuple)):
-            raise KeyError(f"{color} not recognised as a valid color string.")
 
     if len(colors) < 2:
         raise KeyError("Not enough colors input to create a colormap.")
 
     fixed_colors = []
-    for c in colors:
-        if is_color_like(c):
-            try:
-                fixed_colors.append(rgb2hex(c))
-            except ValueError:
-                fixed_colors.append(c)
-        else:
-            raise KeyError(f"{c} not recognised as a valid color string.")
+    for color in colors:
+        fixed_colors.append(to_hex(color))
+
     return LinearSegmentedColormap.from_list(
-        f"{'_'.join(fixed_colors)}",
-        list(zip(np.linspace(0, 1, len(fixed_colors)), fixed_colors)),
-        N=256,
+        name=f"{'_'.join(fixed_colors)}",
+        colors=fixed_colors,
+        N=N,
     )
 
 
-def lb_colormap(name: Union[int, str] = "original") -> LinearSegmentedColormap:
+def lb_colormap(name: int | str = "original") -> LinearSegmentedColormap:
     """Create a Matplotlib from a colormap provided by Ladybug.
 
     Args:
-        name (Union[int, str], optional):
+        name (int | str, optional):
             The name of the colormap to create. Defaults to "original".
 
     Raises:
@@ -174,25 +198,25 @@ def lb_colormap(name: Union[int, str] = "original") -> LinearSegmentedColormap:
 
 def annotate_imshow(
     im: mimage.AxesImage,
-    data: List[float] = None,
+    data: list[float] = None,
     valfmt: str = "{x:.2f}",
-    textcolors: Tuple[str] = ("black", "white"),
+    textcolors: tuple[str] = ("black", "white"),
     threshold: float = None,
-    exclude_vals: List[float] = None,
+    exclude_vals: list[float] = None,
     **text_kw,
-) -> List[str]:
+) -> list[str]:
     """A function to annotate a heatmap.
 
     Args:
         im (AxesImage):
             The AxesImage to be labeled.
-        data (List[float], optional):
+        data (list[float], optional):
             Data used to annotate. If None, the image's data is used. Defaults to None.
         valfmt (_type_, optional):
             The format of the annotations inside the heatmap. This should either use the string
             format method, e.g. "$ {x:.2f}", or be a `matplotlib.ticker.Formatter`.
             Defaults to "{x:.2f}".
-        textcolors (Tuple[str], optional):
+        textcolors (tuple[str], optional):
             A pair of colors.  The first is used for values below a threshold, the second for
             those above.. Defaults to ("black", "white").
         threshold (float, optional):
@@ -204,7 +228,7 @@ def annotate_imshow(
             All other keyword arguments are passed on to the created `~matplotlib.text.Text`
 
     Returns:
-        List[str]:
+        list[str]:
             The texts added to the AxesImage.
     """
 
@@ -240,7 +264,7 @@ def annotate_imshow(
     return texts
 
 
-def lighten_color(color: Union[str, Tuple], amount: float = 0.5) -> Tuple[float]:
+def lighten_color(color: str | tuple, amount: float = 0.5) -> tuple[float]:
     """
     Lightens the given color by multiplying (1-luminosity) by the given amount.
 
@@ -251,7 +275,7 @@ def lighten_color(color: Union[str, Tuple], amount: float = 0.5) -> Tuple[float]
             The amount of lightening to apply.
 
     Returns:
-        Tuple[float]:
+        tuple[float]:
             An RGB value.
 
     Examples:
@@ -267,38 +291,7 @@ def lighten_color(color: Union[str, Tuple], amount: float = 0.5) -> Tuple[float]
     return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
 
 
-def add_bar_labels(ax: plt.Axes, orientation: str, threshold: float) -> None:
-    """Add labels to the end of each bar in a bar chart.
-
-    Arguments:
-        ax (matplotlib.axes.Axes):
-            The matplotlib object containing the axes of the plot to annotate.
-        orientation (str):
-            The orientation of the plot. Either "vertical" or "horizontal".
-        threshold (float):
-            The threshold value to use to determine whether to add a label.
-
-    """
-
-    for rect in ax.patches:
-        x = rect.get_x() + (rect.get_width() / 2)
-        y = rect.get_y() + (rect.get_height() / 2)
-        if orientation == "vertical":
-            value = rect.get_height()
-        elif orientation == "horizontal":
-            value = rect.get_width()
-        else:
-            raise ValueError("orientation must be either 'vertical' or 'horizontal'")
-        if value > threshold:
-            ax.annotate(
-                f"{value:.0%}",
-                (x, y),
-                ha="center",
-                va="center",
-                c=contrasting_color(rect.get_facecolor()),
-            )
-
-
+@bhom_analytics()
 def create_title(text: str, plot_type: str) -> str:
     """Create a title for a plot.
 
@@ -324,6 +317,7 @@ def create_title(text: str, plot_type: str) -> str:
     )
 
 
+@bhom_analytics()
 def average_color(colors: Any, keep_alpha: bool = False) -> str:
     """Return the average color from a list of colors.
 
@@ -353,6 +347,7 @@ def average_color(colors: Any, keep_alpha: bool = False) -> str:
     return rgb2hex(to_rgba_array(colors).mean(axis=0), keep_alpha=keep_alpha)
 
 
+@bhom_analytics()
 def base64_to_image(base64_string: str, image_path: Path) -> Path:
     """Convert a base64 encoded image into a file on disk.
 
@@ -377,6 +372,7 @@ def base64_to_image(base64_string: str, image_path: Path) -> Path:
     return image_path
 
 
+@bhom_analytics()
 def image_to_base64(image_path: Path, html: bool = False) -> str:
     """Load an image file from disk and convert to base64 string.
 
@@ -413,6 +409,7 @@ def image_to_base64(image_path: Path, html: bool = False) -> str:
     return base64_string
 
 
+@bhom_analytics()
 def figure_to_base64(figure: plt.Figure, html: bool = False) -> str:
     """Convert a matplotlib figure object into a base64 string.
 
@@ -440,6 +437,7 @@ def figure_to_base64(figure: plt.Figure, html: bool = False) -> str:
     return base64_string
 
 
+@bhom_analytics()
 def figure_to_image(fig: plt.Figure) -> Image:
     """Convert a matplotlib Figure object into a PIL Image.
 
@@ -464,13 +462,14 @@ def figure_to_image(fig: plt.Figure) -> Image:
     return Image.fromarray(buf)
 
 
+@bhom_analytics()
 def tile_images(
-    imgs: Union[List[Path], List[Image.Image]], rows: int, cols: int
+    imgs: list[Path] | list[Image.Image], rows: int, cols: int
 ) -> Image.Image:
     """Tile a set of images into a grid.
 
     Args:
-        imgs (Union[List[Path], List[Image.Image]]):
+        imgs (Union[list[Path], list[Image.Image]]):
             A list of images to tile.
         rows (int):
             The number of rows in the grid.
@@ -508,6 +507,7 @@ def tile_images(
     return grid
 
 
+@bhom_analytics()
 def triangulation_area(triang: Triangulation) -> float:
     """Calculate the area of a matplotlib Triangulation.
 
@@ -539,9 +539,10 @@ def triangulation_area(triang: Triangulation) -> float:
     return area
 
 
+@bhom_analytics()
 def create_triangulation(
-    x: List[float],
-    y: List[float],
+    x: list[float],
+    y: list[float],
     alpha: float = None,
     max_iterations: int = 250,
     increment: float = 0.01,
@@ -550,9 +551,9 @@ def create_triangulation(
         remove elements with edges larger than alpha.
 
     Args:
-        x (List[float]):
+        x (list[float]):
             A list of x coordinates.
-        y (List[float]):
+        y (list[float]):
             A list of y coordinates.
         alpha (float, optional):
             A value to start alpha at.
@@ -607,3 +608,44 @@ def create_triangulation(
     plt.close(fig)
     triang.set_mask(maxi > alpha)
     return triang
+
+
+@bhom_analytics()
+def format_polar_plot(ax: plt.Axes, yticklabels: bool = True) -> plt.Axes:
+    """Format a polar plot, to save on having to write this every time!"""
+    ax.set_theta_zero_location("N")
+    ax.set_theta_direction(-1)
+
+    # format plot area
+    ax.spines["polar"].set_visible(False)
+    ax.grid(True, which="both", ls="--", zorder=0, alpha=0.3)
+    ax.yaxis.set_major_locator(plt.MaxNLocator(6))
+    plt.setp(ax.get_yticklabels(), fontsize="small")
+    ax.set_xticks(np.radians((0, 90, 180, 270)), minor=False)
+    ax.set_xticklabels(("N", "E", "S", "W"), minor=False, **{"fontsize": "medium"})
+    ax.set_xticks(
+        np.radians(
+            (22.5, 45, 67.5, 112.5, 135, 157.5, 202.5, 225, 247.5, 292.5, 315, 337.5)
+        ),
+        minor=True,
+    )
+    ax.set_xticklabels(
+        (
+            "NNE",
+            "NE",
+            "ENE",
+            "ESE",
+            "SE",
+            "SSE",
+            "SSW",
+            "SW",
+            "WSW",
+            "WNW",
+            "NW",
+            "NNW",
+        ),
+        minor=True,
+        **{"fontsize": "x-small"},
+    )
+    if not yticklabels:
+        ax.set_yticklabels([])
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/solar.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/solar.py
new file mode 100644
index 00000000..e9869c9a
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/solar.py
@@ -0,0 +1,954 @@
+"""Methods for handling solar radiation."""
+
+# pylint: disable=E0401
+import itertools
+import textwrap
+from datetime import datetime
+import json
+import concurrent
+from dataclasses import dataclass, field
+from enum import Enum, auto
+from pathlib import Path
+from typing import Any
+
+# pylint: enable=E0401
+
+import matplotlib.ticker as mticker
+import numpy as np
+import pandas as pd
+from honeybee.config import folders as hb_folders
+from ladybug.wea import EPW, AnalysisPeriod, Wea, Location
+from matplotlib import pyplot as plt
+from tqdm import tqdm
+
+from .helpers import (
+    OpenMeteoVariable,
+    angle_from_north,
+    angle_to_vector,
+    cardinality,
+    circular_weighted_mean,
+    rolling_window,
+    scrape_meteostat,
+    scrape_openmeteo,
+    wind_speed_at_height,
+    remove_leap_days,
+)
+from .bhom.analytics import bhom_analytics
+from .bhom.logging import CONSOLE_LOGGER
+from .ladybug_extension.analysisperiod import (
+    analysis_period_to_boolean,
+    analysis_period_to_datetimes,
+    describe_analysis_period,
+)
+from .ladybug_extension.location import location_to_string
+from ladybug.sunpath import Sunpath, Sun
+from .ladybug_extension.datacollection import header_to_string
+
+from .plot.utilities import contrasting_color, format_polar_plot
+
+
+class IrradianceType(Enum):
+    """Irradiance types."""
+
+    TOTAL = auto()
+    DIRECT = auto()
+    DIFFUSE = auto()
+    REFLECTED = auto()
+
+    def to_string(self) -> str:
+        """Get the string representation of the IrradianceType."""
+        return self.name.title()
+
+
+@dataclass(init=True, repr=True, eq=True)
+class Solar:
+    """An object to handle solar radiation."""
+
+    global_horizontal_irradiance: list[float]
+    direct_normal_irradiance: list[float]
+    diffuse_horizontal_irradiance: list[float]
+    datetimes: list[datetime] | pd.DatetimeIndex
+    source: str = None
+
+    def __post_init__(self):
+        if not (
+            len(self.global_horizontal_irradiance)
+            == len(self.direct_normal_irradiance)
+            == len(self.diffuse_horizontal_irradiance)
+            == len(self.datetimes)
+        ):
+            raise ValueError(
+                "global_horizontal_irradiance, direct_normal_irradiance, diffuse_horizontal_irradiance and datetimes must be the same length."
+            )
+
+        if len(self.global_horizontal_irradiance) <= 1:
+            raise ValueError(
+                "global_horizontal_irradiance, direct_normal_irradiance, diffuse_horizontal_irradiance and datetimes must be at least 2 items long."
+            )
+
+        if len(set(self.datetimes)) != len(self.datetimes):
+            raise ValueError("datetimes contains duplicates.")
+
+        # convert to lists
+        self.global_horizontal_irradiance = np.array(self.global_horizontal_irradiance)
+        self.direct_normal_irradiance = np.array(self.direct_normal_irradiance)
+        self.diffuse_horizontal_irradiance = np.array(
+            self.diffuse_horizontal_irradiance
+        )
+        self.datetimes = pd.DatetimeIndex(self.datetimes)
+
+        # validate
+        if np.any(np.isnan(self.global_horizontal_irradiance)):
+            raise ValueError("global_horizontal_irradiance contains null values.")
+
+        if np.any(np.isnan(self.direct_normal_irradiance)):
+            raise ValueError("direct_normal_irradiance contains null values.")
+
+        if np.any(np.isnan(self.diffuse_horizontal_irradiance)):
+            raise ValueError("diffuse_horizontal_irradiance contains null values.")
+
+        if np.any(self.global_horizontal_irradiance < 0):
+            raise ValueError("global_horizontal_irradiance must be >= 0")
+
+        if np.any(self.direct_normal_irradiance < 0):
+            raise ValueError("direct_normal_irradiance must be >= 0")
+
+        if np.any(self.diffuse_horizontal_irradiance < 0):
+            raise ValueError("diffuse_horizontal_irradiance must be >= 0")
+
+    def __len__(self) -> int:
+        return len(self.datetimes)
+
+    def __repr__(self) -> str:
+        """The printable representation of the given object"""
+        if self.source:
+            return f"{self.__class__.__name__} from {self.source}"
+
+        return (
+            f"{self.__class__.__name__}({min(self.datetimes):%Y-%m-%d} to "
+            f"{max(self.datetimes):%Y-%m-%d}, n={len(self.datetimes)}, "
+            "NO SOURCE"
+        )
+
+    def __str__(self) -> str:
+        """The string representation of the given object"""
+        return self.__repr__()
+
+    #################
+    # CLASS METHODS #
+    #################
+
+    def to_dict(self) -> dict:
+        """Return the object as a dictionary."""
+
+        return {
+            "_t": "BH.oM.LadybugTools.Solar",
+            "global_horizontal_irradiance": [
+                float(i) for i in self.global_horizontal_irradiance
+            ],
+            "direct_normal_irradiance": [
+                float(i) for i in self.direct_normal_irradiance
+            ],
+            "diffuse_horizontal_irradiance": [
+                i for i in self.diffuse_horizontal_irradiance
+            ],
+            "datetimes": [i.isoformat() for i in self.datetimes],
+            "source": self.source,
+        }
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "Solar":
+        """Create this object from a dictionary."""
+
+        return cls(
+            global_horizontal_irradiance=d["global_horizontal_irradiance"],
+            direct_normal_irradiance=d["direct_normal_irradiance"],
+            diffuse_horizontal_irradiance=d["diffuse_horizontal_irradiance"],
+            datetimes=pd.to_datetime(d["datetimes"]),
+            source=d["source"],
+        )
+
+    def to_json(self) -> str:
+        """Convert this object to a JSON string."""
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_string: str) -> "Solar":
+        """Create this object from a JSON string."""
+
+        return cls.from_dict(json.loads(json_string))
+
+    def to_file(self, path: Path) -> Path:
+        """Convert this object to a JSON file."""
+
+        if Path(path).suffix != ".json":
+            raise ValueError("path must be a JSON file.")
+
+        with open(Path(path), "w") as fp:
+            fp.write(self.to_json())
+
+        return Path(path)
+
+    @classmethod
+    def from_file(cls, path: Path) -> "Solar":
+        """Create this object from a JSON file."""
+        with open(Path(path), "r") as fp:
+            return cls.from_json(fp.read())
+
+    def to_csv(self, path: Path) -> Path:
+        """Save this object as a csv file.
+
+        Args:
+            path (Path):
+                The path containing the CSV file.
+
+        Returns:
+            Path:
+                The resultant CSV file.
+        """
+        csv_path = Path(path)
+        self.df.to_csv(csv_path)
+        return csv_path
+
+    @classmethod
+    def from_dataframe(
+        cls,
+        df: pd.DataFrame,
+        global_horizontal_irradiance_column: Any,
+        direct_normal_irradiance_column: Any,
+        diffuse_horizontal_irradiance_columns: Any,
+        source: str = "DataFrame",
+    ) -> "Solar":
+        """Create a Solar object from a Pandas DataFrame, with global horizontal irradiance, direct normal irradiance and diffuse horizontal irradiance columns.
+
+        Args:
+            df (pd.DataFrame):
+                A DataFrame object containing speed and direction columns, and a datetime index.
+            global_horizontal_irradiance_column (str):
+                The name of the column where global horizontal irradiance data exists.
+            direct_normal_irradiance_column (str):
+                The name of the column where direct normal irradiance data exists.
+            diffuse_horizontal_irradiance_columns (str):
+                The name of the column where diffuse horizontal irradiance data exists.
+            source (str, optional):
+                The source of the data. Defaults to "DataFrame".
+
+        """
+
+        if not isinstance(df, pd.DataFrame):
+            raise ValueError(f"df must be of type {type(pd.DataFrame)}")
+
+        if not isinstance(df.index, pd.DatetimeIndex):
+            raise ValueError(
+                f"The DataFrame's index must be of type {type(pd.DatetimeIndex)}"
+            )
+
+        # remove NaN values
+        df.dropna(axis=0, how="any", inplace=True)
+
+        # remove duplicates in input dataframe
+        df = df.loc[~df.index.duplicated()]
+
+        return cls(
+            global_horizontal_irradiance=df[
+                global_horizontal_irradiance_column
+            ].tolist(),
+            direct_normal_irradiance=df[direct_normal_irradiance_column].tolist(),
+            diffuse_horizontal_irradiance=df[
+                diffuse_horizontal_irradiance_columns
+            ].tolist(),
+            datetimes=df.index.tolist(),
+            source=source,
+        )
+
+    @classmethod
+    def from_csv(
+        cls,
+        csv_path: Path,
+        global_horizontal_irradiance_column: Any,
+        direct_normal_irradiance_column: Any,
+        diffuse_horizontal_irradiance_columns: Any,
+        **kwargs,
+    ) -> "Solar":
+        """Create a Wind object from a csv containing wind speed and direction columns.
+
+        Args:
+            csv_path (Path):
+                The path to the CSV file containing speed and direction columns, and a datetime index.
+            global_horizontal_irradiance_column (str):
+                The name of the column where global horizontal irradiance data exists.
+            direct_normal_irradiance_column (str):
+                The name of the column where direct normal irradiance data exists.
+            diffuse_horizontal_irradiance_columns (str):
+                The name of the column where diffuse horizontal irradiance data exists.
+            **kwargs:
+                Additional keyword arguments passed to pd.read_csv.
+        """
+        csv_path = Path(csv_path)
+        df = pd.read_csv(csv_path, **kwargs)
+        return cls.from_dataframe(
+            df,
+            global_horizontal_irradiance=df[
+                global_horizontal_irradiance_column
+            ].tolist(),
+            direct_normal_irradiance=df[direct_normal_irradiance_column].tolist(),
+            diffuse_horizontal_irradiance=df[
+                diffuse_horizontal_irradiance_columns
+            ].tolist(),
+            datetimes=df.index.tolist(),
+            source=csv_path.name,
+        )
+
+    @classmethod
+    def from_epw(cls, epw: Path | EPW) -> "Solar":
+        """Create a Solar object from an EPW file or object.
+
+        Args:
+            epw (Path | EPW):
+                The path to the EPW file, or an EPW object.
+        """
+
+        if isinstance(epw, (str, Path)):
+            source = Path(epw).name
+            epw = EPW(epw)
+        else:
+            source = Path(epw.file_path).name
+
+        return cls(
+            global_horizontal_irradiance=epw.global_horizontal_radiation.values,
+            direct_normal_irradiance=epw.direct_normal_radiation.values,
+            diffuse_horizontal_irradiance=epw.diffuse_horizontal_radiation.values,
+            datetimes=analysis_period_to_datetimes(AnalysisPeriod()),
+            source=source,
+        )
+
+    @classmethod
+    def from_openmeteo(
+        cls,
+        latitude: float,
+        longitude: float,
+        start_date: datetime | str,
+        end_date: datetime | str,
+    ) -> "Solar":
+        """Create a Solar object from data obtained from the Open-Meteo database of historic weather station data.
+
+        Args:
+            latitude (float):
+                The latitude of the target site, in degrees.
+            longitude (float):
+                The longitude of the target site, in degrees.
+            start_date (datetime | str):
+                The start-date from which records will be obtained.
+            end_date (datetime | str):
+                The end-date beyond which records will be ignored.
+        """
+
+        df = scrape_openmeteo(
+            latitude=latitude,
+            longitude=longitude,
+            start_date=start_date,
+            end_date=end_date,
+            variables=[
+                OpenMeteoVariable.SHORTWAVE_RADIATION,
+                OpenMeteoVariable.DIRECT_NORMAL_IRRADIANCE,
+                OpenMeteoVariable.DIFFUSE_RADIATION,
+            ],
+            convert_units=True,
+        )
+
+        df.dropna(how="any", axis=0, inplace=True)
+
+        global_horizontal_irradiance = df[
+            "Global Horizontal Radiation (Wh/m2)"
+        ].tolist()
+        direct_normal_irradiance = df["Direct Normal Radiation (Wh/m2)"].tolist()
+        diffuse_horizontal_irradiance = df[
+            "Diffuse Horizontal Radiation (Wh/m2)"
+        ].tolist()
+
+        if (
+            len(global_horizontal_irradiance) == 0
+            or len(direct_normal_irradiance) == 0
+            or len(diffuse_horizontal_irradiance) == 0
+        ):
+            raise ValueError(
+                "OpenMeteo did not return any data for the given latitude, longitude and start/end dates."
+            )
+        datetimes = df.index.tolist()
+
+        return cls(
+            global_horizontal_irradiance=global_horizontal_irradiance,
+            direct_normal_irradiance=direct_normal_irradiance,
+            diffuse_horizontal_irradiance=diffuse_horizontal_irradiance,
+            datetimes=datetimes,
+            source="OpenMeteo",
+        )
+
+    @property
+    def df(self) -> pd.DataFrame:
+        """Get a dataframe of the data."""
+        return pd.concat([self.ghi, self.dni, self.dhi], axis=1)
+
+    @property
+    def ghi(self) -> pd.Series:
+        """Get the global horizontal irradiance."""
+        return pd.Series(
+            self.global_horizontal_irradiance,
+            index=self.datetimes,
+            name="Global Horizontal Irradiance (Wh/m2)",
+        )
+
+    @property
+    def dni(self) -> pd.Series:
+        """Get the direct normal irradiance."""
+        return pd.Series(
+            self.direct_normal_irradiance,
+            index=self.datetimes,
+            name="Direct Normal Irradiance (Wh/m2)",
+        )
+
+    @property
+    def dhi(self) -> pd.Series:
+        """Get the diffuse horizontal irradiance."""
+        return pd.Series(
+            self.diffuse_horizontal_irradiance,
+            index=self.datetimes,
+            name="Diffuse Horizontal Irradiance (Wh/m2)",
+        )
+
+    def suns(self, location: Location) -> list[Sun]:
+        """Create a list f suns representing position for the given location.
+
+        Args:
+            location (Location):
+                The location of the site.
+
+        Returns:
+            list[Sun]:
+                A list of Sun objects.
+        """
+        sp = Sunpath.from_location(location=location)
+        return [sp.calculate_sun_from_date_time(dt) for dt in self.datetimes]
+
+    def wea(self, location: Location) -> Wea:
+        """Create a Wea object from this Solar object.
+
+        Args:
+            location (Location):
+                The location of the site.
+
+        Returns:
+            Wea:
+                A Wea object.
+        """
+
+        # create annual values
+        if max(self.datetimes) - min(self.datetimes) < pd.Timedelta(
+            days=365
+        ) - pd.Timedelta(minutes=60):
+            raise ValueError(
+                "The Solar object must contain at least 1 year's worth of data to generate a Wea."
+            )
+
+        df = remove_leap_days(self.df)
+
+        grouped = df.groupby([df.index.month, df.index.day, df.index.hour]).mean()
+        index = pd.date_range("2017-01-01", periods=8760, freq="60T")
+        grouped.set_index(index, inplace=True)
+
+        return Wea.from_annual_values(
+            location=location,
+            direct_normal_irradiance=grouped[
+                "Direct Normal Irradiance (Wh/m2)"
+            ].tolist(),
+            diffuse_horizontal_irradiance=grouped[
+                "Diffuse Horizontal Irradiance (Wh/m2)"
+            ].tolist(),
+        )
+
+    @staticmethod
+    def altitudes(n: int) -> list[float]:
+        """Get a list of altitudes."""
+        if not isinstance(n, int):
+            raise ValueError("n_altitudes must be an integer.")
+        if n < 3:
+            raise ValueError("n_altitudes must be an integer >= 3.")
+        return np.linspace(0, 90, n).tolist()
+
+    @staticmethod
+    def azimuths(n: int) -> list[float]:
+        """Get a list of azimuths."""
+        if not isinstance(n, int):
+            raise ValueError("n_azimuths must be an integer.")
+        if n < 3:
+            raise ValueError("n_azimuths must be an integer >= 3.")
+        return np.linspace(0, 360, n).tolist()
+
+    def directional_irradiance_matrix(
+        self,
+        location: Location,
+        altitude: float | int = 0,
+        directions: int = 32,
+        ground_reflectance: float = 0.2,
+        isotropic: bool = True,
+        reload: bool = True,
+    ) -> pd.DataFrame:
+        """Calculate the irradiance in W/m2 for each direction, for the given altitude.
+
+        Args:
+            location (Location):
+                The location of the site.
+            altitude (float, optional):
+                The altitude of the facade. Defaults to 0 for a vertical surface.
+            directions (int, optional):
+                The number of directions to calculate. Defaults to 32.
+            ground_reflectance (float, optional):
+                The ground reflectance. Defaults to 0.2.
+            isotropic (bool, optional):
+                Calculate isotropic diffuse irradiance. Defaults to True.
+            reload (bool, optional):
+                reload results if they've already been saved.
+
+        Returns:
+            pd.DataFrame:
+                A dataframe containing a huge amount of information!
+        """
+        _wea = self.wea(location=location)
+        midpoints = np.linspace(0, 360, directions + 1)[:-1]
+
+        _dir = Path(hb_folders.default_simulation_folder) / "_lbt_tk_solar"
+        _dir.mkdir(exist_ok=True, parents=True)
+        sp = (
+            _dir
+            / f"{location_to_string(location)}_{ground_reflectance}_{isotropic}_{directions}_{altitude}.h5"
+        )
+        if reload and sp.exists():
+            CONSOLE_LOGGER.info(f"Loading results from {sp}.")
+            return pd.read_hdf(sp, key="df")
+
+        # get headers
+        cols = _wea.directional_irradiance(0, 0)
+        units = [header_to_string(i.header) for i in cols]
+        idx = analysis_period_to_datetimes(cols[0].header.analysis_period)
+
+        results = []
+        pbar = tqdm(total=len(midpoints), desc="Calculating irradiance")
+        with concurrent.futures.ProcessPoolExecutor() as executor:
+            futures = []
+            for _az in midpoints:
+                futures.append(
+                    executor.submit(
+                        _wea.directional_irradiance,
+                        altitude,
+                        _az,
+                        ground_reflectance,
+                        isotropic,
+                    )
+                )
+            for future in concurrent.futures.as_completed(futures):
+                pbar.update(n=1)
+                results.append(future.result())
+
+        # convert results into a massive array
+        headers = []
+        for _az in midpoints:
+            for rad_type, unit in zip(
+                *[["Total", "Direct", "Diffuse", "Reflected"], units]
+            ):
+                headers.append((altitude, _az, rad_type, unit))
+
+        df = pd.DataFrame(
+            np.array(results).reshape(len(midpoints) * 4, 8760),
+            columns=idx,
+            index=pd.MultiIndex.from_tuples(
+                headers, names=["Altitude", "Azimuth", "Type", "Unit"]
+            ),
+        ).T
+
+        df.to_hdf(sp, key="df", complevel=9, complib="blosc:zlib")
+
+        return df
+
+    def detailed_irradiance_matrix(
+        self,
+        location: Location,
+        altitudes: int = 3,
+        azimuths: int = 3,
+        ground_reflectance: float = 0.2,
+        isotropic: bool = True,
+        reload: bool = True,
+    ) -> pd.DataFrame:
+        """Calculate the irradiance in W/m2 for each combination of tilt and orientation.
+
+        Args:
+            location (Location):
+                The location of the site.
+            altitudes (int, optional):
+                The number of altitudes to calculate. Defaults to 3.
+            azimuths (int, optional):
+                The number of azimuths to calculate. Defaults to 3.
+            ground_reflectance (float, optional):
+                The ground reflectance. Defaults to 0.2.
+            isotropic (bool, optional):
+                Calculate isotropic diffuse irradiance. Defaults to True.
+            reload (bool, optional):
+                reload results if they've already been saved.
+
+        Returns:
+            pd.DataFrame:
+                A dataframe containing a huge amount of information!
+        """
+
+        _wea = self.wea(location=location)
+
+        altitudes = self.altitudes(altitudes)
+        azimuths = self.azimuths(azimuths)
+        combinations = list(itertools.product(altitudes, azimuths))
+
+        _dir = Path(hb_folders.default_simulation_folder) / "_lbt_tk_solar"
+        _dir.mkdir(exist_ok=True, parents=True)
+        sp = (
+            _dir
+            / f"{location_to_string(location)}_{ground_reflectance}_{isotropic}_{len(altitudes)}_{len(azimuths)}.h5"
+        )
+
+        if reload and sp.exists():
+            CONSOLE_LOGGER.info(f"Loading results from {sp}.")
+            return pd.read_hdf(sp, key="df")
+
+        # get headers
+        cols = _wea.directional_irradiance(0, 0)
+        units = [header_to_string(i.header) for i in cols]
+        idx = analysis_period_to_datetimes(cols[0].header.analysis_period)
+
+        results = []
+        pbar = tqdm(total=len(combinations), desc="Calculating irradiance")
+        with concurrent.futures.ProcessPoolExecutor() as executor:
+            futures = []
+            for _alt, _az in combinations:
+                futures.append(
+                    executor.submit(
+                        _wea.directional_irradiance,
+                        _alt,
+                        _az,
+                        ground_reflectance,
+                        isotropic,
+                    )
+                )
+            for future in concurrent.futures.as_completed(futures):
+                pbar.update(n=1)
+                results.append(future.result())
+
+        # convert results into a massive array
+        headers = []
+        for _alt, _az in combinations:
+            for rad_type, unit in zip(
+                *[["Total", "Direct", "Diffuse", "Reflected"], units]
+            ):
+                headers.append((_alt, _az, rad_type, unit))
+
+        df = pd.DataFrame(
+            np.array(results).reshape(len(combinations) * 4, 8760),
+            columns=idx,
+            index=pd.MultiIndex.from_tuples(
+                headers, names=["Altitude", "Azimuth", "Type", "Unit"]
+            ),
+        ).T
+
+        df.to_hdf(sp, key="df", complevel=9, complib="blosc:zlib")
+
+        return df
+
+    def plot_tilt_orientation_factor(
+        self,
+        location: Location,
+        ax: plt.Axes = None,
+        altitudes: int = 3,
+        azimuths: int = 3,
+        ground_reflectance: float = 0.2,
+        isotropic: bool = True,
+        irradiance_type: IrradianceType = IrradianceType.TOTAL,
+        analysis_period: AnalysisPeriod = AnalysisPeriod(),
+        agg: str = "sum",
+        **kwargs,
+    ) -> plt.Axes:
+        """Plot a tilt-orientation factor.
+
+        Args:
+            location (Location):
+                The location of the site.
+            ax (plt.Axes, optional):
+                The axes to plot on. Defaults to None.
+            altitudes (int, optional):
+                The number of altitudes to calculate. Defaults to 3.
+            azimuths (int, optional):
+                The number of azimuths to calculate. Defaults to 3.
+            ground_reflectance (float, optional):
+                The ground reflectance. Defaults to 0.2.
+            isotropic (bool, optional):
+                Calculate isotropic diffuse irradiance. Defaults to True.
+            irradiance_type (IrradianceType, optional):
+                The irradiance type to plot. Defaults to IrradianceType.TOTAL.
+            analysis_period (AnalysisPeriod, optional):
+                The analysis period. Defaults to AnalysisPeriod().
+            agg (str, optional):
+                The aggregation method. Defaults to "sum".
+            **kwargs:
+                Keyword arguments to pass to tricontourf.
+        """
+        if ax is None:
+            ax = plt.gca()
+
+        mtx = (
+            self.detailed_irradiance_matrix(
+                location=location,
+                azimuths=azimuths,
+                altitudes=altitudes,
+                ground_reflectance=ground_reflectance,
+                isotropic=isotropic,
+            )
+            .filter(regex=irradiance_type.to_string())
+            .iloc[analysis_period_to_boolean(analysis_period)]
+        ).agg(agg, axis=0) / (1000 if agg == "sum" else 1)
+
+        _max = mtx.max()
+        _max_alt, _max_az, _, unit = mtx.idxmax()
+        if agg == "sum":
+            unit = unit.replace("(W/m2)", "kWh/m$^2$").replace("Irradiance ", "")
+        else:
+            unit = unit.replace("(W/m2)", "Wh/m$^2$").replace("Irradiance ", "")
+
+        if _max == 0:
+            raise ValueError(f"No solar radiation within {analysis_period}.")
+
+        tcf = ax.tricontourf(
+            mtx.index.get_level_values("Azimuth"),
+            mtx.index.get_level_values("Altitude"),
+            mtx.values,
+            extend="max",
+            cmap=kwargs.pop("cmap", "YlOrRd"),
+            levels=kwargs.pop("levels", 51),
+            **kwargs,
+        )
+
+        quantiles = [0.25, 0.5, 0.75, 0.95]
+        quantile_values = mtx.quantile(quantiles).values
+        tcl = ax.tricontour(
+            mtx.index.get_level_values("Azimuth"),
+            mtx.index.get_level_values("Altitude"),
+            mtx.values,
+            levels=quantile_values,
+            colors="k",
+            linestyles="--",
+            alpha=0.5,
+        )
+
+        def cl_fmt(x):
+            return f"{x:,.0f}{unit}"
+
+        _ = ax.clabel(tcl, fontsize="small", fmt=cl_fmt)
+        ax.scatter(_max_az, _max_alt, c="k", s=10, marker="x")
+        alt_offset = (90 / 100) * 0.5 if _max_alt <= 45 else -(90 / 100) * 0.5
+        az_offset = (360 / 100) * 0.5 if _max_az <= 180 else -(360 / 100) * 0.5
+        ha = "left" if _max_az <= 180 else "right"
+        va = "bottom" if _max_alt <= 45 else "top"
+        ax.text(
+            _max_az + az_offset,
+            _max_alt + alt_offset,
+            f"{_max:,.0f}{unit}\n({_max_az:0.0f}°, {_max_alt:0.0f}°)",
+            ha=ha,
+            va=va,
+            c="k",
+            weight="bold",
+            size="small",
+        )
+
+        # format plot
+        for spine in ["top", "right"]:
+            ax.spines[spine].set_visible(False)
+        ax.xaxis.set_major_locator(mticker.MultipleLocator(base=30))
+        ax.yaxis.set_major_locator(mticker.MultipleLocator(base=10))
+
+        cb = plt.colorbar(
+            tcf,
+            ax=ax,
+            orientation="vertical",
+            drawedges=False,
+            fraction=0.05,
+            aspect=25,
+            pad=0.02,
+            label=unit,
+        )
+        cb.outline.set_visible(False)
+        for quantile_val in quantile_values:
+            cb.ax.plot([0, 1], [quantile_val] * 2, "k", ls="--", alpha=0.5)
+
+        ax.set_title(
+            (
+                f"{location_to_string(location)}, from {self.source}\n{irradiance_type.to_string()} "
+                f"Irradiance ({agg.upper()})\n{describe_analysis_period(analysis_period)}"
+            )
+        )
+        ax.set_xlabel("Panel orientation (clockwise from North at 0°)")
+        ax.set_ylabel("Panel tilt (0° facing the horizon, 90° facing the sky)")
+        return ax
+
+    def plot_directional_irradiance(
+        self,
+        location: Location,
+        ax: plt.Axes = None,
+        altitude: float | int = 0,
+        directions: int = 32,
+        ground_reflectance: float = 0.2,
+        isotropic: bool = True,
+        irradiance_type: IrradianceType = IrradianceType.TOTAL,
+        analysis_period: AnalysisPeriod = AnalysisPeriod(),
+        agg: str = "sum",
+        labelling: bool = True,
+        **kwargs,
+    ) -> plt.Axes:
+        """Plot the directional irradiance for the given configuration.
+
+        Args:
+            location (Location):
+                The location of the site.
+            ax (plt.Axes, optional):
+                The axes to plot on. Defaults to None.
+            altitude (int, optional):
+                The altitude to calculate. Defaults to 0 for vertical surfaces.
+            directions (int, optional):
+                The number of directions to calculate. Defaults to 32.
+            ground_reflectance (float, optional):
+                The ground reflectance. Defaults to 0.2.
+            isotropic (bool, optional):
+                Calculate isotropic diffuse irradiance. Defaults to True.
+            irradiance_type (IrradianceType, optional):
+                The irradiance type to plot. Defaults to IrradianceType.TOTAL.
+            analysis_period (AnalysisPeriod, optional):
+                The analysis period. Defaults to AnalysisPeriod().
+            agg (str, optional):
+                The aggregation method. Defaults to "sum".
+            labelling (bool, optional):
+                Label the plot. Defaults to True.
+            **kwargs:
+                Keyword arguments to pass to tricontourf.
+
+        Returns:
+            plt.Axes: The matplotlib axes.
+        """
+
+        if ax is None:
+            _, ax = plt.subplots(subplot_kw={"projection": "polar"})
+
+        format_polar_plot(ax)
+
+        cmap = plt.get_cmap(
+            kwargs.pop(
+                "cmap",
+                "Spectral_r",
+            )
+        )
+        width = kwargs.pop("width", 0.9)
+        vmin = kwargs.pop("vmin", 0)
+
+        # plot data
+        data = self.directional_irradiance_matrix(
+            location=location,
+            altitude=altitude,
+            directions=directions,
+            ground_reflectance=ground_reflectance,
+            isotropic=isotropic,
+        ).filter(regex=irradiance_type.to_string()).iloc[
+            analysis_period_to_boolean(analysis_period)
+        ].agg(
+            agg, axis=0
+        ) / (
+            1000 if agg == "sum" else 1
+        )
+
+        _max = data.max()
+        _, _max_az, _, unit = data.idxmax()
+        if agg == "sum":
+            unit = unit.replace("(W/m2)", "kWh/m$^2$").replace("Irradiance ", "")
+        else:
+            unit = unit.replace("(W/m2)", "Wh/m$^2$").replace("Irradiance ", "")
+        if _max == 0:
+            raise ValueError(f"No solar radiation within {analysis_period}.")
+
+        vmax = kwargs.pop("vmax", data.max())
+        colors = [cmap(i) for i in np.interp(data.values, [vmin, vmax], [0, 1])]
+        thetas = np.deg2rad(data.index.get_level_values("Azimuth"))
+        radiis = data.values
+        bars = ax.bar(
+            thetas,
+            radiis,
+            zorder=2,
+            width=width * np.deg2rad(360 / len(thetas)),
+            color=colors,
+        )
+
+        # colorbar
+        if (data.min() < vmin) and (data.max() > vmax):
+            extend = "both"
+        elif data.min() < vmin:
+            extend = "min"
+        elif data.max() > vmax:
+            extend = "max"
+        else:
+            extend = "neither"
+        norm = plt.Normalize(vmin=vmin, vmax=vmax)
+        sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+        sm.set_array([])
+        cb = plt.colorbar(sm, ax=ax, orientation="vertical", label=unit, extend=extend)
+        cb.outline.set_visible(False)
+
+        # labelling
+        if labelling:
+            for rect, idx, val, colr in list(
+                zip(
+                    *[bars, data.index.get_level_values("Azimuth"), data.values, colors]
+                )
+            ):
+                if len(set(data.values)) == 1:
+                    ax.text(
+                        0,
+                        0,
+                        textwrap.fill(
+                            f"{val:,.0f}{unit} {agg} total insolation",
+                            16,
+                        ),
+                        ha="center",
+                        va="center",
+                        bbox={
+                            "ec": "none",
+                            "fc": "w",
+                            "alpha": 0.5,
+                            "boxstyle": "round,pad=0.3",
+                        },
+                    )
+                    break
+                if val == data.max():
+                    rect.set_edgecolor("k")
+                if val > data.max() / 1.5:
+                    ax.text(
+                        np.deg2rad(idx),
+                        val,
+                        f" {val:,.0f}{unit} ",
+                        rotation_mode="anchor",
+                        rotation=(-idx + 90) if idx < 180 else 180 + (-idx + 90),
+                        ha="right" if idx < 180 else "left",
+                        va="center",
+                        fontsize="xx-small",
+                        c=contrasting_color(colr)
+                        # bbox=dict(ec="none", fc="w", alpha=0.5, boxstyle="round,pad=0.3"),
+                    )
+
+        _ = ax.set_title(
+            (
+                f"{location_to_string(location)}, from {self.source}\n"
+                f"{irradiance_type.to_string()} irradiance ({agg.upper()}) at {altitude}° tilt\n"
+                f"{describe_analysis_period(analysis_period)}"
+            )
+        )
+
+        return ax
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind.py
new file mode 100644
index 00000000..9586980d
--- /dev/null
+++ b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind.py
@@ -0,0 +1,1992 @@
+"""Methods for working with time-indexed wind data."""
+
+# pylint: disable=E0401
+import calendar
+import json
+import textwrap
+import warnings
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+# pylint: enable=E0401
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
+import numpy as np
+import pandas as pd
+from ladybug.dt import DateTime
+from ladybug.epw import EPW
+from matplotlib.cm import ScalarMappable
+from matplotlib.collections import PatchCollection
+from matplotlib.colors import Colormap, ListedColormap, Normalize, to_hex
+from matplotlib.patches import Rectangle
+from pandas.tseries.frequencies import to_offset
+from scipy.stats import weibull_min
+
+from .categorical.categories import BEAUFORT_CATEGORIES
+from .helpers import (
+    OpenMeteoVariable,
+    angle_from_north,
+    angle_to_vector,
+    cardinality,
+    circular_weighted_mean,
+    rolling_window,
+    scrape_meteostat,
+    scrape_openmeteo,
+    wind_speed_at_height,
+)
+from .ladybug_extension.analysisperiod import (
+    AnalysisPeriod,
+    analysis_period_to_boolean,
+    analysis_period_to_datetimes,
+    describe_analysis_period,
+)
+from .plot._timeseries import timeseries
+from .plot.utilities import contrasting_color, format_polar_plot
+
+
+@dataclass(init=True, eq=True, repr=True)
+class Wind:
+    """An object containing historic, time-indexed wind data.
+
+    Args:
+        wind_speeds (list[int | float | np.number]):
+            An iterable of wind speeds in m/s.
+        wind_directions (list[int | float | np.number]):
+            An iterable of wind directions in degrees from North (with North at 0-degrees).
+        datetimes (Union[pd.DatetimeIndex, list[Union[datetime, np.datetime64, pd.Timestamp]]]):
+            An iterable of datetime-like objects.
+        height_above_ground (float, optional):
+            The height above ground (in m) where the input wind speeds and directions were collected. Defaults to 10m.
+        source (str, optional):
+                A source string to describe where the input data comes from. Defaults to None.
+    """
+
+    wind_speeds: list[float]
+    wind_directions: list[float]
+    datetimes: list[datetime] | pd.DatetimeIndex
+    height_above_ground: float
+    source: str = None
+
+    def __post_init__(self):
+        if self.height_above_ground < 0.1:
+            raise ValueError("Height above ground must be >= 0.1m.")
+
+        if not (
+            len(self.wind_speeds) == len(self.wind_directions) == len(self.datetimes)
+        ):
+            raise ValueError(
+                "wind_speeds, wind_directions and datetimes must be the same length."
+            )
+
+        if len(self.wind_speeds) <= 1:
+            raise ValueError(
+                "wind_speeds, wind_directions and datetimes must be at least 2 items long."
+            )
+
+        if len(set(self.datetimes)) != len(self.datetimes):
+            raise ValueError("datetimes contains duplicates.")
+
+        # convert to lists
+        self.wind_speeds = np.array(self.wind_speeds)
+        self.wind_directions = np.array(self.wind_directions)
+        self.datetimes = pd.DatetimeIndex(self.datetimes)
+
+        # validate wind speeds and directions
+        if np.any(np.isnan(self.wind_speeds)):
+            raise ValueError("wind_speeds contains null values.")
+
+        if np.any(np.isnan(self.wind_directions)):
+            raise ValueError("wind_directions contains null values.")
+
+        if np.any(self.wind_speeds < 0):
+            raise ValueError("wind_speeds must be >= 0")
+        if np.any(self.wind_directions < 0) or np.any(self.wind_directions > 360):
+            raise ValueError("wind_directions must be within 0-360")
+        self.wind_directions = self.wind_directions % 360
+
+    def __len__(self) -> int:
+        return len(self.datetimes)
+
+    def __repr__(self) -> str:
+        """The printable representation of the given object"""
+        if self.source:
+            return f"{self.__class__.__name__}(@{self.height_above_ground}m) from {self.source}"
+
+        return (
+            f"{self.__class__.__name__}({min(self.datetimes):%Y-%m-%d} to "
+            f"{max(self.datetimes):%Y-%m-%d}, n={len(self.datetimes)} @{self.freq}, "
+            f"@{self.height_above_ground}m) NO SOURCE"
+        )
+
+    def __str__(self) -> str:
+        """The string representation of the given object"""
+        return self.__repr__()
+
+    #################
+    # CLASS METHODS #
+    #################
+
+    def to_dict(self) -> dict:
+        """Return the object as a dictionary."""
+
+        return {
+            "_t": "BH.oM.LadybugTools.Wind",
+            "wind_speeds": [float(i) for i in self.wind_speeds],
+            "wind_directions": [float(i) for i in self.wind_directions],
+            "datetimes": [i.isoformat() for i in self.datetimes],
+            "height_above_ground": self.height_above_ground,
+            "source": self.source,
+        }
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "Wind":
+        """Create this object from a dictionary."""
+
+        return cls(
+            wind_speeds=d["wind_speeds"],
+            wind_directions=d["wind_directions"],
+            datetimes=pd.to_datetime(d["datetimes"]),
+            height_above_ground=d["height_above_ground"],
+            source=d["source"],
+        )
+
+    def to_json(self) -> str:
+        """Convert this object to a JSON string."""
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_string: str) -> "Wind":
+        """Create this object from a JSON string."""
+
+        return cls.from_dict(json.loads(json_string))
+
+    def to_file(self, path: Path) -> Path:
+        """Convert this object to a JSON file."""
+
+        if Path(path).suffix != ".json":
+            raise ValueError("path must be a JSON file.")
+
+        with open(Path(path), "w") as fp:
+            fp.write(self.to_json())
+
+        return Path(path)
+
+    @classmethod
+    def from_file(cls, path: Path) -> "Wind":
+        """Create this object from a JSON file."""
+        with open(Path(path), "r") as fp:
+            return cls.from_json(fp.read())
+
+    def to_csv(self, path: Path) -> Path:
+        """Save this object as a csv file.
+
+        Args:
+            path (Path):
+                The path containing the CSV file.
+
+        Returns:
+            Path:
+                The resultant CSV file.
+        """
+        csv_path = Path(path)
+        self.df.to_csv(csv_path)
+        return csv_path
+
+    @classmethod
+    def from_dataframe(
+        cls,
+        df: pd.DataFrame,
+        wind_speed_column: Any,
+        wind_direction_column: Any,
+        height_above_ground: float = 10,
+        source: str = "DataFrame",
+    ) -> "Wind":
+        """Create a Wind object from a Pandas DataFrame, with WindSpeed and WindDirection columns.
+
+        Args:
+            df (pd.DataFrame):
+                A DataFrame object containing speed and direction columns, and a datetime index.
+            wind_speed_column (str):
+                The name of the column where wind-speed data exists.
+            wind_direction_column (str):
+                The name of the column where wind-direction data exists.
+            height_above_ground (float, optional):
+                Defaults to 10m.
+            source (str, optional):
+                A source string to describe where the input data comes from. Defaults to "DataFrame"".
+        """
+
+        if not isinstance(df, pd.DataFrame):
+            raise ValueError(f"df must be of type {type(pd.DataFrame)}")
+
+        if not isinstance(df.index, pd.DatetimeIndex):
+            raise ValueError(
+                f"The DataFrame's index must be of type {type(pd.DatetimeIndex)}"
+            )
+
+        # remove NaN values
+        df.dropna(axis=0, how="any", inplace=True)
+
+        # remove duplicates in input dataframe
+        df = df.loc[~df.index.duplicated()]
+
+        return cls(
+            wind_speeds=df[wind_speed_column].tolist(),
+            wind_directions=df[wind_direction_column].tolist(),
+            datetimes=df.index.tolist(),
+            height_above_ground=height_above_ground,
+            source=source,
+        )
+
+    @classmethod
+    def from_csv(
+        cls,
+        csv_path: Path,
+        wind_speed_column: str,
+        wind_direction_column: str,
+        height_above_ground: float = 10,
+        **kwargs,
+    ) -> "Wind":
+        """Create a Wind object from a csv containing wind speed and direction columns.
+
+        Args:
+            csv_path (Path):
+                The path to the CSV file containing speed and direction columns, and a datetime index.
+            wind_speed_column (str):
+                The name of the column where wind-speed data exists.
+            wind_direction_column (str):
+                The name of the column where wind-direction data exists.
+            height_above_ground (float, optional):
+                Defaults to 10m.
+            **kwargs:
+                Additional keyword arguments passed to pd.read_csv.
+        """
+        csv_path = Path(csv_path)
+        df = pd.read_csv(csv_path, **kwargs)
+        return cls.from_dataframe(
+            df,
+            wind_speed_column=wind_speed_column,
+            wind_direction_column=wind_direction_column,
+            height_above_ground=height_above_ground,
+            source=csv_path.name,
+        )
+
+    @classmethod
+    def from_epw(cls, epw: Path | EPW) -> "Wind":
+        """Create a Wind object from an EPW file or object.
+
+        Args:
+            epw (Path | EPW):
+                The path to the EPW file, or an EPW object.
+        """
+
+        if isinstance(epw, (str, Path)):
+            source = Path(epw).name
+            epw = EPW(epw)
+        else:
+            source = Path(epw.file_path).name
+
+        return cls(
+            wind_speeds=epw.wind_speed.values,
+            wind_directions=epw.wind_direction.values,
+            datetimes=analysis_period_to_datetimes(AnalysisPeriod()),
+            height_above_ground=10,
+            source=source,
+        )
+
+    @classmethod
+    def from_openmeteo(
+        cls,
+        latitude: float,
+        longitude: float,
+        start_date: datetime | str,
+        end_date: datetime | str,
+    ) -> "Wind":
+        """Create a Wind object from data obtained from the Open-Meteo database of historic weather station data.
+
+        Args:
+            latitude (float):
+                The latitude of the target site, in degrees.
+            longitude (float):
+                The longitude of the target site, in degrees.
+            start_date (datetime | str):
+                The start-date from which records will be obtained.
+            end_date (datetime | str):
+                The end-date beyond which records will be ignored.
+        """
+
+        df = scrape_openmeteo(
+            latitude=latitude,
+            longitude=longitude,
+            start_date=start_date,
+            end_date=end_date,
+            variables=[
+                OpenMeteoVariable.WINDSPEED_10M,
+                OpenMeteoVariable.WINDDIRECTION_10M,
+            ],
+            convert_units=True,
+        )
+
+        df.dropna(how="any", axis=0, inplace=True)
+        wind_speeds = df["Wind Speed (m/s)"].tolist()
+        wind_directions = df["Wind Direction (degrees)"].tolist()
+        if len(wind_speeds) == 0 or len(wind_directions) == 0:
+            raise ValueError(
+                "OpenMeteo did not return any data for the given latitude, longitude and start/end dates."
+            )
+        datetimes = df.index.tolist()
+
+        return cls(
+            wind_speeds=wind_speeds,
+            wind_directions=wind_directions,
+            datetimes=datetimes,
+            height_above_ground=10,
+            source="OpenMeteo",
+        )
+
+    @classmethod
+    def from_meteostat(
+        cls,
+        latitude: float,
+        longitude: float,
+        start_date: datetime | str,
+        end_date: datetime | str,
+        altitude: float = 10,
+    ) -> "Wind":
+        """Create a Wind object from data obtained from the Meteostat database of historic weather station data.
+
+        Args:
+            latitude (float):
+                The latitude of the target site, in degrees.
+            longitude (float):
+                The longitude of the target site, in degrees.
+            start_date (datetime | str):
+                The start-date from which records will be obtained.
+            end_date (datetime | str):
+                The end-date beyond which records will be ignored.
+            altitude (float, optional):
+                The altitude of the target site, in meters. Defaults to 10.
+        """
+
+        df = scrape_meteostat(
+            latitude=latitude,
+            longitude=longitude,
+            start_date=start_date,
+            end_date=end_date,
+            altitude=altitude,
+            convert_units=True,
+        )[["Wind Speed (m/s)", "Wind Direction (degrees)"]]
+
+        df.dropna(axis=0, inplace=True, how="any")
+
+        return cls.from_dataframe(
+            df=df,
+            wind_speed_column="Wind Speed (m/s)",
+            wind_direction_column="Wind Direction (degrees)",
+            height_above_ground=altitude,
+            source="Meteostat",
+        )
+
+    @classmethod
+    def from_average(
+        cls, wind_objects: list["Wind"], weights: list[float] = None
+    ) -> "Wind":
+        """Create an average Wind object from a set of input Wind objects, with optional weighting for each."""
+
+        # create default weightings if None
+        if weights is None:
+            weights = [1 / len(wind_objects)] * len(wind_objects)
+        else:
+            if sum(weights) != 1:
+                raise ValueError("weights must total 1.")
+
+        # create source string
+        source = []
+        for src, wgt in list(zip([wind_objects, weights])):
+            source.append(f"{src.source}|{wgt}")
+        source = "_".join(source)
+
+        # align collections so that intersection only is created
+        df_ws = pd.concat([i.ws for i in wind_objects], axis=1).dropna()
+        df_wd = pd.concat([i.wd for i in wind_objects], axis=1).dropna()
+
+        # construct the weighted means
+        wd_avg = np.array(
+            [circular_weighted_mean(i, weights) for _, i in df_wd.iterrows()]
+        )
+        ws_avg = np.average(df_ws, axis=1, weights=weights)
+        dts = df_ws.index
+
+        # return the new averaged object
+        return cls(
+            wind_speeds=ws_avg.tolist(),
+            wind_directions=wd_avg.tolist(),
+            datetimes=dts,
+            height_above_ground=np.average(
+                [i.height_above_ground for i in wind_objects], weights=weights
+            ),
+            source=source,
+        )
+
+    @classmethod
+    def from_uv(
+        cls,
+        u: list[float],
+        v: list[float],
+        datetimes: list[datetime],
+        height_above_ground: float = 10,
+        source: str = None,
+    ) -> "Wind":
+        """Create a Wind object from a set of U, V wind components.
+
+        Args:
+            u (list[float]):
+                An iterable of U (eastward) wind components in m/s.
+            v (list[float]):
+                An iterable of V (northward) wind components in m/s.
+            datetimes (list[datetime]):
+                An iterable of datetime-like objects.
+            height_above_ground (float, optional):
+                The height above ground (in m) where the input wind speeds and directions were collected.
+                Defaults to 10m.
+            source (str, optional):
+                A source string to describe where the input data comes from. Defaults to None.
+
+        Returns:
+            Wind:
+                A Wind object!
+        """
+
+        # convert UV into angle and magnitude
+        wind_direction = angle_from_north(np.stack([u, v]))
+        wind_speed = np.sqrt(np.square(u) + np.square(v))
+
+        if any(wind_direction[wind_speed == 0] == 90):
+            warnings.warn(
+                "Some input vectors have velocity of 0. This is not bad, but can mean directions may be misreported."
+            )
+
+        return cls(
+            wind_speeds=wind_speed.tolist(),
+            wind_directions=wind_direction.tolist(),
+            datetimes=datetimes,
+            height_above_ground=height_above_ground,
+            source=source,
+        )
+
+    # ##############
+    # # PROPERTIES #
+    # ##############
+
+    @property
+    def freq(self) -> str:
+        """Return the inferred frequency of the datetimes associated with this object."""
+        freq = pd.infer_freq(self.datetimes)
+        if freq is None:
+            return "inconsistent"
+        return freq
+
+    @property
+    def index(self) -> pd.DatetimeIndex:
+        """Get the datetimes as a pandas DateTimeIndex."""
+        return pd.to_datetime(self.datetimes)
+
+    @property
+    def ws(self) -> pd.Series:
+        """Convenience accessor for wind speeds as a time-indexed pd.Series object."""
+        return pd.Series(
+            self.wind_speeds, index=self.index, name="Wind Speed (m/s)"
+        ).sort_index(ascending=True, inplace=False)
+
+    @property
+    def wd(self) -> pd.Series:
+        """Convenience accessor for wind directions as a time-indexed pd.Series object."""
+        return pd.Series(
+            self.wind_directions, index=self.index, name="Wind Direction (degrees)"
+        ).sort_index(ascending=True, inplace=False)
+
+    @property
+    def df(self) -> pd.DataFrame:
+        """Convenience accessor for wind direction and speed as a time-indexed pd.DataFrame object."""
+        return pd.concat([self.wd, self.ws], axis=1)
+
+    @property
+    def calm_datetimes(self) -> list[datetime]:
+        """Return the datetimes where wind speed is < 0.1.
+
+        Returns:
+            list[datetime]:
+                "Calm" wind datetimes.
+        """
+        return self.ws[self.ws <= 0.1].index.tolist()  # pylint: disable=E1136
+
+    @property
+    def uv(self) -> pd.DataFrame:
+        """Return the U and V wind components in m/s."""
+        u, v = angle_to_vector(self.wd)
+        return pd.concat([u * self.ws, v * self.ws], axis=1, keys=["u", "v"])
+
+    @property
+    def mean_uv(self) -> list[float, float]:
+        """Calculate the average U and V wind components in m/s.
+
+        Returns:
+            list[float, float]:
+                A tuple containing the average U and V wind components.
+        """
+        return self.uv.mean().tolist()
+
+    def mean_speed(self, remove_calm: bool = False) -> float:
+        """Return the mean wind speed for this object.
+
+        Args:
+            remove_calm (bool, optional):
+                Remove calm wind speeds before calculating the mean. Defaults to False.
+
+        Returns:
+            float:
+                Mean wind speed.
+
+        """
+        return np.linalg.norm(
+            self.filter_by_speed(min_speed=1e-10 if remove_calm else 0).mean_uv
+        )
+
+    @property
+    def mean_direction(self) -> tuple[float, float]:
+        """Calculate the average speed and direction for this object.
+
+        Returns:
+            tuple[float, float]:
+                A tuple containing the average speed and direction.
+        """
+        return angle_from_north(self.mean_uv)
+
+    @property
+    def min_speed(self) -> float:
+        """Return the min wind speed for this object."""
+        return self.ws.min()
+
+    @property
+    def max_speed(self) -> float:
+        """Return the max wind speed for this object."""
+        return self.ws.max()
+
+    @property
+    def median_speed(self) -> float:
+        """Return the median wind speed for this object."""
+        return self.ws.median()
+
+    ###################
+    # GENERAL METHODS #
+    ###################
+
+    def calm(self, threshold: float = 1e-10) -> float:
+        """Return the proportion of timesteps "calm" (i.e. wind-speed ≤ 1e-10)."""
+        return (self.ws < threshold).sum() / len(self.ws)
+
+    def percentile(self, percentile: float) -> float:
+        """Calculate the wind speed at the given percentile.
+
+        Args:
+            percentile (float):
+                The percentile to calculate.
+
+        Returns:
+            float:
+                Wind speed at the given percentile.
+        """
+        return self.ws.quantile(percentile)
+
+    def resample(self, rule: pd.DateOffset | pd.Timedelta | str) -> "Wind":
+        """Resample the wind data collection to a different timestep. This can only be used to downsample.
+
+        Args:
+            rule (Union[pd.DateOffset, pd.Timedelta, str]):
+                A rule for resampling. This uses the same inputs as a Pandas Series.resample() method.
+
+        Returns:
+            Wind:
+                A wind data collection object!
+        """
+
+        warnings.warn(
+            (
+                "Resampling wind speeds and direction is generally not advisable. "
+                "When input directions are opposing, the average returned is likely inaccurate, "
+                "and the average speed does not include any outliers. USE WITH CAUTION!"
+            )
+        )
+
+        common_dt = pd.to_datetime("2000-01-01")
+        f_a = common_dt + to_offset(self.freq)
+        f_b = common_dt + to_offset(rule)
+        if f_a < f_b:
+            raise ValueError("Resampling can only be used to downsample.")
+
+        resampled_speeds = self.ws.resample(rule).mean()
+        resampled_datetimes = resampled_speeds.index.tolist()
+        resampled_directions = self.wd.resample(rule).apply(circular_weighted_mean)
+
+        return Wind(
+            wind_speeds=resampled_speeds.tolist(),
+            wind_directions=resampled_directions.tolist(),
+            datetimes=resampled_datetimes,
+            height_above_ground=self.height_above_ground,
+            source=self.source,
+        )
+
+    def to_height(
+        self,
+        target_height: float,
+        terrain_roughness_length: float = 1,
+        log_function: bool = True,
+    ) -> "Wind":
+        """Translate the object to a different height above ground.
+
+        Args:
+            target_height (float):
+                Height to translate to (in m).
+            terrain_roughness_length (float, optional):
+                Terrain roughness (how big objects are to adjust translation). Defaults to 1.
+            log_function (bool, optional):
+                Whether to use log-function or pow-function. Defaults to True.
+
+        Returns:
+            Wind:
+                A translated Wind object.
+        """
+        ws = wind_speed_at_height(
+            reference_value=self.ws,
+            reference_height=self.height_above_ground,
+            target_height=target_height,
+            terrain_roughness_length=terrain_roughness_length,
+            log_function=log_function,
+        )
+        return Wind(
+            wind_speeds=ws.tolist(),
+            wind_directions=self.wd.tolist(),
+            datetimes=self.datetimes,
+            height_above_ground=target_height,
+            source=f"{self.source} translated to {target_height}m",
+        )
+
+    def apply_directional_factors(
+        self, directions: int, factors: tuple[float]
+    ) -> "Wind":
+        """Adjust wind speed values by a set of factors per direction. Factors start at north, and move clockwise.
+
+        Example:
+            >>> wind = Wind.from_epw(epw_path)
+            >>> wind.apply_directional_factors(
+            ...     directions=4,
+            ...     factors=(0.5, 0.75, 1, 0.75)
+            ... )
+
+        Where northern winds would be multiplied by 0.5, eastern winds by 0.75, southern winds by 1, and western winds
+        by 0.75.
+
+        Args:
+            directions (int):
+                The number of directions to bin wind-directions into.
+            factors (tuple[float], optional):
+                Adjustment factors per direction.
+
+        Returns:
+            Wind:
+                An adjusted Wind object.
+        """
+
+        factors = np.array(factors).tolist()
+
+        if len(factors) != directions:
+            raise ValueError(
+                f"number of factors ({len(factors)}) must equal number of directions ({directions})"
+            )
+
+        direction_binned = self.bin_data(directions=directions)
+        directional_factor_lookup = dict(
+            zip(*[np.roll(np.unique(direction_binned.iloc[:, 0]), 1), factors])
+        )
+
+        adjusted_wind_speed = self.ws * [
+            directional_factor_lookup[i] for i in direction_binned.iloc[:, 0]
+        ]
+
+        return Wind(
+            wind_speeds=adjusted_wind_speed.tolist(),
+            wind_directions=self.wd.tolist(),
+            datetimes=self.datetimes,
+            height_above_ground=self.height_above_ground,
+            source=f"{self.source} adjusted by factors {factors}",
+        )
+
+    def filter_by_analysis_period(
+        self,
+        analysis_period: AnalysisPeriod,
+    ) -> "Wind":
+        """Filter the current object by a ladybug AnalysisPeriod object.
+
+        Args:
+            analysis_period (AnalysisPeriod):
+                An AnalysisPeriod object.
+
+        Returns:
+            Wind:
+                A dataset describing historic wind speed and direction relationship.
+        """
+
+        if analysis_period == AnalysisPeriod():
+            return self
+
+        if analysis_period.timestep != 1:
+            raise ValueError("The timestep of the analysis period must be 1 hour.")
+
+        # remove 29th Feb dates where present
+        df = self.df
+        df = df[~((df.index.month == 2) & (df.index.day == 29))]
+
+        # filter available data
+        possible_datetimes = [
+            DateTime(dt.month, dt.day, dt.hour, dt.minute) for dt in df.index
+        ]
+        lookup = dict(
+            zip(AnalysisPeriod().datetimes, analysis_period_to_boolean(analysis_period))
+        )
+        mask = [lookup[i] for i in possible_datetimes]
+        df = df[mask]
+
+        if len(df) == 0:
+            raise ValueError("No data remains within the given analysis_period filter.")
+
+        return Wind.from_dataframe(
+            df,
+            wind_speed_column="Wind Speed (m/s)",
+            wind_direction_column="Wind Direction (degrees)",
+            height_above_ground=self.height_above_ground,
+            source=f"{self.source} (filtered to {describe_analysis_period(analysis_period)})",
+        )
+
+    def filter_by_boolean_mask(self, mask: tuple[bool]) -> "Wind":
+        """Filter the current object by a boolean mask.
+
+        Returns:
+            Wind:
+                A dataset describing historic wind speed and direction relationship.
+        """
+
+        if len(mask) != len(self.ws):
+            raise ValueError(
+                "The length of the boolean mask must match the length of the current object."
+            )
+
+        if sum(mask) == len(self.ws):
+            return self
+
+        if len(self.ws.values[mask]) == 0:
+            raise ValueError("No data remains within the given boolean filters.")
+
+        return Wind(
+            wind_speeds=self.ws.values[mask].tolist(),
+            wind_directions=self.wd.values[mask].tolist(),
+            datetimes=self.datetimes[mask],
+            height_above_ground=self.height_above_ground,
+            source=f"{self.source} (filtered)",
+        )
+
+    def filter_by_time(
+        self,
+        months: tuple[float] = tuple(range(1, 13, 1)),  # type: ignore
+        hours: tuple[int] = tuple(range(0, 24, 1)),  # type: ignore
+        years: tuple[int] = tuple(range(1900, 2100, 1)),
+    ) -> "Wind":
+        """Filter the current object by month and hour.
+
+        Args:
+            months (list[int], optional):
+                A list of months. Defaults to all months.
+            hours (list[int], optional):
+                A list of hours. Defaults to all hours.
+            years (tuple[int], optional):
+                A list of years to include. Default to all years since 1900.
+
+        Returns:
+            Wind:
+                A dataset describing historic wind speed and direction relationship.
+        """
+
+        mask = np.all(
+            [
+                self.datetimes.year.isin(years),
+                self.datetimes.month.isin(months),
+                self.datetimes.hour.isin(hours),
+            ],
+            axis=0,
+        ).flatten()
+
+        if mask.sum() == len(self.ws):
+            return self
+
+        if len(self.ws.iloc[mask]) == 0:
+            raise ValueError("No data remains within the given time filters.")
+
+        filtered_by = []
+        if len(years) != len(range(1900, 2100, 1)):
+            filtered_by.append("year")
+        if len(months) != len(range(1, 13, 1)):
+            filtered_by.append("month")
+        if len(hours) != len(range(0, 24, 1)):
+            filtered_by.append("hour")
+
+        filtered_by = ", ".join(filtered_by)
+        return Wind(
+            wind_speeds=self.ws.values[mask].tolist(),
+            wind_directions=self.wd.values[mask].tolist(),
+            datetimes=self.datetimes[mask],
+            height_above_ground=self.height_above_ground,
+            source=f"{self.source} (filtered {filtered_by})",
+        )
+
+    def filter_by_direction(
+        self, left_angle: float = 0, right_angle: float = 360, inclusive: bool = True
+    ) -> "Wind":
+        """Filter the current object by wind direction, based on the angle as observed from a location.
+
+        Args:
+            left_angle (float):
+                The left-most angle, to the left of which wind speeds and directions will be removed.
+            right_angle (float):
+                The right-most angle, to the right of which wind speeds and directions will be removed.
+            inclusive (bool, optional):
+                Include values that are exactly the left or right angle values.
+
+        Return:
+            Wind:
+                A Wind object!
+        """
+
+        if left_angle < 0 or right_angle > 360:
+            raise ValueError("Angle limits must be between 0 and 360 degrees.")
+
+        if left_angle == 0 and right_angle == 360:
+            return self
+
+        if (left_angle == right_angle) or (left_angle == 360 and right_angle == 0):
+            raise ValueError("Angle limits cannot be identical.")
+
+        if left_angle > right_angle:
+            if inclusive:
+                mask = (self.wd >= left_angle) | (self.wd <= right_angle)
+            else:
+                mask = (self.wd > left_angle) | (self.wd < right_angle)
+        else:
+            if inclusive:
+                mask = (self.wd >= left_angle) & (self.wd <= right_angle)
+            else:
+                mask = (self.wd > left_angle) & (self.wd < right_angle)
+
+        if len(self.ws.values[mask]) == 0:
+            raise ValueError("No data remains within the given direction filter.")
+
+        return Wind(
+            wind_speeds=self.ws.values[mask].tolist(),
+            wind_directions=self.wd.values[mask].tolist(),
+            datetimes=self.datetimes[mask],
+            height_above_ground=self.height_above_ground,
+            source=f"{self.source} filtered by direction ({left_angle}-{right_angle})",
+        )
+
+    def filter_by_speed(
+        self, min_speed: float = 0, max_speed: float = 999, inclusive: bool = True
+    ) -> "Wind":
+        """Filter the current object by wind speed, based on given low-high limit values.
+
+        Args:
+            min_speed (float):
+                The lowest speed to include. Values below this wil be removed.
+            max_speed (float):
+                The highest speed to include. Values above this wil be removed.
+            inclusive (bool, optional):
+                Include values that are exactly the left or right angle values.
+
+        Return:
+            Wind:
+                A Wind object!
+        """
+
+        if min_speed < 0:
+            raise ValueError("min_speed cannot be negative.")
+
+        if max_speed <= min_speed:
+            raise ValueError("min_speed must be less than max_speed.")
+
+        if min_speed == 0 and max_speed == 999:
+            return self
+
+        if inclusive:
+            mask = (self.ws >= min_speed) & (self.ws <= max_speed)
+        else:
+            mask = (self.ws > min_speed) & (self.ws < max_speed)
+
+        if len(self.ws.values[mask]) == 0:
+            raise ValueError("No data remains within the given speed filter.")
+
+        return Wind(
+            wind_speeds=self.ws.values[mask].tolist(),
+            wind_directions=self.wd.values[mask].tolist(),
+            datetimes=self.datetimes[mask],
+            height_above_ground=self.height_above_ground,
+            source=f"{self.source} filtered by speed ({min_speed}-{max_speed})",
+        )
+
+    @staticmethod
+    def _direction_bin_edges(directions: int) -> list[float]:
+        """Calculate the bin edges for a given number of directions.
+
+        Args:
+            directions (int):
+                The number of directions to calculate bin edges for.
+
+        Returns:
+            list[float]:
+                A list of bin edges.
+        """
+
+        if directions <= 2:
+            raise ValueError("directions must be > 2.")
+
+        direction_bin_edges = np.unique(
+            (
+                (np.linspace(0, 360, directions + 1) - ((360 / directions) / 2)) % 360
+            ).tolist()
+            + [0, 360]
+        )
+        return direction_bin_edges
+
+    def process_direction_data(
+        self, directions: int
+    ) -> tuple[pd.Series, list[tuple[float]]]:
+        """Process wind direction data for this object.
+
+        Args:
+            directions (int):
+                The number of directions to calculate bin edges for.
+
+        Returns:
+            tuple[pd.Series, list[tuple[float]]]:
+                - A pd.Series containing the processed categorical data.
+                - A list of tuples containing the bin edges.
+        """
+
+        # bin the wind directions
+        categories = pd.cut(
+            self.wd,
+            bins=Wind._direction_bin_edges(directions),
+            include_lowest=True,
+        )
+
+        x = [tuple([i.left, i.right]) for i in categories.cat.categories.tolist()]
+        bin_tuples = x[1:-1]
+        bin_tuples.append((bin_tuples[-1][1], bin_tuples[0][0]))
+
+        mapper = dict(
+            zip(
+                *[
+                    categories.cat.categories.tolist(),
+                    [bin_tuples[-1]] + bin_tuples,
+                ]
+            )
+        )
+
+        # modify bin tuples and create mapping
+        categories = pd.Series(
+            [mapper[i] for i in categories],
+            index=categories.index,
+            name=categories.name,
+        )
+
+        return categories, bin_tuples
+
+    def process_other_data(
+        self,
+        other_data: list[float] | pd.Series = None,
+        other_bins: list[float] | int = None,
+        name: str = None,
+    ) -> tuple[pd.Series, list[tuple[float]]]:
+        """Process aligned data for this object.
+
+        Args:
+            other_data (list[float] | pd.Series, optional):
+                The other data to process.
+                Defaults to None, which in turn uses Wind Speed.
+            other_bins (list[float] | int, optional):
+                The bins to sort this data into.
+                Defaults to None, which if other_data is None, uses Beaufort scale bins.
+            name (str, optional): A name to be given to the other data.
+                Defaults to None which uses "other", or the name of the input Series if input is a Series.
+
+        Returns:
+            tuple[pd.Series, list[tuple[float]]]:
+                - A pd.Series containing the processed categorical data.
+                - A list of tuples containing the bin edges.
+        """
+        # prepare other data
+        if other_data is None:
+            other_data = self.ws
+            if other_bins is None:
+                other_bins = BEAUFORT_CATEGORIES.bins
+
+        if len(other_data) != len(self):
+            raise ValueError(
+                f"other_data must be the same length as this {type(self)} object."
+            )
+
+        if isinstance(other_data, list | tuple | np.ndarray):
+            other_data = pd.Series(
+                other_data, index=self.index, name="other" if name is None else name
+            )
+
+        if isinstance(other_data, pd.Series):
+            if not other_data.index.equals(self.index):
+                raise ValueError(
+                    f"other_data must have the same index as this {type(self)} object."
+                )
+            if name is not None:
+                other_data.name = name
+
+        # prepare other bins
+        if other_bins is None:
+            other_bins = 11
+
+        if isinstance(other_bins, int):
+            other_bins = np.linspace(min(other_data), max(other_data), 11)
+
+        if len(other_bins) < 2:
+            raise ValueError("other_bins must contain at least 2 values.")
+
+        if min(other_data) < min(other_bins):
+            raise ValueError(
+                f"Data exists below the bounds of the given bins ({min(other_data)}<{min(other_bins)})."
+            )
+        if max(other_data) > max(other_bins):
+            raise ValueError(
+                f"Data exists above the bounds of the given bins ({max(other_data)}>{max(other_bins)})."
+            )
+
+        # bin the other data
+        categories = pd.cut(other_data, bins=other_bins, include_lowest=False)
+
+        bin_tuples = [
+            tuple([i.left, i.right]) for i in categories.cat.categories.tolist()
+        ]
+
+        mapper = dict(
+            zip(
+                *[
+                    categories.cat.categories.tolist(),
+                    bin_tuples,
+                ]
+            )
+        )
+        mapper[np.nan] = bin_tuples[0]
+
+        # modify bin tuples and create mapping
+        categories = pd.Series(
+            [mapper[i] for i in categories],
+            index=categories.index,
+            name=categories.name,
+        )
+
+        return categories, bin_tuples
+
+    def bin_data(
+        self,
+        directions: int = 36,
+        other_data: list[float] = None,
+        other_bins: list[float] = None,
+    ) -> pd.DataFrame:
+        """Create categories for wind direction and "other" data. By defualt "other" data is the wind speed associate with the wind direction in this object.
+
+        Args:
+            directions (int, optional):
+                The number of wind directions to bin values into.
+            other_data (list[float], optional):
+                An iterable of data to bin. Defaults to None.
+            other_bins (list[float], optional):
+                An iterable of bin edges to use for the other data. Defaults to None.
+
+        Returns:
+            pd.DataFrame:
+                A DataFrame containing the wind direction categories and the "other" data categories.
+        """
+
+        other_categories, _ = self.process_other_data(
+            other_data=other_data, other_bins=other_bins
+        )
+        direction_categories, _ = self.process_direction_data(directions=directions)
+
+        return pd.concat([direction_categories, other_categories], axis=1)
+
+    def histogram(
+        self,
+        directions: int = 36,
+        other_data: list[float] = None,
+        other_bins: list[float] = None,
+        density: bool = False,
+        remove_calm: bool = False,
+    ) -> pd.DataFrame:
+        """Bin data by direction, returning counts for each direction.
+
+        Args:
+            directions (int, optional):
+                The number of directions to use. Defaults to 36.
+            other_data (list[float], optional):
+                A list of other data to bin by direction. If None, then wind speed will be used.
+            other_bins (list[float]):
+                The other data bins to use for the histogram. These bins are right inclusive.
+            density (bool, optional):
+                If True, then return the probability density function. Defaults to False.
+            remove_calm (bool, optional):
+                If True, then remove calm wind speeds from the histogram. Defaults to False.
+
+        Returns:
+            pd.DataFrame:
+                A numpy array, containing the number or probability for each bin, for each direction bin.
+        """
+
+        other_categories, other_bin_tuples = self.process_other_data(
+            other_data=other_data, other_bins=other_bins
+        )
+        direction_categories, direction_bin_tuples = self.process_direction_data(
+            directions=directions
+        )
+
+        df = pd.concat([direction_categories, other_categories], axis=1)
+
+        if remove_calm:
+            df = df[self.ws > 1e-10]
+
+        # pivot data
+        df = (
+            df.groupby([df.columns[0], df.columns[1]], observed=True)
+            .value_counts()
+            .unstack()
+            .fillna(0)
+            .astype(int)
+        )
+
+        # re-add missing rows/columns to the dataframe which were not in the cut data
+        for b in other_bin_tuples:
+            if b not in df.columns:
+                df[b] = 0
+        df.sort_index(axis=1, inplace=True)
+        df = df.T
+        for b in direction_bin_tuples:
+            if b not in df.columns:
+                df[b] = 0
+        df.sort_index(axis=1, inplace=True)
+        df = df.T
+
+        if density:
+            df = df / df.values.sum()
+
+        return df
+
+    def prevailing(
+        self,
+        directions: int = 36,
+        n: int = 1,
+        as_cardinal: bool = False,
+    ) -> list[float] | list[str]:
+        """Calculate the prevailing wind direction/s for this object.
+
+        Args:
+            directions (int, optional):
+                The number of wind directions to bin values into.
+            n (int, optional):
+                The number of prevailing directions to return. Default is 1.
+            as_cardinal (bool, optional):
+                If True, then return the prevailing directions as cardinal directions. Defaults to False.
+
+        Returns:
+            list[float] | list[str]:
+                A list of wind directions.
+        """
+
+        binned = self.bin_data(directions=directions)
+        prevailing_angles = binned.iloc[:, 0].value_counts().index[:n]
+
+        if as_cardinal:
+            card = []
+            for i in prevailing_angles:
+                if i[0] < i[1]:
+                    card.append(cardinality(np.mean(i), directions=32))
+                else:
+                    card.append(cardinality(0, directions=32))
+            return card
+
+        return prevailing_angles.tolist()
+
+    def probabilities(
+        self,
+        directions: int = 36,
+        percentiles: tuple[float, float] = (0.5, 0.95),
+        other_data: list[float] = None,
+    ) -> pd.DataFrame:
+        """Calculate the probabilities of wind speeds at the given percentiles, for the direction bins specified.
+
+        Args:
+            directions (int, optional):
+                The number of wind directions to bin values into.
+            percentiles (tuple[float, float], optional):
+                A tuple of floats between 0-1 describing percentiles. Defaults to (0.5, 0.95).
+            other_data (list[float], optional):
+                A list of other data to bin by direction. If None, then wind speed will be used.
+
+        Returns:
+            pd.DataFrame:
+                A table showing probabilities at the given percentiles.
+        """
+
+        df = self.bin_data(directions=directions, other_data=other_data)
+
+        if other_data is None:
+            data_name = self.ws.name
+            s = pd.Series(self.ws.values, name=data_name, index=self.index)
+        else:
+            data_name = df.iloc[:, 1].name
+            s = pd.Series(other_data, name=data_name, index=self.index)
+
+        # group data
+        temp = s.groupby(df.iloc[:, 0], observed=True).quantile(percentiles).unstack()
+
+        # rename columns to be percentages - potentially unecessary but makes it clearer for users
+        temp.columns = [f"{i:0.1%}" for i in temp.columns]
+        temp.columns.name = s.name
+
+        return temp
+
+    def _probability_density_function(
+        self,
+        directions: int = 36,
+        other_data: list[float] = None,
+        other_bins: list[float] = None,
+    ) -> pd.DataFrame:
+        """Create a table with the probability density function for each "other_data" and direction.
+
+        Args:
+            directions (int, optional):
+                The number of directions to use. Defaults to 36.
+            other_data (list[float], optional):
+                A list of other data to bin by direction. If None, then wind speed will be used.
+            other_bins (list[float]):
+                The other data bins to use for the histogram. These bins are right inclusive.
+
+        Returns:
+            pd.DataFrame:
+                A probability density table.
+        """
+
+        hist = self.histogram(
+            directions=directions,
+            other_data=other_data,
+            other_bins=other_bins,
+            density=False,
+            remove_calm=True,
+        )
+        return pd.DataFrame(
+            hist.values.T / hist.sum(axis=1).values,
+            index=hist.columns,
+            columns=hist.index,
+        )
+
+    def pdf(
+        self,
+        directions: int = 36,
+        other_data: list[float] = None,
+        other_bins: list[float] = None,
+    ) -> pd.DataFrame:
+        """Alias for the probability_density_function method."""
+        return self._probability_density_function(
+            directions=directions,
+            other_data=other_data,
+            other_bins=other_bins,
+        )
+
+    def _cumulative_density_function(
+        self,
+        directions: int = 36,
+        other_data: list[float] = None,
+        other_bins: list[float] = None,
+    ) -> pd.DataFrame:
+        """Create a table with the cumulative probability density function for each "other_data" and direction.
+
+        Args:
+            directions (int, optional):
+                The number of directions to use. Defaults to 36.
+            other_data (list[float], optional):
+                A list of other data to bin by direction. If None, then wind speed will be used.
+            other_bins (list[float]):
+                The other data bins to use for the histogram. These bins are right inclusive.
+
+        Returns:
+            pd.DataFrame:
+                A cumulative density table.
+        """
+
+        return self._probability_density_function(
+            directions=directions, other_data=other_data, other_bins=other_bins
+        ).cumsum(axis=0)
+
+    def cdf(
+        self,
+        directions: int = 36,
+        other_data: list[float] = None,
+        other_bins: list[float] = None,
+    ) -> pd.DataFrame:
+        """Alias for the cumulative_density_function method."""
+        return self._cumulative_density_function(
+            directions=directions,
+            other_data=other_data,
+            other_bins=other_bins,
+        )
+
+    def exceedance(
+        self,
+        limit_value: float,
+        directions: int = 36,
+        other_data: list[float] = None,
+    ) -> pd.DataFrame:
+        """Calculate the % of time where a limit-value is exceeded for each direction.
+
+        Args:
+            limit_value (float):
+                The value above which speed frequency will be be calculated.
+            directions (int, optional):
+                The number of directions to use. Defaults to 36.
+            other_data (list[float], optional):
+                A list of other data to bin by direction. If None, then wind speed will be used.
+
+        Returns:
+            pd.DataFrame:
+                A table containing exceedance values.
+        """
+
+        other_categories, other_bin_tuples = self.process_other_data(
+            other_data=other_data
+        )
+        direction_categories, direction_bin_tuples = self.process_direction_data(
+            directions=directions
+        )
+
+        if other_data is None:
+            other_data = self.ws
+
+        exceedance = []
+        for month in range(1, 13, 1):
+            mask = self.index.month == month
+            meets = other_data[mask] > limit_value
+            temp = pd.concat([direction_categories[mask], meets], axis=1)
+            exceedance.append(
+                (temp.iloc[:, 1].groupby([temp.iloc[:, 0]]).sum() / mask.sum()).rename(
+                    calendar.month_abbr[month]
+                )
+            )
+
+        df = pd.concat(exceedance, axis=1)
+        df.columns.name = f"{other_categories.name} > {limit_value}"
+
+        # re-add missing rows if they were not in the cut data
+        for db in direction_bin_tuples:
+            if db not in df.index:
+                df.loc[db] = 0
+        df.sort_index(axis=0, inplace=True)
+
+        return df.fillna(0)
+
+    def wind_matrix(self) -> pd.DataFrame:
+        """Calculate average wind speed and direction for each month and hour of day in a pandas DataFrame.
+        Returns:
+            pd.DataFrame:
+                A DataFrame containing average wind speed and direction for each month and hour of day.
+        """
+
+        wind_directions = (
+            (
+                (
+                    self.wd.groupby(
+                        [self.wd.index.month, self.wd.index.hour], axis=0
+                    ).apply(circular_weighted_mean)
+                )
+                % 360
+            )
+            .unstack()
+            .T
+        )
+        wind_directions.columns = [
+            calendar.month_abbr[i] for i in wind_directions.columns
+        ]
+        wind_speeds = (
+            self.ws.groupby([self.ws.index.month, self.ws.index.hour], axis=0)
+            .mean()
+            .unstack()
+            .T
+        )
+        wind_speeds.columns = [calendar.month_abbr[i] for i in wind_speeds.columns]
+
+        df = pd.concat(
+            [wind_directions, wind_speeds], axis=1, keys=["direction", "speed"]
+        )
+        df.index.name = "hour"
+
+        return df
+
+    def summarise(self, directions: int = 36) -> list[str]:
+        """Generate a textual summary of the current object.
+
+        Args:
+            directions (int, optional):
+                The number of directions to use. Defaults to 36.
+
+        Returns:
+            list[str]:
+                A list of strings describing the current object.
+        """
+
+        binned = self.bin_data(directions=directions)
+        prevailing_angles = binned.iloc[:, 0].value_counts()
+
+        start_time = self.datetimes[0]
+        end_time = self.datetimes[-1]
+
+        prevailing_angle = prevailing_angles.index[0]
+        prevailing_count = prevailing_angles.iloc[0]
+        prevailing_cardinal = (
+            cardinality(np.mean(prevailing_angle), directions=32)
+            if prevailing_angle[0] < prevailing_angle[1]
+            else cardinality(0, directions=32)
+        )
+        prevailing_mean = self.ws.values[binned.iloc[:, 0] == prevailing_angle].mean()
+        prevailing_max = self.ws.values[binned.iloc[:, 0] == prevailing_angle].max()
+
+        # pylint: disable=line-too-long
+        return_strings = []
+        from_epw = self.source.endswith(".epw")
+        if from_epw:
+            return_strings.append(
+                f'This summary describes wind speed and direction relationship at {self.height_above_ground}m above ground for the file "{self.source}".'
+            )
+        else:
+            return_strings.append(
+                f"This summary describes historic wind speed and direction relationship for the period {start_time} to {end_time} at {self.height_above_ground}m above ground, from {self.source}."
+            )
+
+        return_strings.append(
+            f"The prevailing wind direction is between {prevailing_angle[0]}° and {prevailing_angle[1]}° (or {prevailing_cardinal}), accounting for {prevailing_count} of {len(self.wd)} timesteps."
+        )
+        return_strings.append(
+            f"Prevailing wind average speed is {prevailing_mean:.2f}m/s, with a maximum of {prevailing_max:.2f}m/s from that direction."
+        )
+
+        if from_epw:
+            return_strings.append(
+                f"Peak wind speeds were observed during {self.ws.idxmax():%B} at {self.ws.idxmax():%H:%M}, reaching {self.ws.max():.2f}m/s from {self.wd.loc[self.ws.idxmax()]}°.",
+            )
+        else:
+            return_strings.append(
+                f"Peak wind speeds were observed at {self.ws.idxmax()}, reaching {self.ws.max():.2f}m/s from {self.wd.loc[self.ws.idxmax()]}°.",
+            )
+
+        return_strings.append(
+            f"{self.calm():.2%} of the time, wind speeds are calm (≤ 1e-10m/s)."
+        )
+        return return_strings
+        # pylint: enable=line-too-long
+
+    def weibull_pdf(self) -> tuple[float]:
+        """Calculate the parameters of an exponentiated Weibull continuous random variable.
+
+        Returns:
+            k (float):
+                Shape parameter
+            loc (float):
+                Location parameter.
+            c (float):
+                Scale parameter.
+        """
+
+        ws = self.ws.values
+        ws = ws[ws != 0]
+        ws = ws[~np.isnan(ws)]
+
+        try:
+            return weibull_min.fit(ws.tolist())
+        except ValueError as exc:
+            warnings.warn(f"Not enough data to calculate Weibull parameters.\n{exc}")
+            return (np.nan, np.nan, np.nan)  # type: ignore
+
+    def weibull_directional(self, directions: int = 36) -> pd.DataFrame:
+        """Calculate directional weibull coefficients for the given number of directions.
+
+        Args:
+            directions (int, optional):
+                The number of directions to use. Defaults to 36.
+
+        Returns:
+            pd.DataFrame:
+                A DataFrame object.
+        """
+
+        hist = self.histogram(directions=directions)
+        d = {}
+        for left, right in hist.index:
+            d[(left, right)] = self.filter_by_direction(
+                left_angle=left, right_angle=right
+            ).weibull_pdf()
+        df = pd.DataFrame(d, index=["k", "loc", "c"])
+        df.columns = df.columns.tolist()
+        return df.T
+
+    ##################################
+    # PLOTTING/VISUALISATION METHODS #
+    ##################################
+
+    def plot_timeseries(self, ax: plt.Axes = None, **kwargs) -> plt.Axes:  # type: ignore
+        """Create a simple line plot of wind speed.
+
+        Args:
+            ax (plt.Axes, optional):
+                The axes to plot on. If None, the current axes will be used.
+            **kwargs:
+                Additional keyword arguments to pass to the function. These include:
+                title (str, optional):
+                    A title for the plot. Defaults to None.
+
+        Returns:
+            plt.Axes:
+                A matplotlib Axes object.
+
+        """
+
+        if ax is None:
+            ax = plt.gca()
+
+        ax.set_title(textwrap.fill(f"{self.source}", 75))
+
+        timeseries(self.ws, ax=ax, **kwargs)
+
+        ax.set_ylabel(self.ws.name)
+
+        return ax
+
+    def plot_windmatrix(
+        self,
+        ax: plt.Axes = None,
+        show_values: bool = False,
+        **kwargs,
+    ) -> plt.Axes:
+        """Create a plot showing the annual wind speed and direction bins
+        using the month_time_average method.
+
+        Args:
+            ax (plt.Axes, optional):
+                The axes to plot on. If None, the current axes will be used.
+            show_values (bool, optional):
+                Whether to show values in the cells. Defaults to False.
+            **kwargs:
+                Additional keyword arguments to pass to the pcolor function.
+
+        Returns:
+            plt.Axes:
+                A matplotlib Axes object.
+
+        """
+
+        if ax is None:
+            ax = plt.gca()
+
+        ax.set_title(textwrap.fill(f"{self.source}", 75))
+
+        df = self.wind_matrix()
+        _wind_speeds = df["speed"]
+        _wind_directions = df["direction"]
+
+        if any(
+            [
+                _wind_speeds.shape != (24, 12),
+                _wind_directions.shape != (24, 12),
+                _wind_directions.shape != _wind_speeds.shape,
+                not np.array_equal(_wind_directions.index, _wind_speeds.index),
+                not np.array_equal(_wind_directions.columns, _wind_speeds.columns),
+            ]
+        ):
+            raise ValueError(
+                "The wind_speeds and wind_directions must cover all months of the "
+                "year, and all hours of the day, and align with each other."
+            )
+
+        cmap = kwargs.pop("cmap", "YlGnBu")
+        vmin = kwargs.pop("vmin", _wind_speeds.values.min())
+        vmax = kwargs.pop("vmax", _wind_speeds.values.max())
+        cbar_title = kwargs.pop("cbar_title", "m/s")
+        norm = kwargs.pop("norm", Normalize(vmin=vmin, vmax=vmax, clip=True))
+        mapper = kwargs.pop("mapper", ScalarMappable(norm=norm, cmap=cmap))
+
+        pc = ax.pcolor(_wind_speeds, cmap=cmap, vmin=vmin, vmax=vmax, **kwargs)
+        _x = -np.sin(np.deg2rad(_wind_directions.values))
+        _y = -np.cos(np.deg2rad(_wind_directions.values))
+        direction_matrix = angle_from_north([_x, _y])
+        ax.quiver(
+            np.arange(1, 13, 1) - 0.5,
+            np.arange(0, 24, 1) + 0.5,
+            _x * _wind_speeds.values / 2,
+            _y * _wind_speeds.values / 2,
+            pivot="mid",
+            fc="white",
+            ec="black",
+            lw=0.5,
+            alpha=0.5,
+        )
+
+        if show_values:
+            for _xx, col in enumerate(_wind_directions.values.T):
+                for _yy, _ in enumerate(col.T):
+                    local_value = _wind_speeds.values[_yy, _xx]
+                    cell_color = mapper.to_rgba(local_value)
+                    text_color = contrasting_color(cell_color)
+                    # direction text
+                    ax.text(
+                        _xx,
+                        _yy,
+                        f"{direction_matrix[_yy][_xx]:0.0f}°",
+                        color=text_color,
+                        ha="left",
+                        va="bottom",
+                        fontsize="xx-small",
+                    )
+                    # speed text
+                    ax.text(
+                        _xx + 1,
+                        _yy + 1,
+                        f"{_wind_speeds.values[_yy][_xx]:0.1f}m/s",
+                        color=text_color,
+                        ha="right",
+                        va="top",
+                        fontsize="xx-small",
+                    )
+        ax.set_xticks(np.arange(1, 13, 1) - 0.5)
+        ax.set_xticklabels([calendar.month_abbr[i] for i in np.arange(1, 13, 1)])
+        ax.set_yticks(np.arange(0, 24, 1) + 0.5)
+        ax.set_yticklabels([f"{i:02d}:00" for i in np.arange(0, 24, 1)])
+        for label in ax.yaxis.get_ticklabels()[1::2]:
+            label.set_visible(False)
+
+        cb = plt.colorbar(pc, label=cbar_title, pad=0.01)
+        cb.outline.set_visible(False)
+
+        return ax
+
+    def plot_densityfunction(
+        self,
+        ax: plt.Axes = None,
+        speed_bins: list[float] | int = 11,
+        percentiles: tuple[float] = (0.5, 0.95),
+        function: str = "pdf",
+        ylim: tuple[float] = None,
+    ) -> plt.Axes:
+        """Create a histogram showing wind speed frequency.
+
+        Args:
+            ax (plt.Axes, optional):
+                The axes to plot this chart on. Defaults to None.
+            speed_bins (list[float], optional):
+                The wind speed bins to use for the histogram. These bins are right inclusive.
+            percentiles (tuple[float], optional):
+                The percentiles to plot. Defaults to (0.5, 0.95).
+            function (str, optional):
+                The function to use. Either "pdf" or "cdf". Defaults to "pdf".
+            ylim (tuple[float], optional):
+                The y-axis limits. Defaults to None.
+
+        Returns:
+            plt.Axes: The axes object.
+        """
+
+        if function not in ["pdf", "cdf"]:
+            raise ValueError('function must be either "pdf" or "cdf".')
+
+        if ax is None:
+            ax = plt.gca()
+
+        ax.set_title(
+            f"{str(self)}\n{'Probability Density Function' if function == 'pdf' else 'Cumulative Density Function'}"
+        )
+
+        self.ws.plot.hist(
+            ax=ax,
+            density=True,
+            bins=speed_bins,
+            cumulative=True if function == "cdf" else False,
+        )
+
+        for percentile in percentiles:
+            x = np.quantile(self.ws, percentile)
+            ax.axvline(x, 0, 1, ls="--", lw=1, c="black", alpha=0.5)
+            ax.text(
+                x + 0.05,
+                0,
+                f"{percentile:0.0%}\n{x:0.2f}m/s",
+                ha="left",
+                va="bottom",
+            )
+
+        ax.set_xlim(0, ax.get_xlim()[-1])
+        if ylim:
+            ax.set_ylim(ylim)
+
+        ax.set_xlabel("Wind Speed (m/s)")
+        ax.set_ylabel("Frequency")
+
+        for spine in ["top", "right"]:
+            ax.spines[spine].set_visible(False)
+        ax.grid(visible=True, which="major", axis="both", ls="--", lw=1, alpha=0.25)
+
+        ax.yaxis.set_major_formatter(mticker.PercentFormatter(1, decimals=1))
+
+        return ax
+
+    def plot_windrose(
+        self,
+        ax: plt.Axes = None,
+        directions: int = 36,
+        other_data: list[float] = None,
+        other_bins: list[float] = None,
+        colors: list[str | tuple[float] | Colormap] = None,
+        legend: bool = True,
+        ylim: tuple[float] = None,
+        label: bool = False,
+    ) -> plt.Axes:
+        """Create a wind rose showing wind speed and direction frequency.
+
+        Args:
+            ax (plt.Axes, optional):
+                The axes to plot this chart on. Defaults to None.
+            directions (int, optional):
+                The number of directions to use. Defaults to 36.
+            other_data (list[float], optional):
+                A list of other data to bin by direction. If None, then wind speed will be used.
+            other_bins (list[float]):
+                The other data bins to use for the histogram. These bins are right inclusive. If other data is None,
+                then the default Beaufort bins will be used, otherwise 11 evenly spaced bins will be used.
+            colors: (str | tuple[float] | Colormap, optional):
+                A list of colors to use for the other bins. May also be a colormap. Defaults to the colors used for
+                Beaufort wind comfort categories.
+            legend (bool, optional):
+                Set to False to remove the legend. Defaults to True.
+            ylim (tuple[float], optional):
+                The y-axis limits. Defaults to None.
+            label (bool, optional):
+                Set to False to remove the bin labels. Defaults to False.
+
+        Returns:
+            plt.Axes: The axes object.
+        """
+
+        if ax is None:
+            _, ax = plt.subplots(subplot_kw={"projection": "polar"})
+
+        # create grouped data for plotting
+        binned = self.histogram(
+            directions=directions,
+            other_data=other_data,
+            other_bins=other_bins,
+            density=True,
+            remove_calm=True,
+        )
+
+        # set colors
+        if colors is None:
+            if other_data is None:
+                colors = [
+                    to_hex(BEAUFORT_CATEGORIES.cmap(i))
+                    for i in np.linspace(0, 1, len(binned.columns))
+                ]
+            else:
+                colors = [
+                    to_hex(plt.get_cmap("viridis")(i))
+                    for i in np.linspace(0, 1, len(binned.columns))
+                ]
+        if isinstance(colors, str):
+            colors = plt.get_cmap(colors)
+        if isinstance(colors, Colormap):
+            colors = [to_hex(colors(i)) for i in np.linspace(0, 1, len(binned.columns))]
+        if isinstance(colors, list | tuple):
+            if len(colors) != len(binned.columns):
+                raise ValueError(
+                    f"colors must be a list of length {len(binned.columns)}, or a colormap."
+                )
+
+        # HACK start - a fix introduced here to ensure that bar ends are curved when using a polar plot.
+        fig = plt.figure()
+        rect = [0.1, 0.1, 0.8, 0.8]
+        hist_ax = plt.Axes(fig, rect)
+        hist_ax.bar(np.array([1]), np.array([1]))
+        # HACK end
+
+        ax.set_title(textwrap.fill(f"{self.source}", 75))
+
+        theta_width = np.deg2rad(360 / directions)
+        patches = []
+        color_list = []
+        x = theta_width / 2
+        for _, data_values in binned.iterrows():
+            y = 0
+            for n, val in enumerate(data_values.values):
+                patches.append(
+                    Rectangle(
+                        xy=(x, y),
+                        width=theta_width,
+                        height=val,
+                        alpha=1,
+                    )
+                )
+                color_list.append(colors[n])
+                y += val
+            if label:
+                ax.text(x, y, f"{y:0.1%}", ha="center", va="center", fontsize="x-small")
+            x += theta_width
+        local_cmap = ListedColormap(np.array(color_list).flatten())
+        pc = PatchCollection(patches, cmap=local_cmap)
+        pc.set_array(np.arange(len(color_list)))
+        ax.add_collection(pc)
+
+        # construct legend
+        if legend:
+            handles = [
+                mpatches.Patch(color=colors[n], label=f"{i} to {j}")
+                for n, (i, j) in enumerate(binned.columns.values)
+            ]
+            _ = ax.legend(
+                handles=handles,
+                bbox_to_anchor=(1.1, 0.5),
+                loc="center left",
+                ncol=1,
+                borderaxespad=0,
+                frameon=False,
+                fontsize="small",
+                title=binned.columns.name,
+                title_fontsize="small",
+            )
+
+        # set y-axis limits
+        if ylim is None:
+            ylim = (0, max(binned.sum(axis=1)))
+        if len(ylim) != 2:
+            raise ValueError("ylim must be a tuple of length 2.")
+        ax.set_ylim(ylim)
+        ax.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1))
+
+        format_polar_plot(ax, yticklabels=True)
+
+        return ax
+
+    def plot_windhistogram(
+        self,
+        ax: plt.Axes = None,
+        directions: int = 36,
+        other_data: list[float] = None,
+        other_bins: list[float] = None,
+        density: bool = False,
+        cmap: str | Colormap = "YlGnBu",
+        show_values: bool = True,
+        vmin: float = None,
+        vmax: float = None,
+    ) -> plt.Axes:
+        """Plot a 2D-histogram for a collection of wind speeds and directions.
+
+        Args:
+            ax (plt.Axes, optional):
+                The axis to plot results on. Defaults to None.
+            directions (int, optional):
+                The number of directions to use. Defaults to 36.
+            other_data (list[float], optional):
+                A list of other data to bin by direction. If None, then wind speed will be used.
+            other_bins (list[float]):
+                The other data bins to use for the histogram. These bins are right inclusive.
+            density (bool, optional):
+                If True, then return the probability density function. Defaults to False.
+            cmap (str | Colormap, optional):
+                The colormap to use. Defaults to "YlGnBu".
+            show_values (bool, optional):
+                Whether to show values in the cells. Defaults to True.
+            vmin (float, optional):
+                The minimum value for the colormap. Defaults to None.
+            vmax (float, optional):
+                The maximum value for the colormap. Defaults to None.
+
+        Returns:
+            plt.Axes:
+                A matplotlib Axes object.
+        """
+
+        if ax is None:
+            ax = plt.gca()
+
+        hist = self.histogram(
+            directions=directions,
+            other_data=other_data,
+            other_bins=other_bins,
+            density=density,
+        )
+
+        vmin = hist.values.min() if vmin is None else vmin
+        vmax = hist.values.max() if vmax is None else vmax
+        cmap = plt.get_cmap(cmap)
+        norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
+        mapper = ScalarMappable(norm=norm, cmap=cmap)
+
+        _xticks = np.roll(hist.index, 1)
+        _values = np.roll(hist.values, 1, axis=0).T
+
+        pc = ax.pcolor(_values, cmap=cmap, vmin=vmin, vmax=vmax)
+        ax.set_xticks(np.arange(0.5, len(hist.index), 1), labels=_xticks, rotation=90)
+        ax.set_xlabel(hist.index.name)
+        ax.set_yticks(np.arange(0.5, len(hist.columns), 1), labels=hist.columns)
+        ax.set_ylabel(hist.columns.name)
+
+        cb = plt.colorbar(pc, pad=0.01, label="Density" if density else "Count")
+        if density:
+            cb.ax.yaxis.set_major_formatter(mticker.PercentFormatter(1, decimals=1))
+        cb.outline.set_visible(False)
+
+        ax.set_title(textwrap.fill(f"{self.source}", 75))
+
+        if show_values:
+            for _xx, row in enumerate(_values):
+                for _yy, col in enumerate(row):
+                    if (col * 100).round(1) == 0:
+                        continue
+                    cell_color = mapper.to_rgba(col)
+                    text_color = contrasting_color(cell_color)
+                    ax.text(
+                        _yy + 0.5,
+                        _xx + 0.5,
+                        f"{col:0.2%}" if density else col,
+                        color=text_color,
+                        ha="center",
+                        va="center",
+                        fontsize="xx-small",
+                    )
+
+        return ax
+
+    # TODO - add WindProfile plot here
+
+    # TODO - add Climate Consultant-style "wind wheel" plot here
+    # (http://2.bp.blogspot.com/-F27rpZL4VSs/VngYxXsYaTI/AAAAAAAACAc/yoGXmk13uf8/s1600/CC-graphics%2B-%2BWind%2BWheel.jpg)
+
+    # TODO - add radial version of plot_windhistogram
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind/__init_.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind/__init_.py
deleted file mode 100644
index d15a5303..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind/__init_.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .direction_bins import DirectionBins
-from .wind import Wind
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind/direction_bins.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind/direction_bins.py
deleted file mode 100644
index 2b13ff01..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind/direction_bins.py
+++ /dev/null
@@ -1,195 +0,0 @@
-from __future__ import annotations
-
-from typing import Dict, List, Tuple
-
-import numpy as np
-import pandas as pd
-
-from ..helpers import angle_from_cardinal, cardinality, rolling_window
-
-
-class DirectionBins:
-    """An object containing directional binning data, used mainly for Wind data collections. These bins assume North is at 0-degrees.
-
-    Args:
-        directions (int, optional):
-            The number of direction bins that should be created. Defaults to 8.
-        centered (bool, optional):
-            Whether the data should be centered about North - True, or starting from North - False. Defaults to True.
-
-    Returns:
-        DirectionBins:
-            The resulting object.
-    """
-
-    def __init__(
-        self,
-        directions: int = 8,
-        centered: bool = True,
-    ) -> DirectionBins:
-        self.directions = directions
-        self.centered = centered
-        self.bins = self.direction_bin_edges(self.directions, self.centered)
-
-    def __len__(self) -> int:
-        return self.directions
-
-    @property
-    def lows(self) -> List[float]:
-        """The "left-most" edge of the direction bins."""
-        return self.bins.T[0]
-
-    @property
-    def highs(self) -> List[float]:
-        """The "right-most" edge of the direction bins."""
-        return self.bins.T[1]
-
-    @property
-    def midpoints(self) -> List[float]:
-        """The mipoints within the direction bins."""
-        if self.is_split:
-            return np.concatenate([[0], np.mean(self.bins, axis=1)[1:-1]])
-        return np.mean(self.bins, axis=1)
-
-    @property
-    def bin_width(self) -> float:
-        """The width of each direction bin."""
-        return 360 / self.directions
-
-    @property
-    def is_split(self) -> bool:
-        """True if the "north" bin is split into two parts - before and after north."""
-        return len(self.bins) != self.directions
-
-    @property
-    def cardinal_directions(self) -> List[str]:
-        """The direction bins as cardinal directions."""
-        return [cardinality(i, directions=32) for i in self.midpoints]
-
-    @staticmethod
-    def direction_bin_edges(directions: int, centered: bool) -> List[List[float]]:
-        """Create a list of start/end points for wind directions, each bin increasing from the previous one.
-            This method assumes that North is at 0 degrees.
-            If the direction bins cross North then the bins returned are all increasing, with the one crossing north
-            split into two (and placed at either end of the list of bins).
-
-        Args:
-            directions (int, optional):
-                The number of directions to bin wind data into.
-            centered (bool, optional):
-                Whether the data should be centered about North - True, or starting from North - False. Defaults to True.
-
-        Returns:
-            List[List[float]]:
-                A set of bin edges.
-        """
-
-        if directions < 2:
-            raise ValueError("n_directions must be ≥ 2.")
-
-        # get angle for each bin
-        bin_angle = 360 / directions
-
-        # get all bin edge angles for 0-360deg
-        bin_edges = np.arange(
-            0 if not centered else 0 - (bin_angle / 2), 360, bin_angle
-        )
-
-        # replace -Ve values with the 360-X version
-        bin_edges = np.where(bin_edges < 0, 360 + bin_edges, bin_edges)
-
-        # add north to sequence if required
-        if not centered:
-            bin_edges = np.concatenate([bin_edges, [360]])
-
-        # create pairs of edges for each bin
-        bin_edges = rolling_window(bin_edges, 2)
-
-        # if the start bin crosses "north", then split it into two - either end of the array
-        if bin_edges[0][0] > bin_edges[0][-1]:
-            bin_edges = np.array(
-                [[0, bin_edges[0][1]]]
-                + bin_edges[1:].tolist()
-                + [[bin_edges[-1][1], 360]]
-            )
-
-        return bin_edges
-
-    @property
-    def interval_index(self) -> pd.IntervalIndex:
-        """The direction bins as a pandasa IntervalIdex."""
-        return pd.IntervalIndex.from_arrays(self.lows, self.highs, closed="left")
-
-    def bin_data(
-        self, direction_data: List[float], other_data: List[float] = None
-    ) -> Dict[Tuple[float], List[float]]:
-        """Bin a set of input data, including combination of split bins if present.
-
-        Args:
-            direction_data (List[float]):
-                A list of wind directions.
-            other_data (List[float], optional):
-                A list of other data to bin by direction. If None, then direction_data will be used.
-
-        Returns:
-            Dict[Tuple[float], List[float]]:
-                A dictionary indexed by direction bin edges, and containing values.
-        """
-
-        if other_data is None:
-            other_data = direction_data
-
-        direction_data = np.array(direction_data)  # type: ignore
-        other_data = np.array(other_data)  # type: ignore
-
-        if len(direction_data) != len(other_data):
-            raise ValueError(
-                f"direction_data and other_data must be the same length! ({len(direction_data)} != {len(other_data)})"
-            )
-
-        # create bin labels
-        bin_labels = list(zip(*[self.lows, self.highs]))  # type: ignore
-
-        # bin data
-        temp = pd.DataFrame([direction_data, other_data]).T
-        d = {}
-        for low, high in bin_labels:
-            d[(low, high)] = temp[1][(temp[0] > low) & (temp[0] <= high)].tolist()
-
-        # combine split bins if present
-        if self.is_split:
-            combined_bin = d[bin_labels[0]] + d[bin_labels[-1]]
-            d = {
-                **{(bin_labels[-1][0], bin_labels[0][1]): combined_bin},
-                **{k: v for k, v in list(d.items())[1:-1]},
-            }
-        return d
-
-    def prevailing(
-        self, direction_data: List[float], n: int, as_angle: bool = False
-    ) -> List[str]:
-        """Given a list of wind directions, return the n-prevailing directions.
-
-        Args:
-            direction_data (List[float]):
-                A set of wind directions.
-            n (int):
-                The number of prevailing directions to return.
-            as_angle (bool, optional):
-                Return the previaling directions as angles. Defaults to False.
-
-        Returns:
-            List[str]: _description_
-        """
-        d = self.bin_data(direction_data)
-        dd = {}
-        for k, v in d.items():
-            dd[k] = len(v)
-        s = pd.DataFrame.from_dict(dd, orient="index").squeeze()
-        s.index = self.cardinal_directions
-        s = s.sort_values(ascending=False)
-
-        cardinal_directions = s.index[:n]
-        if as_angle:
-            return [angle_from_cardinal(i) for i in cardinal_directions]
-        return cardinal_directions
diff --git a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind/wind.py b/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind/wind.py
deleted file mode 100644
index 1355b928..00000000
--- a/LadybugTools_Engine/Python/src/ladybugtools_toolkit/wind/wind.py
+++ /dev/null
@@ -1,1271 +0,0 @@
-from __future__ import annotations
-
-import calendar
-import warnings
-from dataclasses import dataclass, field
-from datetime import datetime
-from pathlib import Path
-from typing import Any, List, Tuple, Union
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from ladybug.dt import DateTime
-from ladybug.epw import EPW
-from matplotlib.colors import Colormap
-from tqdm import tqdm
-
-from ..bhomutil.bhom_object import BHoMObject
-from ..categorical.categories import BEAUFORT_CATEGORIES
-from ..helpers import (
-    OpenMeteoVariable,
-    circular_weighted_mean,
-    rolling_window,
-    scrape_openmeteo,
-    weibull_directional,
-    weibull_pdf,
-    wind_direction_average,
-    wind_speed_at_height,
-)
-from ..ladybug_extension.analysis_period import (
-    AnalysisPeriod,
-    analysis_period_to_boolean,
-    analysis_period_to_datetimes,
-)
-from ..plot import timeseries
-from ..plot._wind import (
-    radial_histogram,
-    wind_cumulative_probability,
-    wind_matrix,
-    wind_speed_frequency,
-    windrose,
-)
-from .direction_bins import DirectionBins
-
-
-@dataclass(init=True, repr=True, eq=True)
-class Wind(BHoMObject):
-    """An object containing historic, time-indexed wind data.
-
-    Args:
-        wind_speeds (List[Union[int, float, np.number]]):
-            An iterable of wind speeds in m/s.
-        wind_directions (List[Union[int, float, np.number]]):
-            An iterable of wind directions in degrees from North (with North at 0-degrees).
-        datetimes (Union[pd.DatetimeIndex, List[Union[datetime, np.datetime64, pd.Timestamp]]]):
-            An iterable of datetime-like objects.
-        height_above_ground (float, optional):
-            The height above ground (in m) where the input wind speeds and directions were collected. Defaults to 10m.
-    """
-
-    wind_speeds: List[Union[int, float, np.number]] = field(
-        init=True, compare=True, repr=False
-    )
-    wind_directions: List[Union[int, float, np.number]] = field(
-        init=True, compare=True, repr=False
-    )
-    datetimes: List[datetime] = field(init=True, compare=True, repr=False)
-    height_above_ground: float = field(init=True, compare=True, repr=False, default=10)
-
-    def __post_init__(self):
-        self.validation(
-            self.wind_speeds,
-            self.wind_directions,
-            self.datetimes,
-            self.height_above_ground,
-        )
-
-        self.datetimes: List[datetime] = pd.to_datetime(self.datetimes)
-        self.wind_speeds = pd.Series(
-            self.wind_speeds, index=self.datetimes, name="speed"
-        ).sort_index()
-        self.wind_directions = pd.Series(
-            self.wind_directions, index=self.datetimes, name="direction"
-        ).sort_index()
-        self.df = pd.concat([self.ws, self.wd], axis=1)
-
-        # wrap methods within this class
-        super().__post_init__()
-
-    ##################
-    # DUNDER METHODS #
-    ##################
-
-    def __len__(self) -> int:
-        return len(self.df)
-
-    def __repr__(self) -> str:
-        """The printable representation of the given object"""
-        return f"{self.__class__.__name__}({min(self.df.index):%Y-%m-%d} to {max(self.df.index):%Y-%m-%d}, n={len(self)} @{self.freq}, @{self.height_above_ground}m)"
-
-    ##############
-    # PROPERTIES #
-    ##############
-
-    @property
-    def freq(self) -> str:
-        """Return the inferred frequency of the datetimes associated with this object."""
-        freq = pd.infer_freq(self.df.index)
-        if freq is None:
-            return "inconsistent"
-        return freq
-
-    @property
-    def ws(self) -> pd.Series:
-        """Convenience accessor for wind speeds as a time-indexed pd.Series object."""
-        return self.wind_speeds
-
-    @property
-    def wd(self) -> pd.Series:
-        """Convenience accessor for wind directions as a time-indexed pd.Series object."""
-        return self.wind_directions
-
-    @property
-    def calm_datetimes(self) -> List[datetime]:
-        """Return the datetimes where wind speed is < 0.1.
-
-        Returns:
-            List[datetime]:
-                "Calm" wind datetimes.
-        """
-        return self.wind_speeds[self.wind_speeds <= 0.1].index.tolist()
-
-    #################
-    # CLASS METHODS #
-    #################
-
-    @classmethod
-    def from_dataframe(
-        cls,
-        df: pd.DataFrame,
-        wind_speed_column: Any,
-        wind_direction_column: Any,
-        height_above_ground: float = 10,
-    ) -> Wind:
-        """Create a Wind object from a Pandas DataFrame, with WindSpeed and WindDirection columns.
-
-        Args:
-            df (pd.DataFrame):
-                A DataFrame object containing speed and direction columns, and a datetime index.
-            wind_speed_column (str):
-                The name of the column where wind-speed data exists.
-            wind_direction_column (str):
-                The name of the column where wind-direction data exists.
-            height_above_ground (float, optional):
-                Defaults to 10m.
-        """
-
-        if not isinstance(df, pd.DataFrame):
-            raise ValueError(f"df must be of type {type(pd.DataFrame)}")
-
-        if not isinstance(df.index, pd.DatetimeIndex):
-            raise ValueError(
-                f"The DataFrame's index must be of type {type(pd.DatetimeIndex)}"
-            )
-
-        # remove NaN values
-        df.dropna(axis=0, how="any", inplace=True)
-
-        # remove duplicates in input dataframe
-        df = df.loc[~df.index.duplicated()]
-
-        return cls(
-            df[wind_speed_column].tolist(),
-            df[wind_direction_column].tolist(),
-            df.index.tolist(),
-            height_above_ground,
-        )
-
-    @classmethod
-    def from_csv(
-        cls,
-        csv_path: Union[str, Path],
-        wind_speed_column: str,
-        wind_direction_column: str,
-        height_above_ground: float = 10,
-    ) -> Wind:
-        """Create a Wind object from a csv containing wind speed and direction columns.
-
-        Args:
-            csv_path (Union[str, Path]):
-                The path to the CSV file containing speed and direction columns, and a datetime index.
-            wind_speed_column (str):
-                The name of the column where wind-speed data exists.
-            wind_direction_column (str):
-                The name of the column where wind-direction data exists.
-            height_above_ground (float, optional):
-                Defaults to 10m.
-        """
-        df = pd.read_csv(csv_path, index_col=0, header=0, parse_dates=True)
-        return Wind.from_dataframe(
-            df,
-            wind_speed_column=wind_speed_column,
-            wind_direction_column=wind_direction_column,
-            height_above_ground=height_above_ground,
-        )
-
-    @classmethod
-    def from_epw(cls, epw: Union[str, Path, EPW]) -> Wind:
-        """Create a Wind object from an EPW file or object.
-
-        Args:
-            epw (Union[str, Path, EPW]):
-                The path to the EPW file, or an EPW object.
-        """
-
-        if isinstance(epw, (str, Path)):
-            epw = EPW(epw)
-
-        return Wind(
-            wind_speeds=epw.wind_speed.values,
-            wind_directions=epw.wind_direction.values,
-            datetimes=analysis_period_to_datetimes(AnalysisPeriod()),
-            height_above_ground=10,
-        )
-
-    @classmethod
-    def from_openmeteo(
-        cls,
-        latitude: float,
-        longitude: float,
-        start_date: Union[datetime, str],
-        end_date: Union[datetime, str],
-    ) -> Wind:
-        """Create a Wind object from data obtained from the Open-Meteo database of historic weather station data.
-
-        Args:
-            latitude (float):
-                The latitude of the target site, in degrees.
-            longitude (float):
-                The longitude of the target site, in degrees.
-            start_date (Union[datetime, str]):
-                The start-date from which records will be obtained.
-            end_date (Union[datetime, str]):
-                The end-date beyond which records will be ignored.
-        """
-        if isinstance(start_date, str):
-            start_date = datetime.strptime(start_date, "%Y-%m-%d")
-        if isinstance(end_date, str):
-            end_date = datetime.strptime(end_date, "%Y-%m-%d")
-        df = scrape_openmeteo(
-            latitude,
-            longitude,
-            start_date,
-            end_date,
-            [OpenMeteoVariable.WINDSPEED_10M, OpenMeteoVariable.WINDDIRECTION_10M],
-        )
-        df.dropna(how="any", axis=0, inplace=True)
-        wind_speeds = np.multiply(
-            df["windspeed_10m (km/h)"].astype(float), 0.277778
-        ).tolist()
-        wind_directions = df["winddirection_10m (°)"].astype(float).tolist()
-        if len(wind_speeds) == 0 or len(wind_directions) == 0:
-            raise ValueError(
-                "OpenMeteo did not return any data for the given latitude, longitude and start/end dates."
-            )
-        datetimes = df.index.tolist()
-        return Wind(wind_speeds, wind_directions, datetimes, 10)
-
-    @classmethod
-    def from_average(
-        cls, wind_objects: List[Wind], weights: List[float] = None
-    ) -> Wind:
-        """Create an average Wind object from a set of input Wind objects, with optional weighting for each."""
-
-        # create default weightings if None
-        if weights is None:
-            weights = [1 / len(wind_objects)] * len(wind_objects)
-        else:
-            if sum(weights) != 1:
-                raise ValueError("weights must total 1.")
-
-        # align collections so that intersection only is created
-        df_ws = pd.concat([i.ws for i in wind_objects], axis=1).dropna()
-        df_wd = pd.concat([i.wd for i in wind_objects], axis=1).dropna()
-
-        # construct the weighted means
-        wd_avg = [circular_weighted_mean(i, weights) for r, i in df_wd.iterrows()]
-        ws_avg = np.average(df_ws, axis=1, weights=weights)
-        dts = df_ws.index
-
-        # return the new averaged object
-        return Wind(
-            ws_avg,
-            wd_avg,
-            dts,
-            np.average([i.height_above_ground for i in wind_objects], weights=weights),
-        )
-
-    ##################
-    # STATIC METHODS #
-    ##################
-
-    @staticmethod
-    def validation(
-        wind_speeds: Any, wind_directions: Any, datetimes: Any, height_above_ground: Any
-    ) -> None:
-        """Ensure that values given conform to expected standards in order to create a valid Wind object.
-
-        Args:
-            wind_speeds (Any):
-                Input for wind_speeds.
-            wind_directions (Any):
-                Input for wind_directions.
-            datetimes (Any):
-                Input for datetimes.
-            height_above_ground (Any):
-                Input for height_above_ground.
-        """
-
-        for arg_name, arg_value in locals().items():
-            if arg_name == "height_above_ground":
-                if arg_value <= 0:
-                    raise ValueError(f"{arg_name} must be greater than 0")
-                continue
-
-            # check that arg is iterable, and not a string
-            try:
-                iter(arg_value)
-            except TypeError as exc:
-                raise ValueError(
-                    f"{arg_name} must be single-dimension iterable"
-                ) from exc
-
-            if isinstance(arg_value, str):
-                raise ValueError(f"{arg_name} must be single-dimension iterable")
-
-            # check that iterable is flat
-            if any(isinstance(el, (list, np.ndarray, tuple)) for el in arg_value):
-                raise ValueError(f"{arg_name} must be single-dimension iterable")
-
-            if arg_name in ["wind_speeds", "wind_directions"]:
-                # check that inputs are numeric
-                if not all(isinstance(el, (int, float, np.number)) for el in arg_value):
-                    raise ValueError(f"{arg_name} must contain numeric values")
-
-                if arg_name == "wind_speeds":
-                    if min(arg_value) < 0:
-                        raise ValueError(f"{arg_name} must be in m/s and ≥ 0")
-                if arg_name == "wind_directions":
-                    if (min(arg_value) < 0) or (max(arg_value) > 360):
-                        raise ValueError(
-                            f"{arg_name} must be in degrees (with north at 0) and in the range 0-360"
-                        )
-
-                # check that iterable contains no NaN values
-                if sum(np.isnan(arg_value)) > 0:
-                    raise ValueError(f"{arg_name} contains null values")
-
-            else:
-                # check that inputs are datetimes
-                if not all(
-                    isinstance(el, (datetime, pd.Timestamp, np.datetime64))
-                    for el in arg_value
-                ):
-                    raise ValueError(f"{arg_name} must contain datetime-like values")
-
-                # check that all datetimes are unique
-                if len(datetimes) != len(np.unique(datetimes)):
-                    raise ValueError(f"duplicate values are present in {arg_name}")
-
-        # check height above ground validity
-        if height_above_ground < 0.1:
-            raise ValueError("height_above_ground must be >= 0.1")
-
-        # check that inputs are the same shape/size
-        if not len(wind_speeds) == len(wind_directions) == len(datetimes):
-            raise ValueError(
-                f"wind_speeds, wind_directions and datetimes must be the same length. ({len(wind_speeds)} != {len(wind_directions)} != {len(datetimes)})."
-            )
-
-    ###################
-    # GENERAL METHODS #
-    ###################
-
-    def mean(self) -> float:
-        """Return the mean wind speed for this object."""
-        return self.ws.mean()
-
-    def min(self) -> float:
-        """Return the min wind speed for this object."""
-        return self.ws.min()
-
-    def max(self) -> float:
-        """Return the max wind speed for this object."""
-        return self.ws.max()
-
-    def median(self) -> float:
-        """Return the median wind speed for this object."""
-        return self.ws.median()
-
-    def calm(self, threshold: float = 0.1) -> float:
-        """Return the proportion of timesteps "calm" (i.e. wind-speed ≤ 0.1)."""
-        return (self.ws <= threshold).sum() / len(self.ws)
-
-    def percentile(self, percentile: float) -> float:
-        """Calculate the wind speed at the given percentile.
-
-        Args:
-            percentile (float):
-                The percentile to calculate.
-
-        Returns:
-            float:
-                Wind speed at the given percentile.
-        """
-        return self.ws.quantile(percentile)
-
-    def resample(self, rule: Union[pd.DateOffset, pd.Timedelta, str]) -> Wind:
-        """Resample the wind data collection to a different timestep. If upsampling then 0m/s
-            and prevailing winds will be added to the data. If downsampling, then the average
-            speed and direction will be used.
-
-        Args:
-            rule (Union[pd.DateOffset, pd.Timedelta, str]):
-                A rule for resampling. This uses the same inputs as a Pandas Series.resample() method.
-
-        Returns:
-            Wind:
-                A wind data collection object!
-        """
-
-        warnings.warn(
-            (
-                "Resampling wind speeds and direction is generally not advisable. "
-                "When input directions are opposing, the average returned is likely inaccurate, "
-                "and the average speed does not include any outliers. USE WITH CAUTION!"
-            )
-        )
-
-        resampled_speeds = self.ws.resample(rule).mean()
-        resampled_datetimes = resampled_speeds.index.tolist()
-        resampled_directions = self.wd.resample(rule).apply(wind_direction_average)
-
-        if resampled_speeds.isna().sum() > 0:
-            warnings.warn(
-                (
-                    'Gaps in the source data mean that resampling must introduce "0m/s" wind speeds in '
-                    'order to create a valid dataset. This artificially increases the amount of "calm" hours.'
-                )
-            )
-            resampled_speeds.fillna(0, inplace=True)
-
-        if resampled_directions.isna().sum() > 0:
-            prevailing_direction = self.prevailing(
-                DirectionBins(32), n=1, as_angle=True
-            )[0]
-            warnings.warn(
-                (
-                    "Gaps in the source data mean that resampling must introduce artifical wind directions "
-                    "in order to create a valid dataset. The wind direction used is the average of the whole "
-                    "input dataset. This artificially increases the amount of wind from the prevailing "
-                    "direction."
-                )
-            )
-            resampled_directions.fillna(prevailing_direction, inplace=True)
-
-        return Wind(
-            resampled_speeds.tolist(),
-            resampled_directions.tolist(),
-            resampled_datetimes,
-            self.height_above_ground,
-        )
-
-    def prevailing(
-        self,
-        direction_bins: DirectionBins = DirectionBins(),
-        n: int = 1,
-        as_angle: bool = False,
-    ) -> Union[List[float], List[str]]:
-        """Calculate the prevailing wind direction/s for this object.
-
-        Args:
-            direction_bins (DirectionBins, optional):
-                The number of wind directions to bin values into.
-            n (int, optional):
-                The number of prevailing directions to return. Default is 1.
-            as_angle (bool, optional):
-                Return directions as an angle or as a cardinal direction. Default is False to return cardinal direction.
-
-        Returns:
-            Union[List[float], List[str]]:
-                A list of wind directions.
-        """
-
-        return direction_bins.prevailing(self.wd.tolist(), n, as_angle)
-
-    def probabilities(
-        self,
-        direction_bins: DirectionBins = DirectionBins(),
-        percentiles: Tuple[float, float] = (0.5, 0.95),
-    ) -> pd.DataFrame:
-        """Calculate the probabilities of wind speeds at the given percentiles, for the direction bins specified.
-
-        Args:
-            direction_bins (DirectionBins, optional):
-                A DirectionBins object. Defaults to DirectionBins().
-            percentiles (Tuple[float, float], optional):
-                A tuple of floats between 0-1 describing percentiles. Defaults to (0.5, 0.95).
-
-        Returns:
-            pd.DataFrame:
-                A table showing probabilities at the given percentiles.
-        """
-
-        binned_data = direction_bins.bin_data(self.wd.tolist(), self.ws.tolist())
-
-        probabilities = []
-        for percentile in percentiles:
-            temp = []
-            for _, vals in binned_data.items():
-                temp.append(np.quantile(vals, percentile))
-            probabilities.append(temp)
-
-        df = pd.DataFrame(
-            data=probabilities, columns=direction_bins.midpoints, index=percentiles  # type: ignore
-        )
-        return df.T
-
-    def filter_by_analysis_period(
-        self,
-        analysis_period: Union[AnalysisPeriod, Tuple[AnalysisPeriod]],
-    ) -> Wind:
-        """Filter the current object by a ladybug AnalysisPeriod object.
-
-        Args:
-            analysis_period (AnalysisPeriod):
-                An AnalysisPeriod object.
-
-        Returns:
-            Wind:
-                A dataset describing historic wind speed and direction relationship.
-        """
-
-        if isinstance(analysis_period, AnalysisPeriod):
-            analysis_period = (analysis_period,)
-
-        for ap in analysis_period:
-            if ap.timestep != 1:
-                raise ValueError("The timestep of the analysis period must be 1 hour.")
-
-        # remove 29th Feb dates where present
-        df = self.df
-        df = df[~((df.index.month == 2) & (df.index.day == 29))]
-
-        # filter available data
-        possible_datetimes = [
-            DateTime(dt.month, dt.day, dt.hour, dt.minute) for dt in df.index
-        ]
-        lookup = dict(
-            zip(AnalysisPeriod().datetimes, analysis_period_to_boolean(analysis_period))
-        )
-        mask = [lookup[i] for i in possible_datetimes]
-        df = df[mask]
-
-        return Wind.from_dataframe(
-            df,
-            wind_speed_column="speed",
-            wind_direction_column="direction",
-            height_above_ground=self.height_above_ground,
-        )
-
-    def filter_by_boolean_mask(self, mask: Tuple[bool]) -> Wind:
-        """Filter the current object by a boolean mask.
-
-        Returns:
-            Wind:
-                A dataset describing historic wind speed and direction relationship.
-        """
-
-        if len(mask) != len(self.ws):
-            raise ValueError(
-                "The length of the boolean mask must match the length of the current object."
-            )
-
-        return Wind(
-            self.ws[mask].tolist(),
-            self.wd[mask].tolist(),
-            self.datetimes[mask],
-            self.height_above_ground,
-        )
-
-    def filter_by_time(
-        self,
-        months: Tuple[float] = tuple(range(1, 13, 1)),  # type: ignore
-        hours: Tuple[int] = tuple(range(0, 24, 1)),  # type: ignore
-        years: Tuple[int] = tuple(range(1900, 2100, 1)),
-    ) -> Wind:
-        """Filter the current object by month and hour.
-
-        Args:
-            months (List[int], optional):
-                A list of months. Defaults to all months.
-            hours (List[int], optional):
-                A list of hours. Defaults to all hours.
-            years (Tuple[int], optional):
-                A list of years to include. Default to all years since 1900.
-
-        Returns:
-            Wind:
-                A dataset describing historic wind speed and direction relationship.
-        """
-
-        if years is None:
-            indices = np.argwhere(
-                np.all(
-                    [
-                        self.datetimes.month.isin(months),
-                        self.datetimes.hour.isin(hours),
-                    ],
-                    axis=0,
-                )
-            ).flatten()
-        else:
-            indices = np.argwhere(
-                np.all(
-                    [
-                        self.datetimes.year.isin(years),
-                        self.datetimes.month.isin(months),
-                        self.datetimes.hour.isin(hours),
-                    ],
-                    axis=0,
-                )
-            ).flatten()
-        return Wind(
-            self.ws.iloc[indices].tolist(),
-            self.wd.iloc[indices].tolist(),
-            self.datetimes[indices],
-            self.height_above_ground,
-        )
-
-    def filter_by_direction(
-        self, left_angle: float = 0, right_angle: float = 360, inclusive: bool = True
-    ) -> Wind:
-        """Filter the current object by wind direction, based on the angle as observed from a location.
-
-        Args:
-            left_angle (float):
-                The left-most angle, to the left of which wind speeds and directions will be removed.
-            right_angle (float):
-                The right-most angle, to the right of which wind speeds and directions will be removed.
-            inclusive (bool, optional):
-                Include values that are exactly the left or right angle values.
-
-        Return:
-            Wind:
-                A Wind object!
-        """
-
-        if left_angle < 0 or right_angle > 360:
-            raise ValueError("Angle limits must be between 0 and 360 degrees.")
-
-        if (left_angle == right_angle) or (left_angle == 360 and right_angle == 0):
-            raise ValueError("Angle limits cannot be identical.")
-
-        if left_angle > right_angle:
-            if inclusive:
-                mask = (self.wd >= left_angle) | (self.wd <= right_angle)
-            else:
-                mask = (self.wd > left_angle) | (self.wd < right_angle)
-        else:
-            if inclusive:
-                mask = (self.wd >= left_angle) & (self.wd <= right_angle)
-            else:
-                mask = (self.wd > left_angle) & (self.wd < right_angle)
-
-        return Wind(
-            self.ws[mask].tolist(),
-            self.wd[mask].tolist(),
-            self.datetimes[mask],
-            self.height_above_ground,
-        )
-
-    def filter_by_speed(
-        self, min_speed: float = 0, max_speed: float = 999, inclusive: bool = True
-    ) -> Wind:
-        """Filter the current object by wind speed, based on given low-high limit values.
-
-        Args:
-            min_speed (float):
-                The lowest speed to include. Values below this wil be removed.
-            max_speed (float):
-                The highest speed to include. Values above this wil be removed.
-            inclusive (bool, optional):
-                Include values that are exactly the left or right angle values.
-
-        Return:
-            Wind:
-                A Wind object!
-        """
-
-        if min_speed < 0:
-            raise ValueError("min_speed cannot be negative.")
-
-        if max_speed <= min_speed:
-            raise ValueError("min_speed must be less than max_speed.")
-
-        if inclusive:
-            mask = (self.ws >= min_speed) & (self.ws <= max_speed)
-        else:
-            mask = (self.ws > min_speed) & (self.ws < max_speed)
-
-        return Wind(
-            self.ws[mask].tolist(),
-            self.wd[mask].tolist(),
-            self.datetimes[mask],
-            self.height_above_ground,
-        )
-
-    def frequency_table(
-        self,
-        speed_bins: Tuple[float] = (0, 0.1) + tuple(range(2, 39, 2)) + (np.inf,),
-        direction_bins: DirectionBins = DirectionBins(),
-        density: bool = False,
-        include_counts: bool = False,
-    ) -> pd.DataFrame:
-        """Create a table with wind direction per rows, and wind speed per column.
-
-        Args:
-            speed_bins (List[float], optional):
-                A list of bins edges, between which wind speeds will be binned. Defaults to (0, 0.1, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 99.9).
-            direction_bins (DirectionBins, optional):
-                A DirectionBins object. Defaults to DirectionBins().
-            density (bool, optional):
-                Set to True to return density (0-1) instead of count. Defaults to False.
-            include_counts (bool, optional):
-                Include counts for each row and columns if True. Defaults to False.
-
-        Returns:
-            pd.DataFrame:
-                A frequency table.
-        """
-
-        if direction_bins.directions <= 3:
-            raise ValueError(
-                "At least 4 directions should be specified in the DirectionBins object."
-            )
-
-        df = self.df
-
-        # bin data using speed bins and direction
-        ds = []
-        speed_bin_labels = []
-        for low, high in rolling_window(speed_bins, 2):
-            speed_bin_labels.append((low, high))
-            mask = (df.speed >= low) & (df.speed < high)
-            binned = direction_bins.bin_data(df.direction[mask])
-            ds.append({k: len(v) for k, v in binned.items()})
-        df = pd.DataFrame.from_dict(ds).T
-        df.columns = speed_bin_labels
-
-        if density:
-            df = df / df.sum().sum()
-
-        if include_counts:
-            df = pd.concat([df, df.sum(axis=1).rename("sum")], axis=1)
-            df = pd.concat(
-                [df, df.sum(axis=0).to_frame().T.rename(index={0: "sum"})], axis=0
-            )
-
-        return df.T
-
-    def weibull_pdf(self) -> Tuple[float]:
-        """Calculate the parameters of an exponentiated Weibull continuous random variable.
-
-        Returns:
-            k (float):
-                Shape parameter
-            loc (float):
-                Location parameter.
-            c (float):
-                Scale parameter.
-        """
-        return weibull_pdf(self.ws.tolist())
-
-    def weibull_directional(
-        self, direction_bins: DirectionBins = DirectionBins()
-    ) -> pd.DataFrame:
-        """Calculate directional weibull coefficients for the given number of directions.
-
-        Args:
-            direction_bins (DirectionBins, optional):
-                A DirectionBins object. Defaults to DirectionBins().
-
-        Returns:
-            pd.DataFrame:
-                A DataFrame object.
-        """
-        binned_data = direction_bins.bin_data(self.wd.tolist(), self.ws.tolist())
-        return weibull_directional(binned_data)
-
-    def to_height(
-        self,
-        target_height: float,
-        terrain_roughness_length: float = 1,
-        log_function: bool = True,
-    ) -> Wind:
-        """Translate the object to a different height above ground.
-
-        Args:
-            target_height (float):
-                Height to translate to (in m).
-            terrain_roughness_length (float, optional):
-                Terrain roughness (how big objects are to adjust translation). Defaults to 1.
-            log_function (bool, optional):
-                Whether to use log-function or pow-function. Defaults to True.
-
-        Returns:
-            Wind:
-                A translated Wind object.
-        """
-        ws = wind_speed_at_height(
-            reference_value=self.ws,  # type: ignore
-            reference_height=self.height_above_ground,
-            target_height=target_height,
-            terrain_roughness_length=terrain_roughness_length,
-            log_function=log_function,
-        )
-        return Wind(ws.tolist(), self.wd.tolist(), self.datetimes, target_height)  # type: ignore
-
-    def apply_directional_factors(
-        self, direction_bins: DirectionBins, factors: Tuple[float]
-    ) -> Wind:
-        """Adjust wind speed values by a set of factors per direction.
-
-        Args:
-            direction_bins (DirectionBins):
-                The number of directions to bin wind-directions into.
-            factors (Tuple[float], optional):
-                Adjustment factors per direction. Defaults to (i for i in range(8)).
-
-        Returns:
-            Wind:
-                An adjusted Wind object.
-        """
-
-        factors = np.array(factors).tolist()
-
-        if len(factors) != len(direction_bins):
-            raise ValueError(
-                f"number of factors must equal number of directional bins ({len(direction_bins)} != {len(factors)})"
-            )
-
-        # repeat first element in inputs to apply to the two directions centered about North
-        if direction_bins.is_split:
-            factors = factors + [factors[0]]  # type: ignore
-
-        # create direction bins for factor to be applied
-        intervals = direction_bins.interval_index
-
-        # bin data and get factor indices to apply to each value
-        binned = pd.cut(self.wd, bins=intervals, include_lowest=True, right=False)
-        d = {i: n for n, i in enumerate(intervals)}
-        factor_indices = (
-            binned.map(d).fillna(0).values.tolist()
-        )  # filling NaN with 0 as this is the value at which failure happen (0/360 doesn't fit into any of the bins)
-        all_factors = [factors[int(i)] for i in factor_indices]
-        ws = self.ws * all_factors
-
-        return Wind(
-            ws.tolist(), self.wd.tolist(), self.datetimes, self.height_above_ground
-        )
-
-    def exceedance(
-        self,
-        limit_value: float,
-        direction_bins: DirectionBins = DirectionBins(),
-        agg: str = "max",
-    ) -> pd.DataFrame:
-        """Calculate the % of time where a limit-value is exceeded for the given direction bins.
-
-        Args:
-            limit_value (float):
-                The value above which speed frequency will be be calculated.
-            direction_bins (DirectionBins, optional):
-                A DirectionBins object defining the bins into which wind will be split according to direction.
-            agg (str, optional):
-                The aggregation method to use. Defaults to "max".
-
-        Returns:
-            pd.DataFrame:
-                A table containing exceedance values.
-        """
-
-        with tqdm(range(1, 13, 1)) as t:
-            exceedances = []
-            for month in t:
-                t.set_description(
-                    f"Calculating wind speed exceedance > {limit_value}m/s for {calendar.month_abbr[month]}"
-                )
-
-                try:
-                    _w = self.filter_by_time(months=[month])
-                except ValueError:
-                    exceedances.append([0] * len(direction_bins))
-                    continue
-                binned_data = direction_bins.bin_data(_w.wd, _w.ws)
-                temp_exceedances = []
-                for _, values in binned_data.items():
-                    with warnings.catch_warnings():
-                        warnings.simplefilter("ignore")
-                        _exceedance_value = (
-                            np.array(values) > limit_value
-                        ).sum() / len(values)
-                    temp_exceedances.append(_exceedance_value)
-                exceedances.append(temp_exceedances)
-
-        df = pd.DataFrame(exceedances).fillna(0)
-
-        df.index = [calendar.month_abbr[i + 1] for i in df.index]  # type: ignore
-        df.columns = [(i[0], i[1]) for i in binned_data.keys()]  # type: ignore
-
-        # add aggregation
-        df = pd.concat([df, df.agg(agg, axis=1).rename(agg)], axis=1)
-        df = pd.concat(
-            [df, df.agg(agg, axis=0).to_frame().T.rename(index={0: agg})], axis=0
-        )
-
-        return df
-
-    def to_csv(self, csv_path: Union[str, Path]) -> Path:
-        """Save this object as a csv file.
-
-        Args:
-            csv_path (Union[str, Path]):
-                The path containing the CSV file.
-
-        Returns:
-            Path:
-                The resultant CSV file.
-        """
-        csv_path = Path(csv_path)
-        self.df.to_csv(csv_path)
-        return csv_path
-
-    def wind_matrix(self) -> pd.DataFrame:
-        """Calculate average wind speed and direction for each month and hour of day in a pandas DataFrame.
-        Returns:
-            pd.DataFrame:
-                A DataFrame containing average wind speed and direction for each month and hour of day.
-        """
-
-        wind_directions = (
-            (
-                (
-                    self.wd.groupby(
-                        [self.wd.index.month, self.wd.index.hour], axis=0
-                    ).apply(wind_direction_average)
-                    # + 90
-                )
-                % 360
-            )
-            .unstack()
-            .T
-        )
-        wind_directions.columns = [
-            calendar.month_abbr[i] for i in wind_directions.columns
-        ]
-        wind_speeds = (
-            self.ws.groupby([self.ws.index.month, self.ws.index.hour], axis=0)
-            .mean()
-            .unstack()
-            .T
-        )
-        wind_speeds.columns = [calendar.month_abbr[i] for i in wind_speeds.columns]
-
-        df = pd.concat(
-            [wind_directions, wind_speeds], axis=1, keys=["direction", "speed"]
-        )
-
-        return df
-
-    ##################################
-    # PLOTTING/VISUALISATION METHODS #
-    ##################################
-
-    def plot_timeseries(self, ax: plt.Axes = None, color: str = "grey") -> plt.Axes:  # type: ignore
-        """Create a simple line plot of wind speed.
-
-        Args:
-            ax (plt.Axes, optional):
-                The axes to plot on. If None, the current axes will be used.
-            color (str, optional):
-                The color of the line to plot. Default is "blue".
-
-        Returns:
-            plt.Axes:
-                A matplotlib Axes object.
-
-        """
-
-        if ax is None:
-            ax = plt.gca()
-
-        timeseries(self.ws, ax=ax, color=color)
-        ax.set_title(str(self))
-
-        return ax
-
-    def plot_windrose(
-        self,
-        ax: plt.Axes = None,
-        direction_bins: DirectionBins = DirectionBins(),
-        bins: Union[int, List[float]] = BEAUFORT_CATEGORIES.bins,
-        include_legend: bool = True,
-        include_percentages: bool = False,
-        calm_threshold: float = 0.1,
-        **kwargs,
-    ) -> plt.Axes:  # type: ignore
-        """Create a windrose.
-
-        Args:
-            ax (plt.Axes, optional):
-                The axes to plot on. If None, the current axes will be used.
-            direction_bins (DirectionBins, optional):
-                A DirectionBins object.
-            bins (Union[int, List[float], CategoriesBase], optional):
-                Bins to sort data into. Defaults to Beaufort scale.
-            include_legend (bool, optional):
-                Set to True to include the legend. Defaults to True.
-            include_percentages (bool, optional):
-                Add bin totals as % to rose. Defaults to False.
-            cmap (Union[Colormap, str], optional):
-                Use a custom colormap. Defaults to "YlGnBu".
-            include_calm_threshold (bool, optional):
-                Set to True to include the calm threshold in the legend. Defaults to True.
-            kwargs:
-                Additional keyword arguments to pass to the windrose function.
-
-        Returns:
-            plt.Axes:
-                A matplotlib Axes object.
-        """
-
-        # remove calm hours and store % calm
-        new_w = self.filter_by_speed(
-            min_speed=calm_threshold, max_speed=np.inf, inclusive=False
-        )
-
-        if "cmap" not in kwargs:
-            kwargs["cmap"] = "YlGnBu"
-
-        return windrose(
-            wind_directions=new_w.wd.tolist(),
-            data=new_w.ws.tolist(),
-            ax=ax,
-            direction_bins=direction_bins,
-            data_bins=bins,
-            include_legend=include_legend,
-            include_percentages=include_percentages,
-            data_unit="m/s",
-            **kwargs,
-        )
-
-    def plot_windhist(
-        self,
-        direction_bins: DirectionBins = DirectionBins(),
-        speed_bins: List[float] = None,
-        density: bool = False,
-        include_cbar: bool = True,
-        title: str = None,
-        cmap: Union[Colormap, str] = "magma_r",
-        calm_threshold: float = 0.1,
-    ) -> plt.Axes:
-        """_summary_
-
-        Args:
-            direction_bins (DirectionBins, optional): _description_. Defaults to DirectionBins().
-            speed_bins (List[float], optional): _description_. Defaults to None.
-            density (bool, optional): _description_. Defaults to False.
-            include_cbar (bool, optional): _description_. Defaults to True.
-            title (str, optional): _description_. Defaults to None.
-            cmap (Union[Colormap, str], optional): _description_. Defaults to "magma_r".
-
-        Returns:
-            plt.Axes: _description_
-        """
-
-        # remove calm hours and store % calm
-        calm_percentage = self.calm(calm_threshold)
-        new_w = self.filter_by_speed(
-            min_speed=calm_threshold, max_speed=np.inf, inclusive=False
-        )
-
-        if speed_bins is None:
-            _low = int(np.floor(new_w.min()))
-            _high = int(np.ceil(new_w.max()))
-            speed_bins = np.linspace(_low, _high, (_high - _low) + 1)
-
-        if title is not None:
-            ti = f"{title}\n{calm_percentage:0.1%} calm (≤ {calm_threshold}m/s)"
-        else:
-            ti = f"{self}\n{calm_percentage:0.1%} calm (≤ {calm_threshold}m/s)"
-
-        frequency_table = new_w.frequency_table(
-            speed_bins, direction_bins, density=density, include_counts=False
-        )
-
-        direction_angles = np.deg2rad(direction_bins.midpoints)
-        radial_bins = [np.mean(i) for i in frequency_table.index]
-
-        if density:
-            cmap_label = "Frequency"  #
-            cbar_freq = True
-        else:
-            cmap_label = "n-occurences"
-            cbar_freq = False
-
-        return radial_histogram(
-            direction_angles,
-            radial_bins,
-            frequency_table.values,
-            cmap=cmap,
-            include_labels=False,
-            include_cbar=include_cbar,
-            cmap_label=cmap_label,
-            cbar_freq=cbar_freq,
-            title=ti,
-        )
-
-    def plot_wind_matrix(
-        self,
-        ax: plt.Axes = None,
-        show_values: bool = False,
-        additional_data: pd.Series = None,
-        **kwargs,
-    ) -> plt.Axes:  # type: ignore
-        """Create a plot showing the annual wind speed and direction bins using the month_time_average method."""
-
-        #                 title: str = None,
-        # cmap: Union[Colormap, str] = "YlGnBu",
-        # cbar_title: str = None,
-
-        cmap = kwargs.pop("cmap", "YlGnBu" if additional_data is None else "viridis")
-        cbar_title = kwargs.pop(
-            "cbar_title",
-            "Average speed (m/s)" if additional_data is None else additional_data.name,
-        )
-        title = kwargs.pop("title", str(self))
-
-        return wind_matrix(
-            wind_speeds=self.ws,
-            wind_directions=self.wd,
-            ax=ax,
-            cmap=cmap,
-            title=title,
-            cbar_title=cbar_title,
-            show_values=show_values,
-            additional_data=additional_data,
-            **kwargs,
-        )
-
-    def plot_speed_frequency(self, title: str = None, speed_bins: Union[List[float], int] = None) -> plt.Figure:  # type: ignore
-        """Create a histogram showing wind speed frequency"""
-
-        # TODO - remake this figure generation as a plt.Axes object!
-
-        if title is None:
-            title = str(self)
-        else:
-            title = f"{self}\n{title}"
-
-        if speed_bins is None:
-            speed_bins = np.linspace(min(self.ws), np.quantile(self.ws, 0.999), 16)
-        elif isinstance(speed_bins, int):
-            speed_bins = np.linspace(
-                min(self.ws), np.quantile(self.ws, 0.999), speed_bins
-            )
-        else:
-            pass
-        percentiles = (0.5, 0.95)
-
-        # TODO - reimplement weibull_pdf plotting curve
-
-        return wind_speed_frequency(
-            self.ws.tolist(),
-            speed_bins=speed_bins,
-            # weibull=self.weibull_pdf(),
-            percentiles=percentiles,
-            title=title,
-        )
-
-    def plot_cumulative_probability(
-        self, percentiles: Tuple[float] = (0.5, 0.95), title: str = None
-    ) -> plt.Figure:  # type: ignore
-        """Create a cumulative probability plot"""
-
-        if title is None:
-            title = str(self)
-        else:
-            title = f"{self}\n{title}"
-
-        return wind_cumulative_probability(
-            self.ws.tolist(),
-            speed_bins=np.linspace(0, 25, 50).tolist(),
-            percentiles=percentiles,
-            title=title,
-        )
-
-    # def plot_windrose_matrix(
-    #     self,
-    #     month_bins: Tuple[List[int]],
-    #     hour_bins: Tuple[List[int]],
-    #     direction_bins: DirectionBins = DirectionBins(),
-    #     data_bins: List[float] = None,
-    #     title: str = None,
-    # ) -> plt.Figure:
-    #     """Create a plot showing the annual wind direction in a matrix of month and hour bins."""
-    #     fig = windrose_matrix(
-    #         wind_direction=self.wd,
-    #         data=self.ws,
-    #         month_bins=month_bins,
-    #         hour_bins=hour_bins,
-    #         data_bins=data_bins,
-    #         direction_bins=direction_bins,
-    #         cmap="YlGnBu",
-    #         title=title if title is not None else str(self),
-    #     )
-    #     return fig
-
-
-# def weighted_wind_speed_direction(
-#     wind_speeds: List[float], wind_directions: List[float]
-# ) -> List[float]:
-#     """Return a speed-weighted average wind direction and speed for a set of input wind speeds and directions.
-
-#     Args:
-#         wind_speeds (List[float]):
-#             A collection of wind speeds, in m/s.
-#         wind_directions (List[float]):
-#             A collection of wind directions, in degrees from North (0).
-
-#     Returns:
-#         List[float]:
-#             A weighted average wind speed and direction.
-#     """
-#     warnings.warn("UNDER DEFVEKLOPMENT")
-#     # convert directions into XY vectors (including speed magnitude)
-#     wind_vectors = (
-#         np.array([angle_to_vector(d) for d in wind_directions]).T * wind_speeds
-#     ).T
-
-#     # create weights based on wind speed (0-1, higher speeds higher weighting)
-#     weights = np.array(wind_speeds) / np.array(wind_speeds).sum()
-
-#     # multiply by wind speeds (magnitude) to get the average wind vector (weighted by speed)
-#     resultant_wind_vector = np.average((wind_vectors.T).T, axis=0, weights=weights)
-
-#     # get unit vector and magnitude (wind_speed)
-#     resultant_wind_speed = np.linalg.norm(resultant_wind_vector)
-
-#     # determine new wind direction (angle from north)
-#     try:
-#         resultant_wind_direction = np.degrees(
-#             Vector2D.from_array(resultant_wind_vector).angle_counterclockwise(
-#                 Vector2D(0, 1)
-#             )
-#         )
-#     except:
-#         resultant_wind_direction = 0
-
-#     return resultant_wind_speed, resultant_wind_direction
diff --git a/LadybugTools_Engine/Python/tests/__init__.py b/LadybugTools_Engine/Python/tests/__init__.py
index 91482505..5af73670 100644
--- a/LadybugTools_Engine/Python/tests/__init__.py
+++ b/LadybugTools_Engine/Python/tests/__init__.py
@@ -1,28 +1,43 @@
 import getpass
 from pathlib import Path
+from weakref import WeakKeyDictionary
 
 from honeybee.config import folders as hb_folders
+from honeybee.model import Model
+from ladybug.epw import EPW
+from ladybug.wea import Wea
 
-# set identifier for all downstream processes
-BASE_IDENTIFIER = "pytest"
+# identifier for all downstream processes
+BASE_IDENTIFIER = "LBTBHoM_pytest"
 
-# set simulation directory for downstream processes
+# simulation directory for downstream processes
 hb_folders.default_simulation_folder = f"C:/Users/{getpass.getuser()}/simulation"
 
-# set identifiers for ExternalComfort simulation and SpatialComfort simulation
-EXTERNAL_COMFORT_IDENTIFIER = f"{BASE_IDENTIFIER}_EC"
+# identifiers for ExternalComfort simulation and SpatialComfort simulation
+EXTERNAL_COMFORT_IDENTIFIER = f"{BASE_IDENTIFIER}_external_comfort"
 EXTERNAL_COMFORT_DIRECTORY = (
     Path(hb_folders.default_simulation_folder) / EXTERNAL_COMFORT_IDENTIFIER
 )
-SPATIAL_COMFORT_IDENTIFIER = f"{BASE_IDENTIFIER}_SC"
+SPATIAL_COMFORT_IDENTIFIER = f"{BASE_IDENTIFIER}_spatial_comfort"
 SPATIAL_COMFORT_DIRECTORY = (
     Path(hb_folders.default_simulation_folder) / SPATIAL_COMFORT_IDENTIFIER
 )
 
-# set source files used in testing
-MODEL_FILE = Path(__file__).parent / "assets" / "example.hbjson"
+# source files used in testing
+SPATIAL_COMFORT_MODEL_FILE = (
+    Path(__file__).parent / "assets" / "example_spatial_comfort.hbjson"
+)
+SPATIAL_COMFORT_MODEL_OBJ: Model = Model.from_hbjson(SPATIAL_COMFORT_MODEL_FILE)
+
 EPW_FILE = Path(__file__).parent / "assets" / "example.epw"
 EPW_CSV_FILE = (Path(__file__).parent / "assets" / "example.csv",)
+EPW_OBJ = EPW(EPW_FILE)
+
+TEST_DAYLIGHT_MODEL: Model = Model.from_hbjson(
+    Path(__file__).parent / "assets" / "example_daylight.hbjson"
+)
+TEST_WEA = Wea.from_epw_file(EPW_FILE)
+
 RES_FILE = Path(__file__).parent / "assets" / "example.res"
 SQL_FILE = Path(__file__).parent / "assets" / "example.sql"
 ILL_FILE = Path(__file__).parent / "assets" / "example.ill"
diff --git a/LadybugTools_Engine/Python/tests/assets/example.hbjson b/LadybugTools_Engine/Python/tests/assets/example.hbjson
deleted file mode 100644
index f7d457c9..00000000
--- a/LadybugTools_Engine/Python/tests/assets/example.hbjson
+++ /dev/null
@@ -1 +0,0 @@
-{"display_name": "pytest_SC", "identifier": "pytest_SC", "tolerance": 0.01, "angle_tolerance": 1.0, "orphaned_faces": [{"display_name": "Face_d4dce78d", "identifier": "Face_d4dce78d", "geometry": {"type": "Face3D", "plane": {"o": [6.0, 6.0, 0.0], "n": [0.0, 0.0, 1.0], "type": "Plane", "x": [1.0, 0.0, 0.0]}, "boundary": [[-6.0, 6.0, 0.0], [-6.0, -6.0, 0.0], [6.0, -6.0, 0.0], [6.0, 6.0, 0.0]], "holes": [[[-3.0, -3.0, 0.0], [-3.0, -1.0, 0.0], [-1.0, -1.0, 0.0], [-1.0, -3.0, 0.0]], [[1.0, 3.0, 0.0], [3.0, 3.0, 0.0], [3.0, 1.0, 0.0], [1.0, 1.0, 0.0]]]}, "face_type": "RoofCeiling", "boundary_condition": {"type": "Ground"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_0", "geometry": {"type": "Face3D", "plane": {"o": [-3.0, -3.0, 8.003790604710268], "n": [-1.0, 0.0, 0.0], "type": "Plane", "x": [0.0, 1.0, 0.0]}, "boundary": [[-3.0, -1.0, 8.003790604710268], [-3.0, -1.0, 0.0], [-3.0, -3.0, 0.0], [-3.0, -3.0, 8.003790604710268]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_1", "geometry": {"type": "Face3D", "plane": {"o": [-5.0, -1.0, 8.003790604710268], "n": [-0.0, 1.0, 0.0], "type": "Plane", "x": [1.0, 0.0, 0.0]}, "boundary": [[-1.0, -1.0, 8.003790604710268], [-1.0, -1.0, 0.0], [-3.0, -1.0, 0.0], [-3.0, -1.0, 8.003790604710268]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_2", "geometry": {"type": "Face3D", "plane": {"o": [-1.0, 3.0, 8.003790604710268], "n": [1.0, 0.0, 0.0], "type": "Plane", "x": [0.0, -1.0, 0.0]}, "boundary": [[-1.0, -3.0, 8.003790604710268], [-1.0, -3.0, 0.0], [-1.0, -1.0, 0.0], [-1.0, -1.0, 8.003790604710268]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_3", "geometry": {"type": "Face3D", "plane": {"o": [5.0, -3.0, 8.003790604710268], "n": [-0.0, -1.0, -0.0], "type": "Plane", "x": [-1.0, 0.0, 0.0]}, "boundary": [[-3.0, -3.0, 8.003790604710268], [-3.0, -3.0, 0.0], [-1.0, -3.0, 0.0], [-1.0, -3.0, 8.003790604710268]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_4", "geometry": {"type": "Face3D", "plane": {"o": [-3.0, -3.0, 8.003790604710268], "n": [-0.0, -0.0, 1.0], "type": "Plane", "x": [0.0, 1.0, 0.0]}, "boundary": [[-3.0, -1.0, 8.003790604710268], [-3.0, -3.0, 8.003790604710268], [-1.0, -3.0, 8.003790604710268], [-1.0, -1.0, 8.003790604710268]]}, "face_type": "RoofCeiling", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_5", "geometry": {"type": "Face3D", "plane": {"o": [-3.0, -3.0, 0.0], "n": [0.0, 0.0, -1.0], "type": "Plane", "x": [-1.0, 0.0, 0.0]}, "boundary": [[-1.0, -1.0, 0.0], [-1.0, -3.0, 0.0], [-3.0, -3.0, 0.0], [-3.0, -1.0, 0.0]]}, "face_type": "Floor", "boundary_condition": {"type": "Ground"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_0", "geometry": {"type": "Face3D", "plane": {"o": [1.0, 1.0, 11.613166712310097], "n": [-1.0, 0.0, 0.0], "type": "Plane", "x": [0.0, 1.0, 0.0]}, "boundary": [[1.0, 3.0, 11.613166712310097], [1.0, 3.0, 0.0], [1.0, 1.0, 0.0], [1.0, 1.0, 11.613166712310097]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_1", "geometry": {"type": "Face3D", "plane": {"o": [-1.0, 3.0, 11.613166712310097], "n": [-0.0, 1.0, 0.0], "type": "Plane", "x": [1.0, 0.0, 0.0]}, "boundary": [[3.0, 3.0, 11.613166712310097], [3.0, 3.0, 0.0], [1.0, 3.0, 0.0], [1.0, 3.0, 11.613166712310097]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_2", "geometry": {"type": "Face3D", "plane": {"o": [3.0, 7.0, 11.613166712310097], "n": [1.0, 0.0, 0.0], "type": "Plane", "x": [0.0, -1.0, 0.0]}, "boundary": [[3.0, 1.0, 11.613166712310097], [3.0, 1.0, 0.0], [3.0, 3.0, 0.0], [3.0, 3.0, 11.613166712310097]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_3", "geometry": {"type": "Face3D", "plane": {"o": [9.0, 1.0, 11.613166712310097], "n": [-0.0, -1.0, -0.0], "type": "Plane", "x": [-1.0, 0.0, 0.0]}, "boundary": [[1.0, 1.0, 11.613166712310097], [1.0, 1.0, 0.0], [3.0, 1.0, 0.0], [3.0, 1.0, 11.613166712310097]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_4", "geometry": {"type": "Face3D", "plane": {"o": [1.0, 1.0, 11.613166712310097], "n": [-0.0, -0.0, 1.0], "type": "Plane", "x": [0.0, 1.0, 0.0]}, "boundary": [[1.0, 3.0, 11.613166712310097], [1.0, 1.0, 11.613166712310097], [3.0, 1.0, 11.613166712310097], [3.0, 3.0, 11.613166712310097]]}, "face_type": "RoofCeiling", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_5", "geometry": {"type": "Face3D", "plane": {"o": [1.0, 1.0, 0.0], "n": [0.0, 0.0, -1.0], "type": "Plane", "x": [-1.0, 0.0, 0.0]}, "boundary": [[3.0, 3.0, 0.0], [3.0, 1.0, 0.0], [1.0, 1.0, 0.0], [1.0, 3.0, 0.0]]}, "face_type": "Floor", "boundary_condition": {"type": "Ground"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}], "type": "Model", "version": "1.50.4", "units": "Meters", "properties": {"radiance": {"modifier_sets": [], "modifiers": [], "type": "ModelRadianceProperties", "sensor_grids": [{"display_name": "pytest_SC", "identifier": "pytest_SC", "sensors": [{"pos": [-5.5, -5.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, -4.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, -3.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, -2.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, -1.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, -0.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, 0.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, 1.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, 2.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, 3.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, 4.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, 5.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-4.5, -5.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-4.5, -4.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-4.5, -3.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-4.5, -2.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-4.5, -1.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-4.5, -0.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-4.5, 0.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, 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0.90000000000000002, "thickness": 0.050000000000000003, "solar_absorptance": 0.69999999999999996, "visible_absorptance": 0.69999999999999996, "roughness": "MediumRough", "type": "EnergyMaterial", "identifier": "Generic 50mm Insulation"}, {"conductivity": 45.0, "specific_heat": 410.0, "density": 7690.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.0015, "solar_absorptance": 0.5, "visible_absorptance": 0.5, "roughness": "Smooth", "type": "EnergyMaterial", "identifier": "Generic Painted Metal"}], "air_boundary_construction": "Generic Air Boundary", "shade_construction": "Generic Shade", "roof_ceiling_set": {"ground_construction": "Generic Underground Roof", "interior_construction": "Generic Interior Ceiling", "exterior_construction": "Generic Roof", "type": "RoofCeilingConstructionSetAbridged"}, "wall_set": {"ground_construction": "Generic Underground Wall", "interior_construction": "Generic Interior Wall", "exterior_construction": "Generic Exterior Wall", "type": "WallConstructionSetAbridged"}, "door_set": {"overhead_construction": "Generic Exterior Door", "interior_glass_construction": "Generic Single Pane", "exterior_glass_construction": "Generic Double Pane", "type": "DoorConstructionSetAbridged", "interior_construction": "Generic Interior Door", "exterior_construction": "Generic Exterior Door"}, "floor_set": {"ground_construction": "Generic Ground Slab", "interior_construction": "Generic Interior Floor", "exterior_construction": "Generic Exposed Floor", "type": "FloorConstructionSetAbridged"}, "type": "GlobalConstructionSet", "aperture_set": {"skylight_construction": "Generic Double Pane", "type": "ApertureConstructionSetAbridged", "interior_construction": "Generic Single Pane", "operable_construction": "Generic Double Pane", "window_construction": "Generic Double Pane"}}, "schedules": [], "program_types": [], "construction_sets": []}, "type": "ModelProperties"}}
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/tests/assets/example.ill b/LadybugTools_Engine/Python/tests/assets/example.ill
index 7b224016..4d9894c2 100644
--- a/LadybugTools_Engine/Python/tests/assets/example.ill
+++ b/LadybugTools_Engine/Python/tests/assets/example.ill
@@ -1,100 +1,25 @@
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3.1012330e+00	 2.8051557e+00	 1.3142790e+00	 9.3443729e-02	 3.1304595e-01	 1.3831742e+00	 2.4742770e+00	 3.0926282e+00	 3.0736126e+00	 9.5849293e-01	 4.9666241e-01	 2.5106299e-01	 1.2487508e-01	 5.2741748e-01	 9.8738360e-01	 3.0348394e+00	 3.0716267e+00	 2.6995749e+00	 1.3144715e+00	 2.5105122e-01	 3.1308550e-01	 2.4857397e+00	 2.4470029e+00	 3.0927594e+00	 3.0720074e+00	 2.4281085e+01	 1.3145016e+00	 2.8271076e-01	 1.2485830e-01	 5.2754277e-01	 9.8782420e-01	 1.2321497e+00	 3.0227089e+00	 9.5849198e-01	 5.2748066e-01	 4.7429857e-01	 2.8180531e-01	 1.3542522e+00	 2.4436803e+00	 3.0586553e+00	 4.5237365e+00	 3.7735844e+01	 1.3467619e+00	 9.3357243e-02	 1.2487915e-01	 5.2754188e-01	 9.5652890e-01	 1.2319825e+00	 1.2335488e+00	 9.5791048e-01	 5.2735937e-01	 1.2476802e-01	 1.2489416e-01	 1.3244672e+00	 2.4422612e+00	 3.0570495e+00	 3.0701303e+00	 2.7289458e+00	 1.3769538e+00	 2.8238460e-01	 5.6391382e-01	 2.5076878e+00	 3.2264402e+00	 3.9992893e+00	 3.0296211e+00	 1.7194148e+01	 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diff --git a/LadybugTools_Engine/Python/tests/assets/example.npy b/LadybugTools_Engine/Python/tests/assets/example.npy
index bcd6ce09..f2c09237 100644
Binary files a/LadybugTools_Engine/Python/tests/assets/example.npy and b/LadybugTools_Engine/Python/tests/assets/example.npy differ
diff --git a/LadybugTools_Engine/Python/tests/assets/example_daylight.hbjson b/LadybugTools_Engine/Python/tests/assets/example_daylight.hbjson
new file mode 100644
index 00000000..6c20eca6
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/assets/example_daylight.hbjson
@@ -0,0 +1,23913 @@
+{
+    "display_name": "LBTBHoM_pytest_daylight", 
+    "identifier": "LBTBHoM_pytest_daylight", 
+    "tolerance": 0.01, 
+    "angle_tolerance": 1.0, 
+    "rooms": [
+        {
+            "display_name": "Room_1", 
+            "identifier": "Room_1_aa5097b2", 
+            "faces": [
+                {
+                    "display_name": "Room_1_aa5097b2..Face0", 
+                    "identifier": "Room_1_aa5097b2..Face0", 
+                    "apertures": [
+                        {
+                            "display_name": "Room_1_aa5097b2..Face0_Glz0", 
+                            "identifier": "Room_1_aa5097b2..Face0_Glz0", 
+                            "outdoor_shades": [
+                                {
+                                    "display_name": "Room_1_aa5097b2..Face0_Glz0_Louver100", 
+                                    "identifier": "Room_1_aa5097b2..Face0_Glz0_Louver100", 
+                                    "geometry": {
+                                        "type": "Face3D", 
+                                        "plane": {
+                                            "o": [
+                                                1.5808823529411766, 
+                                                0.0, 
+                                                2.4999999000000002
+                                            ], 
+                                            "n": [
+                                                0.0, 
+                                                0.0, 
+                                                1.0
+                                            ], 
+                                            "type": "Plane", 
+                                            "x": [
+                                                1.0, 
+                                                0.0, 
+                                                0.0
+                                            ]
+                                        }, 
+                                        "boundary": [
+                                            [
+                                                0.91911764705882359, 
+                                                0.0, 
+                                                2.4999999000000002
+                                            ], 
+                                            [
+                                                0.91911764705882359, 
+                                                -0.10000000000000001, 
+                                                2.4999999000000002
+                                            ], 
+                                            [
+                                                1.5808823529411766, 
+                                                -0.10000000000000001, 
+                                                2.4999999000000002
+                                            ], 
+                                            [
+                                                1.5808823529411766, 
+                                                0.0, 
+                                                2.4999999000000002
+                                            ]
+                                        ]
+                                    }, 
+                                    "type": "Shade", 
+                                    "properties": {
+                                        "radiance": {
+                                            "type": "ShadeRadiancePropertiesAbridged"
+                                        }, 
+                                        "energy": {
+                                            "type": "ShadeEnergyPropertiesAbridged"
+                                        }, 
+                                        "type": "ShadePropertiesAbridged"
+                                    }
+                                }, 
+                                {
+                                    "display_name": "Room_1_aa5097b2..Face0_Glz0_Louver101", 
+                                    "identifier": "Room_1_aa5097b2..Face0_Glz0_Louver101", 
+                                    "geometry": {
+                                        "type": "Face3D", 
+                                        "plane": {
+                                            "o": [
+                                                1.5808823529411766, 
+                                                0.0, 
+                                                1.6500000000000004
+                                            ], 
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+                                0.050000000000000003, 
+                                0.08623256, 
+                                0.25869767999999999, 
+                                0.12934883999999999, 
+                                0.04311628, 
+                                0.050000000000000003
+                            ], 
+                            "type": "ScheduleDay", 
+                            "interpolate": false
+                        }, 
+                        {
+                            "identifier": "OfficeMedium BLDG_LIGHT_SCH_2013_Wkdy", 
+                            "times": [
+                                [
+                                    0, 
+                                    0
+                                ], 
+                                [
+                                    5, 
+                                    0
+                                ], 
+                                [
+                                    6, 
+                                    0
+                                ], 
+                                [
+                                    7, 
+                                    0
+                                ], 
+                                [
+                                    8, 
+                                    0
+                                ], 
+                                [
+                                    17, 
+                                    0
+                                ], 
+                                [
+                                    18, 
+                                    0
+                                ], 
+                                [
+                                    20, 
+                                    0
+                                ], 
+                                [
+                                    22, 
+                                    0
+                                ], 
+                                [
+                                    23, 
+                                    0
+                                ]
+                            ], 
+                            "values": [
+                                0.050000000000000003, 
+                                0.10000000000000001, 
+                                0.08623256, 
+                                0.25869767999999999, 
+                                0.77609304000000001, 
+                                0.43116280000000001, 
+                                0.25869767999999999, 
+                                0.17246512, 
+                                0.08623256, 
+                                0.04311628
+                            ], 
+                            "type": "ScheduleDay", 
+                            "interpolate": false
+                        }
+                    ]
+                }, 
+                {
+                    "identifier": "Generic Office Occupancy", 
+                    "schedule_rules": [
+                        {
+                            "apply_tuesday": true, 
+                            "apply_saturday": false, 
+                            "schedule_day": "OfficeMedium BLDG_OCC_SCH_Wkdy", 
+                            "apply_friday": true, 
+                            "apply_monday": true, 
+                            "start_date": [
+                                1, 
+                                1
+                            ], 
+                            "end_date": [
+                                12, 
+                                31
+                            ], 
+                            "apply_wednesday": true, 
+                            "type": "ScheduleRuleAbridged", 
+                            "apply_thursday": true, 
+                            "apply_sunday": false
+                        }, 
+                        {
+                            "apply_tuesday": false, 
+                            "apply_saturday": true, 
+                            "schedule_day": "OfficeMedium BLDG_OCC_SCH_Sat", 
+                            "apply_friday": false, 
+                            "apply_monday": false, 
+                            "start_date": [
+                                1, 
+                                1
+                            ], 
+                            "end_date": [
+                                12, 
+                                31
+                            ], 
+                            "apply_wednesday": false, 
+                            "type": "ScheduleRuleAbridged", 
+                            "apply_thursday": false, 
+                            "apply_sunday": false
+                        }
+                    ], 
+                    "default_day_schedule": "OfficeMedium BLDG_OCC_SCH_Default", 
+                    "holiday_schedule": "OfficeMedium BLDG_OCC_SCH_Default", 
+                    "summer_designday_schedule": "OfficeMedium BLDG_OCC_SCH_SmrDsn", 
+                    "schedule_type_limit": "Fractional", 
+                    "type": "ScheduleRulesetAbridged", 
+                    "winter_designday_schedule": "OfficeMedium BLDG_OCC_SCH_WntrDsn", 
+                    "day_schedules": [
+                        {
+                            "identifier": "OfficeMedium BLDG_OCC_SCH_Default", 
+                            "times": [
+                                [
+                                    0, 
+                                    0
+                                ], 
+                                [
+                                    6, 
+                                    0
+                                ], 
+                                [
+                                    18, 
+                                    0
+                                ]
+                            ], 
+                            "values": [
+                                0.0, 
+                                0.050000000000000003, 
+                                0.0
+                            ], 
+                            "type": "ScheduleDay", 
+                            "interpolate": false
+                        }, 
+                        {
+                            "identifier": "OfficeMedium BLDG_OCC_SCH_SmrDsn", 
+                            "times": [
+                                [
+                                    0, 
+                                    0
+                                ], 
+                                [
+                                    6, 
+                                    0
+                                ], 
+                                [
+                                    22, 
+                                    0
+                                ]
+                            ], 
+                            "values": [
+                                0.0, 
+                                1.0, 
+                                0.050000000000000003
+                            ], 
+                            "type": "ScheduleDay", 
+                            "interpolate": false
+                        }, 
+                        {
+                            "identifier": "OfficeMedium BLDG_OCC_SCH_WntrDsn", 
+                            "times": [
+                                [
+                                    0, 
+                                    0
+                                ]
+                            ], 
+                            "values": [
+                                0.0
+                            ], 
+                            "type": "ScheduleDay", 
+                            "interpolate": false
+                        }, 
+                        {
+                            "identifier": "OfficeMedium BLDG_OCC_SCH_Wkdy", 
+                            "times": [
+                                [
+                                    0, 
+                                    0
+                                ], 
+                                [
+                                    6, 
+                                    0
+                                ], 
+                                [
+                                    7, 
+                                    0
+                                ], 
+                                [
+                                    8, 
+                                    0
+                                ], 
+                                [
+                                    12, 
+                                    0
+                                ], 
+                                [
+                                    13, 
+                                    0
+                                ], 
+                                [
+                                    17, 
+                                    0
+                                ], 
+                                [
+                                    18, 
+                                    0
+                                ], 
+                                [
+                                    22, 
+                                    0
+                                ]
+                            ], 
+                            "values": [
+                                0.0, 
+                                0.10000000000000001, 
+                                0.20000000000000001, 
+                                0.94999999999999996, 
+                                0.5, 
+                                0.94999999999999996, 
+                                0.29999999999999999, 
+                                0.10000000000000001, 
+                                0.050000000000000003
+                            ], 
+                            "type": "ScheduleDay", 
+                            "interpolate": false
+                        }, 
+                        {
+                            "identifier": "OfficeMedium BLDG_OCC_SCH_Sat", 
+                            "times": [
+                                [
+                                    0, 
+                                    0
+                                ], 
+                                [
+                                    6, 
+                                    0
+                                ], 
+                                [
+                                    8, 
+                                    0
+                                ], 
+                                [
+                                    12, 
+                                    0
+                                ], 
+                                [
+                                    17, 
+                                    0
+                                ], 
+                                [
+                                    19, 
+                                    0
+                                ]
+                            ], 
+                            "values": [
+                                0.0, 
+                                0.10000000000000001, 
+                                0.29999999999999999, 
+                                0.10000000000000001, 
+                                0.050000000000000003, 
+                                0.0
+                            ], 
+                            "type": "ScheduleDay", 
+                            "interpolate": false
+                        }
+                    ]
+                }
+            ], 
+            "program_types": [
+                {
+                    "identifier": "Generic Office Program", 
+                    "infiltration": {
+                        "flow_per_exterior_area": 0.00022660000000000001, 
+                        "schedule": "Generic Office Infiltration", 
+                        "type": "InfiltrationAbridged", 
+                        "identifier": "Generic Office Infiltration"
+                    }, 
+                    "lighting": {
+                        "identifier": "Generic Office Lighting", 
+                        "radiant_fraction": 0.69999999999999996, 
+                        "visible_fraction": 0.20000000000000001, 
+                        "type": "LightingAbridged", 
+                        "return_air_fraction": 0.0, 
+                        "watts_per_area": 10.550000000000001, 
+                        "schedule": "Generic Office Lighting"
+                    }, 
+                    "electric_equipment": {
+                        "identifier": "Generic Office Equipment", 
+                        "radiant_fraction": 0.5, 
+                        "latent_fraction": 0.0, 
+                        "type": "ElectricEquipmentAbridged", 
+                        "watts_per_area": 10.33, 
+                        "lost_fraction": 0.0, 
+                        "schedule": "Generic Office Equipment"
+                    }, 
+                    "setpoint": {
+                        "heating_schedule": "Generic Office Heating", 
+                        "type": "SetpointAbridged", 
+                        "cooling_schedule": "Generic Office Cooling", 
+                        "identifier": "Generic Office Setpoints"
+                    }, 
+                    "ventilation": {
+                        "flow_per_area": 0.00030499999999999999, 
+                        "type": "VentilationAbridged", 
+                        "flow_per_person": 0.0023600000000000001, 
+                        "identifier": "Generic Office Ventilation"
+                    }, 
+                    "type": "ProgramTypeAbridged", 
+                    "people": {
+                        "identifier": "Generic Office People", 
+                        "radiant_fraction": 0.29999999999999999, 
+                        "people_per_area": 0.056500000000000002, 
+                        "latent_fraction": {
+                            "type": "Autocalculate"
+                        }, 
+                        "occupancy_schedule": "Generic Office Occupancy", 
+                        "type": "PeopleAbridged", 
+                        "activity_schedule": "Seated Adult Activity"
+                    }
+                }
+            ], 
+            "construction_sets": []
+        }, 
+        "type": "ModelProperties"
+    }
+}
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/tests/assets/example_spatial_comfort.hbjson b/LadybugTools_Engine/Python/tests/assets/example_spatial_comfort.hbjson
new file mode 100644
index 00000000..17129d31
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/assets/example_spatial_comfort.hbjson
@@ -0,0 +1 @@
+{"display_name": "LBTBHoM_pytest_spatial_comfort", "identifier": "LBTBHoM_pytest_spatial_comfort", "tolerance": 0.01, "angle_tolerance": 1.0, "orphaned_faces": [{"display_name": "Face_d4dce78d", "identifier": "Face_d4dce78d", "geometry": {"type": "Face3D", "plane": {"o": [6.0, 6.0, 0.0], "n": [0.0, 0.0, 1.0], "type": "Plane", "x": [1.0, 0.0, 0.0]}, "boundary": [[-6.0, 6.0, 0.0], [-6.0, -6.0, 0.0], [6.0, -6.0, 0.0], [6.0, 6.0, 0.0]], "holes": [[[-3.0, -3.0, 0.0], [-3.0, -1.0, 0.0], [-1.0, -1.0, 0.0], [-1.0, -3.0, 0.0]], [[1.0, 3.0, 0.0], [3.0, 3.0, 0.0], [3.0, 1.0, 0.0], [1.0, 1.0, 0.0]]]}, "face_type": "RoofCeiling", "boundary_condition": {"type": "Ground"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_0", "geometry": {"type": "Face3D", "plane": {"o": [-3.0, -3.0, 8.003790604710268], "n": [-1.0, 0.0, 0.0], "type": "Plane", "x": [0.0, 1.0, 0.0]}, "boundary": [[-3.0, -1.0, 8.003790604710268], [-3.0, -1.0, 0.0], [-3.0, -3.0, 0.0], [-3.0, -3.0, 8.003790604710268]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_1", "geometry": {"type": "Face3D", "plane": {"o": [-5.0, -1.0, 8.003790604710268], "n": [-0.0, 1.0, 0.0], "type": "Plane", "x": [1.0, 0.0, 0.0]}, "boundary": [[-1.0, -1.0, 8.003790604710268], [-1.0, -1.0, 0.0], [-3.0, -1.0, 0.0], [-3.0, -1.0, 8.003790604710268]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_2", "geometry": {"type": "Face3D", "plane": {"o": [-1.0, 3.0, 8.003790604710268], "n": [1.0, 0.0, 0.0], "type": "Plane", "x": [0.0, -1.0, 0.0]}, "boundary": [[-1.0, -3.0, 8.003790604710268], [-1.0, -3.0, 0.0], [-1.0, -1.0, 0.0], [-1.0, -1.0, 8.003790604710268]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_3", "geometry": {"type": "Face3D", "plane": {"o": [5.0, -3.0, 8.003790604710268], "n": [-0.0, -1.0, -0.0], "type": "Plane", "x": [-1.0, 0.0, 0.0]}, "boundary": [[-3.0, -3.0, 8.003790604710268], [-3.0, -3.0, 0.0], [-1.0, -3.0, 0.0], [-1.0, -3.0, 8.003790604710268]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_4", "geometry": {"type": "Face3D", "plane": {"o": [-3.0, -3.0, 8.003790604710268], "n": [-0.0, -0.0, 1.0], "type": "Plane", "x": [0.0, 1.0, 0.0]}, "boundary": [[-3.0, -1.0, 8.003790604710268], [-3.0, -3.0, 8.003790604710268], [-1.0, -3.0, 8.003790604710268], [-1.0, -1.0, 8.003790604710268]]}, "face_type": "RoofCeiling", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_18c07f39", "identifier": "Face_18c07f39_5", "geometry": {"type": "Face3D", "plane": {"o": [-3.0, -3.0, 0.0], "n": [0.0, 0.0, -1.0], "type": "Plane", "x": [-1.0, 0.0, 0.0]}, "boundary": [[-1.0, -1.0, 0.0], [-1.0, -3.0, 0.0], [-3.0, -3.0, 0.0], [-3.0, -1.0, 0.0]]}, "face_type": "Floor", "boundary_condition": {"type": "Ground"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_0", "geometry": {"type": "Face3D", "plane": {"o": [1.0, 1.0, 11.613166712310097], "n": [-1.0, 0.0, 0.0], "type": "Plane", "x": [0.0, 1.0, 0.0]}, "boundary": [[1.0, 3.0, 11.613166712310097], [1.0, 3.0, 0.0], [1.0, 1.0, 0.0], [1.0, 1.0, 11.613166712310097]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_1", "geometry": {"type": "Face3D", "plane": {"o": [-1.0, 3.0, 11.613166712310097], "n": [-0.0, 1.0, 0.0], "type": "Plane", "x": [1.0, 0.0, 0.0]}, "boundary": [[3.0, 3.0, 11.613166712310097], [3.0, 3.0, 0.0], [1.0, 3.0, 0.0], [1.0, 3.0, 11.613166712310097]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_2", "geometry": {"type": "Face3D", "plane": {"o": [3.0, 7.0, 11.613166712310097], "n": [1.0, 0.0, 0.0], "type": "Plane", "x": [0.0, -1.0, 0.0]}, "boundary": [[3.0, 1.0, 11.613166712310097], [3.0, 1.0, 0.0], [3.0, 3.0, 0.0], [3.0, 3.0, 11.613166712310097]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_3", "geometry": {"type": "Face3D", "plane": {"o": [9.0, 1.0, 11.613166712310097], "n": [-0.0, -1.0, -0.0], "type": "Plane", "x": [-1.0, 0.0, 0.0]}, "boundary": [[1.0, 1.0, 11.613166712310097], [1.0, 1.0, 0.0], [3.0, 1.0, 0.0], [3.0, 1.0, 11.613166712310097]]}, "face_type": "Wall", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_4", "geometry": {"type": "Face3D", "plane": {"o": [1.0, 1.0, 11.613166712310097], "n": [-0.0, -0.0, 1.0], "type": "Plane", "x": [0.0, 1.0, 0.0]}, "boundary": [[1.0, 3.0, 11.613166712310097], [1.0, 1.0, 11.613166712310097], [3.0, 1.0, 11.613166712310097], [3.0, 3.0, 11.613166712310097]]}, "face_type": "RoofCeiling", "boundary_condition": {"view_factor": {"type": "Autocalculate"}, "sun_exposure": true, "wind_exposure": true, "type": "Outdoors"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}, {"display_name": "Face_d56f9126", "identifier": "Face_d56f9126_5", "geometry": {"type": "Face3D", "plane": {"o": [1.0, 1.0, 0.0], "n": [0.0, 0.0, -1.0], "type": "Plane", "x": [-1.0, 0.0, 0.0]}, "boundary": [[3.0, 3.0, 0.0], [3.0, 1.0, 0.0], [1.0, 1.0, 0.0], [1.0, 3.0, 0.0]]}, "face_type": "Floor", "boundary_condition": {"type": "Ground"}, "type": "Face", "properties": {"radiance": {"type": "FaceRadiancePropertiesAbridged"}, "energy": {"type": "FaceEnergyPropertiesAbridged"}, "type": "FacePropertiesAbridged"}}], "type": "Model", "version": "1.50.4", "units": "Meters", "properties": {"radiance": {"modifier_sets": [], "modifiers": [], "type": "ModelRadianceProperties", "sensor_grids": [{"display_name": "pytest_SC", "identifier": "pytest_SC", "sensors": [{"pos": [-5.5, -5.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, -4.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, -3.5, 1.2000000476837158], "dir": [0.0, 0.0, 1.0]}, {"pos": [-5.5, -2.5, 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"solar_diffusing": false, "visible_transmittance": 0.70999999999999996, "infrared_transmittance": 0.0, "conductivity": 1.0, "solar_reflectance_back": 0.35999999999999999, "identifier": "Generic Low-e Glass", "solar_transmittance": 0.45000000000000001, "thickness": 0.0060000000000000001}, {"conductivity": 0.53000000000000003, "specific_heat": 840.0, "density": 1280.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.10000000000000001, "solar_absorptance": 0.80000000000000004, "visible_absorptance": 0.80000000000000004, "roughness": "MediumRough", "type": "EnergyMaterial", "identifier": "Generic LW Concrete"}, {"conductivity": 0.55600000000000005, "specific_heat": 1000.0, "density": 1.28, "thermal_absorptance": 0.90000000000000002, "thickness": 0.10000000000000001, "solar_absorptance": 0.69999999999999996, "visible_absorptance": 0.69999999999999996, "roughness": "Smooth", "type": "EnergyMaterial", "identifier": "Generic Ceiling Air Gap"}, {"conductivity": 0.059999999999999998, "specific_heat": 590.0, "density": 368.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.02, "solar_absorptance": 0.20000000000000001, "visible_absorptance": 0.20000000000000001, "roughness": "MediumSmooth", "type": "EnergyMaterial", "identifier": "Generic Acoustic Tile"}, {"conductivity": 1.95, "specific_heat": 900.0, "density": 2240.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.20000000000000001, "solar_absorptance": 0.80000000000000004, "visible_absorptance": 0.80000000000000004, "roughness": "MediumRough", "type": "EnergyMaterial", "identifier": "Generic HW Concrete"}, {"conductivity": 0.66700000000000004, "specific_heat": 1000.0, "density": 1.28, "thermal_absorptance": 0.90000000000000002, "thickness": 0.10000000000000001, "solar_absorptance": 0.69999999999999996, "visible_absorptance": 0.69999999999999996, "roughness": "Smooth", "type": "EnergyMaterial", "identifier": "Generic Wall Air Gap"}, {"conductivity": 0.16, "specific_heat": 1460.0, "density": 1120.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.01, "solar_absorptance": 0.65000000000000002, "visible_absorptance": 0.65000000000000002, "roughness": "MediumRough", "type": "EnergyMaterial", "identifier": "Generic Roof Membrane"}, {"conductivity": 0.16, "specific_heat": 1090.0, "density": 800.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.012699999999999999, "solar_absorptance": 0.5, "visible_absorptance": 0.5, "roughness": "MediumSmooth", "type": "EnergyMaterial", "identifier": "Generic Gypsum Board"}, {"gas_type": "Air", "thickness": 0.012699999999999999, "identifier": "Generic Window Air Gap", "type": "EnergyWindowMaterialGas"}, {"conductivity": 0.90000000000000002, "specific_heat": 790.0, "density": 1920.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.10000000000000001, "solar_absorptance": 0.65000000000000002, "visible_absorptance": 0.65000000000000002, "roughness": "MediumRough", "type": "EnergyMaterial", "identifier": "Generic Brick"}, {"conductivity": 0.029999999999999999, "specific_heat": 1210.0, "density": 43.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.050000000000000003, "solar_absorptance": 0.69999999999999996, "visible_absorptance": 0.69999999999999996, "roughness": "MediumRough", "type": "EnergyMaterial", "identifier": "Generic 25mm Insulation"}, {"visible_reflectance_back": 0.080000000000000002, "solar_reflectance": 0.070000000000000007, "visible_reflectance": 0.080000000000000002, "type": "EnergyWindowMaterialGlazing", "emissivity_back": 0.83999999999999997, "emissivity": 0.83999999999999997, "dirt_correction": 1.0, "solar_diffusing": false, "visible_transmittance": 0.88, "infrared_transmittance": 0.0, "conductivity": 1.0, "solar_reflectance_back": 0.070000000000000007, "identifier": "Generic Clear Glass", "solar_transmittance": 0.77000000000000002, "thickness": 0.0060000000000000001}, {"conductivity": 0.029999999999999999, "specific_heat": 1210.0, "density": 43.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.050000000000000003, "solar_absorptance": 0.69999999999999996, "visible_absorptance": 0.69999999999999996, "roughness": "MediumRough", "type": "EnergyMaterial", "identifier": "Generic 50mm Insulation"}, {"conductivity": 45.0, "specific_heat": 410.0, "density": 7690.0, "thermal_absorptance": 0.90000000000000002, "thickness": 0.0015, "solar_absorptance": 0.5, "visible_absorptance": 0.5, "roughness": "Smooth", "type": "EnergyMaterial", "identifier": "Generic Painted Metal"}], "air_boundary_construction": "Generic Air Boundary", "shade_construction": "Generic Shade", "roof_ceiling_set": {"ground_construction": "Generic Underground Roof", "interior_construction": "Generic Interior Ceiling", "exterior_construction": "Generic Roof", "type": "RoofCeilingConstructionSetAbridged"}, "wall_set": {"ground_construction": "Generic Underground Wall", "interior_construction": "Generic Interior Wall", "exterior_construction": "Generic Exterior Wall", "type": "WallConstructionSetAbridged"}, "door_set": {"overhead_construction": "Generic Exterior Door", "interior_glass_construction": "Generic Single Pane", "exterior_glass_construction": "Generic Double Pane", "type": "DoorConstructionSetAbridged", "interior_construction": "Generic Interior Door", "exterior_construction": "Generic Exterior Door"}, "floor_set": {"ground_construction": "Generic Ground Slab", "interior_construction": "Generic Interior Floor", "exterior_construction": "Generic Exposed Floor", "type": "FloorConstructionSetAbridged"}, "type": "GlobalConstructionSet", "aperture_set": {"skylight_construction": "Generic Double Pane", "type": "ApertureConstructionSetAbridged", "interior_construction": "Generic Single Pane", "operable_construction": "Generic Double Pane", "window_construction": "Generic Double Pane"}}, "schedules": [], "program_types": [], "construction_sets": []}, "type": "ModelProperties"}}
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/tests/conftest.py b/LadybugTools_Engine/Python/tests/conftest.py
index 4d59887f..44a4a9c7 100644
--- a/LadybugTools_Engine/Python/tests/conftest.py
+++ b/LadybugTools_Engine/Python/tests/conftest.py
@@ -1,4 +1,5 @@
 import shutil
+from ladybug.futil import nukedir
 
 from . import EXTERNAL_COMFORT_DIRECTORY, SPATIAL_COMFORT_DIRECTORY
 
@@ -7,7 +8,13 @@ def pytest_sessionstart(session):
     """_"""
     if SPATIAL_COMFORT_DIRECTORY.exists():
         print(f"Removing existing test files from {SPATIAL_COMFORT_DIRECTORY}")
-        shutil.rmtree(SPATIAL_COMFORT_DIRECTORY)
+        try:
+            shutil.rmtree(SPATIAL_COMFORT_DIRECTORY)
+        except PermissionError:
+            nukedir(SPATIAL_COMFORT_DIRECTORY, rmdir=True)
     if EXTERNAL_COMFORT_DIRECTORY.exists():
         print(f"Removing existing test files from {EXTERNAL_COMFORT_DIRECTORY}")
-        shutil.rmtree(EXTERNAL_COMFORT_DIRECTORY)
+        try:
+            shutil.rmtree(EXTERNAL_COMFORT_DIRECTORY)
+        except PermissionError:
+            nukedir(EXTERNAL_COMFORT_DIRECTORY, rmdir=True)
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/test_external_comfort.py b/LadybugTools_Engine/Python/tests/external_comfort/test_external_comfort.py
deleted file mode 100644
index 1d3d3a28..00000000
--- a/LadybugTools_Engine/Python/tests/external_comfort/test_external_comfort.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import pytest
-from ladybugtools_toolkit.external_comfort.external_comfort import (
-    ExternalComfort,
-    SimulationResult,
-)
-from ladybugtools_toolkit.external_comfort.material import Materials
-from ladybugtools_toolkit.external_comfort.shelter import Shelters
-from ladybugtools_toolkit.external_comfort.typology import Typologies, Typology
-
-from .. import EPW_FILE, EXTERNAL_COMFORT_IDENTIFIER
-
-GROUND_MATERIAL = Materials.LBT_AsphaltPavement.value
-SHADE_MATERIAL = Materials.FABRIC.value
-
-SIMULATION_RESULT = SimulationResult(
-    EPW_FILE,
-    GROUND_MATERIAL,
-    SHADE_MATERIAL,
-    EXTERNAL_COMFORT_IDENTIFIER,
-).run()
-TYPOLOGY = Typologies.EAST_SHELTER_WITH_CANOPY.value
-
-
-def test_external_comfort():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf, ExternalComfort)
-
-
-def test_external_comfort_array():
-    """_"""
-    typ = Typology(
-        Name="example",
-        Shelters=[Shelters.EAST.value],
-        EvaporativeCoolingEffect=np.where(
-            np.array(range(8760)) % 8 == 0, np.ones(8760) * 0.85, np.zeros(8760)
-        ),
-        WindSpeedMultiplier=np.where(
-            np.array(range(8760)) % 2 == 0, np.ones(8760) - 0.5, np.zeros(8760) + 1.5
-        ),
-        RadiantTemperatureAdjustment=np.where(
-            np.array(range(8760)) % 3 == 0, np.zeros(8760) - 1.2, np.zeros(8760)
-        ),
-    )
-    ext_comf = ExternalComfort(SimulationResult=SIMULATION_RESULT, Typology=typ)
-
-    assert (
-        pytest.approx(ext_comf.universal_thermal_climate_index.average, rel=0.1)
-        == 7.331275990178459
-    )
-
-
-def test_to_dataframe():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf.to_dataframe(), pd.DataFrame)
-
-
-def test_plot_utci_day_comfort_metrics():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf.plot_utci_day_comfort_metrics(), plt.Axes)
-    plt.close("all")
-
-
-def test_plot_utci_heatmap():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf.plot_utci_heatmap(), plt.Axes)
-    plt.close("all")
-
-
-def test_plot_utci_heatmap_histogram():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf.plot_utci_heatmap_histogram(), plt.Figure)
-    plt.close("all")
-
-
-def test_plot_utci_distance_to_comfortable():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf.plot_utci_distance_to_comfortable(), plt.Axes)
-    plt.close("all")
-
-
-def test_plot_dbt_heatmap():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf.plot_dbt_heatmap(), plt.Axes)
-    plt.close("all")
-
-
-def test_plot_rh_heatmap():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf.plot_rh_heatmap(), plt.Axes)
-    plt.close("all")
-
-
-def test_plot_ws_heatmap():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf.plot_ws_heatmap(), plt.Axes)
-    plt.close("all")
-
-
-def test_plot_mrt_heatmap():
-    """_"""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    assert isinstance(ext_comf.plot_mrt_heatmap(), plt.Axes)
-    plt.close("all")
-
-
-def test_to_dict():
-    """Test whether an object can be converted to a dictionary."""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    for obj in [ext_comf]:
-        obj_dict = obj.to_dict()
-        assert "_t" in obj_dict
-
-
-def test_to_json():
-    """Test whether an object can be converted to a json string."""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    for obj in [ext_comf]:
-        obj_json = obj.to_json()
-        assert '"_t":' in obj_json
-
-
-def test_from_dict_native():
-    """Test whether an object can be converted from a dictionary directly."""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    for obj in [ext_comf]:
-        new_obj = type(obj).from_dict(obj.to_dict())
-        assert isinstance(new_obj, type(obj))
-
-
-def test_from_json_native():
-    """Test whether an object can be converted from a json string directly."""
-    ext_comf = ExternalComfort(SIMULATION_RESULT, TYPOLOGY)
-    for obj in [ext_comf]:
-        new_obj = type(obj).from_json(obj.to_json())
-        assert isinstance(new_obj, type(obj))
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/test_material.py b/LadybugTools_Engine/Python/tests/external_comfort/test_material.py
deleted file mode 100644
index c1cd1c89..00000000
--- a/LadybugTools_Engine/Python/tests/external_comfort/test_material.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from ladybugtools_toolkit.external_comfort.material import (
-    EnergyMaterial,
-    EnergyMaterialVegetation,
-    Materials,
-    OpaqueMaterial,
-    OpaqueVegetationMaterial,
-    material_from_dict,
-)
-
-from .. import BASE_IDENTIFIER
-
-MATERIALS = [
-    OpaqueMaterial(BASE_IDENTIFIER, "source", 1, 1, 100, 100),
-    OpaqueVegetationMaterial(BASE_IDENTIFIER, "source", 1, 1, 100, 100),
-]
-
-
-def test_to_dict():
-    """Test whether an object can be converted to a dictionary."""
-    for obj in MATERIALS:
-        obj_dict = obj.to_dict()
-        assert "_t" in obj_dict.keys()
-
-
-def test_to_json():
-    """Test whether an object can be converted to a json string."""
-    for obj in MATERIALS:
-        obj_json = obj.to_json()
-        assert '"_t":' in obj_json
-
-
-def test_from_dict_native():
-    """Test whether an object can be converted from a dictionary directly."""
-    for obj in MATERIALS:
-        new_obj = type(obj).from_dict(obj.to_dict())
-        assert isinstance(new_obj, type(obj))
-
-
-def test_from_json_native():
-    """Test whether an object can be converted from a json string directly."""
-    for obj in MATERIALS:
-        new_obj = type(obj).from_json(obj.to_json())
-        assert isinstance(new_obj, type(obj))
-
-
-def test_to_lbt():
-    """_"""
-    for obj in MATERIALS:
-        assert isinstance(obj.to_lbt(), (EnergyMaterial, EnergyMaterialVegetation))
-
-
-def test_from_lbt():
-    """_"""
-    for obj in MATERIALS:
-        assert isinstance(
-            type(obj).from_lbt(obj.to_lbt()), (OpaqueMaterial, OpaqueVegetationMaterial)
-        )
-
-
-def test_material_from_dict():
-    """_"""
-    for obj in MATERIALS:
-        assert isinstance(
-            material_from_dict(obj.to_dict()),
-            (OpaqueMaterial, OpaqueVegetationMaterial),
-        )
-
-
-def test_materials():
-    """_"""
-    for material in Materials:
-        assert isinstance(material.value, (OpaqueMaterial, OpaqueVegetationMaterial))
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/test_model.py b/LadybugTools_Engine/Python/tests/external_comfort/test_model.py
deleted file mode 100644
index 18677bfa..00000000
--- a/LadybugTools_Engine/Python/tests/external_comfort/test_model.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import pytest
-from honeybee_energy.material.opaque import EnergyMaterial
-from ladybugtools_toolkit.external_comfort.material import Materials
-from ladybugtools_toolkit.external_comfort.model import (
-    _create_ground_zone,
-    _create_shade_valence,
-    _create_shade_zone,
-    create_model,
-    single_layer_construction,
-)
-
-from .. import BASE_IDENTIFIER
-
-GROUND_MATERIAL = Materials.LBT_AsphaltPavement.value
-SHADE_MATERIAL = Materials.FABRIC.value
-
-
-def test_single_layer_construction():
-    """_"""
-    material = EnergyMaterial(
-        identifier="TEST_MATERIAL",
-        thickness=1,
-        conductivity=1,
-        density=1,
-        specific_heat=500,
-    )
-    construction = single_layer_construction(material)
-    assert construction.identifier == "TEST_MATERIAL"
-    assert construction.materials[0].identifier == "TEST_MATERIAL"
-
-
-def test_create_ground_zone():
-    """_"""
-    assert (
-        _create_ground_zone(single_layer_construction(GROUND_MATERIAL.to_lbt())).volume
-        == 10 * 10 * 1
-    )
-
-
-def test_create_ground_zone_material():
-    """_"""
-    with pytest.raises(AssertionError):
-        _create_ground_zone("not_a_material")
-
-
-def test_create_shade_valence():
-    """_"""
-    assert (
-        _create_shade_valence(single_layer_construction(SHADE_MATERIAL.to_lbt()))[
-            0
-        ].area
-        == 10 * 3
-    )
-
-
-def test_create_shade_zone():
-    """_"""
-    assert (
-        _create_shade_zone(single_layer_construction(SHADE_MATERIAL.to_lbt())).volume
-        == 10 * 10 * 0.2
-    )
-
-
-def test_create_shade_zone_construction():
-    """_"""
-    with pytest.raises(AssertionError):
-        _create_shade_zone("not_a_construction")
-
-
-@pytest.mark.dependency()
-def test_create_model():
-    """_"""
-    model = create_model(
-        GROUND_MATERIAL.to_lbt(),
-        SHADE_MATERIAL.to_lbt(),
-        identifier=BASE_IDENTIFIER,
-    )
-    assert model.identifier == BASE_IDENTIFIER
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/test_shelter.py b/LadybugTools_Engine/Python/tests/external_comfort/test_shelter.py
deleted file mode 100644
index 6b523f11..00000000
--- a/LadybugTools_Engine/Python/tests/external_comfort/test_shelter.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from ladybug.epw import EPW
-from ladybugtools_toolkit.external_comfort.shelter import Point3D, Shelter
-from ladybugtools_toolkit.ladybug_extension.epw import sun_position_list
-
-from .. import EPW_FILE
-
-EPW_OBJ = EPW(EPW_FILE)
-GENERIC_SHELTER = Shelter(
-    [
-        Point3D(-10, -10, 5),
-        Point3D(-10, 10, 5),
-        Point3D(10, 10, 5),
-        Point3D(10, -10, 5),
-    ]
-)
-SUNS = sun_position_list(EPW_OBJ)
-
-
-def test_to_dict():
-    """Test whether an object can be converted to a dictionary."""
-    obj = GENERIC_SHELTER
-    obj_dict = obj.to_dict()
-    assert "_t" in obj_dict.keys()
-
-
-def test_to_json():
-    """Test whether an object can be converted to a json string."""
-    obj = GENERIC_SHELTER
-    obj_json = obj.to_json()
-    assert '"_t":' in obj_json
-
-
-def test_from_dict_native():
-    """Test whether an object can be converted from a dictionary directly."""
-    obj = GENERIC_SHELTER
-    obj_dict = obj.to_dict()
-    assert isinstance(Shelter.from_dict(obj_dict), Shelter)
-
-
-def test_from_json_native():
-    """Test whether an object can be converted from a json string directly."""
-    obj = GENERIC_SHELTER
-    obj_dict = obj.to_json()
-    assert isinstance(Shelter.from_json(obj_dict), Shelter)
-
-
-def test_set_porosity():
-    """Test whether porosity setting works."""
-    assert GENERIC_SHELTER.WindPorosity == 0
-    assert GENERIC_SHELTER.RadiationPorosity == 0
-    assert GENERIC_SHELTER.set_porosity(0.5).WindPorosity == 0.5
-    assert GENERIC_SHELTER.set_porosity(0.5).RadiationPorosity == 0.5
-
-
-def test_sky_exposure():
-    """Test amount of sky exposure."""
-    assert GENERIC_SHELTER.sky_exposure() == 0.31195840554592724
-    assert GENERIC_SHELTER.set_porosity(0.5).sky_exposure() == 0.6559792027729636
-
-
-def test_sun_exposure():
-    """Test sun exposure"""
-    assert sum(GENERIC_SHELTER.annual_sun_exposure(EPW_OBJ)) == 2041
-    assert sum(GENERIC_SHELTER.set_porosity(0.5).annual_sun_exposure(EPW_OBJ)) == 3253
-
-
-def test_wind_adjustment():
-    """Test wind speed adjustment"""
-    assert (
-        sum(GENERIC_SHELTER.annual_effective_wind_speed(EPW_OBJ)) == 12495.516000000243
-    )
-    assert (
-        sum(GENERIC_SHELTER.set_porosity(0.5).annual_effective_wind_speed(EPW_OBJ))
-        == 21015.186000001042
-    )
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/test_simulate.py b/LadybugTools_Engine/Python/tests/external_comfort/test_simulate.py
deleted file mode 100644
index bdfdba01..00000000
--- a/LadybugTools_Engine/Python/tests/external_comfort/test_simulate.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import pytest
-from ladybug.epw import EPW
-from ladybugtools_toolkit.external_comfort.material import Materials
-from ladybugtools_toolkit.external_comfort.model import create_model
-from ladybugtools_toolkit.external_comfort.simulate import (
-    SimulationResult,
-    radiant_temperature,
-    solar_radiation,
-    surface_temperature,
-)
-
-from .. import EPW_FILE, EXTERNAL_COMFORT_IDENTIFIER
-
-EPW_OBJ = EPW(EPW_FILE)
-GROUND_MATERIAL = Materials.LBT_AsphaltPavement.value
-SHADE_MATERIAL = Materials.FABRIC.value
-
-
-def test_solar_radiation():
-    """_"""
-    model = create_model(
-        ground_material=GROUND_MATERIAL.to_lbt(),
-        shade_material=SHADE_MATERIAL.to_lbt(),
-        identifier=EXTERNAL_COMFORT_IDENTIFIER,
-    )
-    result = solar_radiation(model, EPW_OBJ)
-    assert result["UnshadedUpTotalIrradiance"].average == pytest.approx(203, rel=1.5)
-
-
-def test_radiant_temperature():
-    """_"""
-    assert (
-        radiant_temperature(
-            [EPW_OBJ.dry_bulb_temperature * 0.5, EPW_OBJ.dry_bulb_temperature * 2],
-            view_factors=[0.25, 0.75],
-        ).average
-        == 16.924573102344482
-    )
-
-
-def test_surface_temperature():
-    """_"""
-    model = create_model(
-        ground_material=GROUND_MATERIAL.to_lbt(),
-        shade_material=SHADE_MATERIAL.to_lbt(),
-        identifier=EXTERNAL_COMFORT_IDENTIFIER,
-    )
-    result = surface_temperature(model, EPW_OBJ)
-    assert result["UnshadedUpTemperature"].average == EPW_OBJ.sky_temperature.average
-
-
-def test_simulation_result():
-    """_"""
-    res = SimulationResult(
-        EPW_FILE, GROUND_MATERIAL, SHADE_MATERIAL, EXTERNAL_COMFORT_IDENTIFIER
-    )
-    assert res.run().is_run()
-
-
-def test_to_dict():
-    """Test whether an object can be converted to a dictionary."""
-
-    res = SimulationResult(
-        EPW_FILE, GROUND_MATERIAL, SHADE_MATERIAL, EXTERNAL_COMFORT_IDENTIFIER
-    )
-    res_run = res.run()
-    for obj in [res, res_run]:
-        obj_dict = obj.to_dict()
-        assert "_t" in obj_dict
-
-
-def test_to_json():
-    """Test whether an object can be converted to a json string."""
-    res = SimulationResult(
-        EPW_FILE, GROUND_MATERIAL, SHADE_MATERIAL, EXTERNAL_COMFORT_IDENTIFIER
-    )
-    res_run = res.run()
-    for obj in [res, res_run]:
-        obj_json = obj.to_json()
-        assert '"_t":' in obj_json
-
-
-def test_from_dict_native():
-    """Test whether an object can be converted from a dictionary directly."""
-    res = SimulationResult(
-        EPW_FILE, GROUND_MATERIAL, SHADE_MATERIAL, EXTERNAL_COMFORT_IDENTIFIER
-    )
-    res_run = res.run()
-    for obj in [res, res_run]:
-        new_obj = type(obj).from_dict(obj.to_dict())
-        assert isinstance(new_obj, type(obj))
-
-
-def test_from_json_native():
-    """Test whether an object can be converted from a json string directly."""
-    res = SimulationResult(
-        EPW_FILE, GROUND_MATERIAL, SHADE_MATERIAL, EXTERNAL_COMFORT_IDENTIFIER
-    )
-    res_run = res.run()
-    for obj in [res, res_run]:
-        new_obj = type(obj).from_json(obj.to_json())
-        assert isinstance(new_obj, type(obj))
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/test_spatial_comfort.py b/LadybugTools_Engine/Python/tests/external_comfort/test_spatial_comfort.py
deleted file mode 100644
index 065998a1..00000000
--- a/LadybugTools_Engine/Python/tests/external_comfort/test_spatial_comfort.py
+++ /dev/null
@@ -1,150 +0,0 @@
-from __future__ import annotations
-
-import getpass
-import shutil
-from pathlib import Path
-
-import pandas as pd
-import pytest
-from honeybee.config import folders as hb_folders
-from honeybee.model import Model
-from ladybug.wea import AnalysisPeriod, Wea
-from ladybugtools_toolkit.external_comfort.external_comfort import \
-    SimulationResult
-from ladybugtools_toolkit.external_comfort.material import Materials
-from ladybugtools_toolkit.external_comfort.spatial.spatial_comfort import \
-    SpatialComfort
-from lbt_recipes.recipe import Recipe, RecipeSettings
-
-from .. import (CFD_DIRECTORY, EPW_FILE, EXTERNAL_COMFORT_IDENTIFIER,
-                MODEL_FILE, SPATIAL_COMFORT_DIRECTORY)
-
-SPATIAL_COMFORT_DIRECTORY.mkdir(parents=True, exist_ok=True)
-CFD_LOCAL_DIRECTORY = SPATIAL_COMFORT_DIRECTORY / "cfd"
-CFD_LOCAL_DIRECTORY.mkdir(parents=True, exist_ok=True)
-
-GROUND_MATERIAL = Materials.LBT_AsphaltPavement.value
-SHADE_MATERIAL = Materials.FABRIC.value
-
-SIMULATION_RESULT = SimulationResult(
-    EPW_FILE,
-    GROUND_MATERIAL,
-    SHADE_MATERIAL,
-    EXTERNAL_COMFORT_IDENTIFIER,
-).run()
-
-# copy cfd values into local directory for testing
-for file in list(CFD_DIRECTORY.glob("**/*")):
-    shutil.copy(file, CFD_LOCAL_DIRECTORY)
-
-SIMULATION_RESULT = SimulationResult(
-    EPW_FILE,
-    GROUND_MATERIAL,
-    SHADE_MATERIAL,
-    EXTERNAL_COMFORT_IDENTIFIER,
-).run()
-
-@pytest.mark.order(1)
-def test_run_spatial_annual_irradiance() -> Path:
-    """Run spatial annual-irradiance simulation and return working directory."""
-
-    # set simulation directory
-    hb_folders.default_simulation_folder = f"C:/Users/{getpass.getuser()}/simulation"
-
-    ill_file = (
-        SPATIAL_COMFORT_DIRECTORY
-        / "annual_irradiance"
-        / "results"
-        / "total"
-        / "pytest_SC.ill"
-    )
-
-    # load test model containing grids
-    model: Model = Model.from_hbjson(MODEL_FILE.as_posix())
-
-    # create wea object
-    wea = Wea.from_epw_file(EPW_FILE)
-
-    # run annual irradiance recipe
-    recipe = Recipe("annual-irradiance")
-    recipe.input_value_by_name("model", model)
-    recipe.input_value_by_name("wea", wea)
-    recipe.input_value_by_name("north", 0)
-    recipe.input_value_by_name("timestep", 1)
-    recipe.input_value_by_name("output-type", "solar")
-    recipe.input_value_by_name("grid-filter", "pytest")
-    recipe_settings = RecipeSettings()
-    _ = recipe.run(
-        settings=recipe_settings,
-        radiance_check=True,
-        queenbee_path=r"C:\Program Files\ladybug_tools\python\Scripts\queenbee.exe",
-    )
-
-    assert ill_file.exists()
-
-@pytest.mark.order(2)
-def test_run_spatial_sky_view() -> Path:
-    """Run spatial sky-view simulation and return working directory."""
-
-    # set simulation directory
-    hb_folders.default_simulation_folder = f"C:/Users/{getpass.getuser()}/simulation"
-
-    res_file_old = (
-        SPATIAL_COMFORT_DIRECTORY
-        / "sky_view"
-        / "results"
-        / "sky_view"
-        / "pytest_SC.res"
-    )
-    res_file_new = SPATIAL_COMFORT_DIRECTORY / "sky_view" / "results" / "pytest_SC.res"
-
-    # load test model containing grids
-    model: Model = Model.from_hbjson(MODEL_FILE.as_posix())
-
-    # run annual irradiance recipe
-    recipe = Recipe("sky-view")
-    recipe.input_value_by_name("model", model)
-    recipe.input_value_by_name("grid-filter", "pytest")
-    recipe_settings = RecipeSettings()
-    _ = recipe.run(
-        settings=recipe_settings,
-        radiance_check=True,
-        queenbee_path=r"C:\Program Files\ladybug_tools\python\Scripts\queenbee.exe",
-    )
-
-    assert res_file_old.exists() or res_file_new.exists()
-
-@pytest.mark.order(3)
-def test_spatial_comfort():
-    """_"""
-
-    assert isinstance(
-        SpatialComfort(SPATIAL_COMFORT_DIRECTORY, SIMULATION_RESULT), SpatialComfort
-    )
-
-@pytest.mark.order(4)
-def test_spatial_comfort_processing():
-    """_"""
-
-    spatial_comfort = SpatialComfort(SPATIAL_COMFORT_DIRECTORY, SIMULATION_RESULT)
-
-    # remove existing files
-    for fp in spatial_comfort.spatial_simulation_directory.glob("*.parquet"):
-        fp.unlink()
-
-    # with pytest.warns((pd.errors.PerformanceWarning)):
-    assert isinstance(
-        spatial_comfort.universal_thermal_climate_index_calculated, pd.DataFrame
-    )
-
-@pytest.mark.order(5)
-def test_spatial_comfort_summary():
-    """_"""
-
-    spatial_comfort = SpatialComfort(SPATIAL_COMFORT_DIRECTORY, SIMULATION_RESULT)
-
-    # remove analsyis periods fo testing
-    spatial_comfort.analysis_periods = [
-        AnalysisPeriod(st_month=3, end_month=3, st_day=21, end_day=21)
-    ]
-    spatial_comfort.run_all()
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/test_typology.py b/LadybugTools_Engine/Python/tests/external_comfort/test_typology.py
deleted file mode 100644
index 6d693407..00000000
--- a/LadybugTools_Engine/Python/tests/external_comfort/test_typology.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import numpy as np
-import pytest
-from ladybug.datacollection import HourlyContinuousCollection
-from ladybug.epw import EPW
-from ladybug_geometry.geometry3d.pointvector import Point3D
-from ladybugtools_toolkit.external_comfort.material import Materials
-from ladybugtools_toolkit.external_comfort.typology import (
-    Shelter,
-    SimulationResult,
-    Typologies,
-    Typology,
-)
-
-from .. import EPW_FILE, EXTERNAL_COMFORT_IDENTIFIER
-
-EPW_OBJ = EPW(EPW_FILE)
-GROUND_MATERIAL = Materials.LBT_AsphaltPavement.value
-SHADE_MATERIAL = Materials.FABRIC.value
-GENERIC_SHELTER = Shelter([Point3D(0, 0, 5), Point3D(1, 1, 5), Point3D(1, 0, 5)])
-
-SIMULATION_RESULT = SimulationResult(
-    EPW_FILE, GROUND_MATERIAL, SHADE_MATERIAL, EXTERNAL_COMFORT_IDENTIFIER
-).run()
-TYPOLOGY = Typology(
-    Name=EXTERNAL_COMFORT_IDENTIFIER,
-    Shelters=[GENERIC_SHELTER],
-    EvaporativeCoolingEffect=0.1,
-)
-
-
-def test_sky_exposure():
-    """_"""
-    assert TYPOLOGY.sky_exposure() == pytest.approx(0.9267766952966369, rel=0.1)
-
-
-def test_sun_exposure():
-    """_"""
-    x = np.array(TYPOLOGY.sun_exposure(EPW_OBJ))
-    assert x[~np.isnan(x)].sum() == 4465
-
-
-def test_dry_bulb_temperature():
-    """_"""
-    assert TYPOLOGY.dry_bulb_temperature(EPW_OBJ).average == pytest.approx(
-        10.036858349164525, rel=0.1
-    )
-
-
-def test_relative_humidity():
-    """_"""
-    assert TYPOLOGY.relative_humidity(EPW_OBJ).average == pytest.approx(
-        81.36280821917846, rel=0.1
-    )
-
-
-def test_wind_speed():
-    """_"""
-    assert TYPOLOGY.wind_speed(EPW_OBJ).average == pytest.approx(
-        3.2418835616436126, rel=0.1
-    )
-
-
-def test_mean_radiant_temperature():
-    """_"""
-    assert TYPOLOGY.mean_radiant_temperature(
-        SIMULATION_RESULT
-    ).average == pytest.approx(15.64262493993061, rel=0.5)
-
-
-def test_typologies():
-    """_"""
-    for typology in Typologies:
-        assert isinstance(typology.value, Typology)
-
-
-def test_universal_thermal_climate_index():
-    """_"""
-    assert isinstance(
-        TYPOLOGY.universal_thermal_climate_index(SIMULATION_RESULT),
-        HourlyContinuousCollection,
-    )
diff --git a/LadybugTools_Engine/Python/tests/ladybug_extension/__init__.py b/LadybugTools_Engine/Python/tests/ladybug_extension/__init__.py
deleted file mode 100644
index 5f282702..00000000
--- a/LadybugTools_Engine/Python/tests/ladybug_extension/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/LadybugTools_Engine/Python/tests/test_bhom/__init__.py b/LadybugTools_Engine/Python/tests/test_bhom/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/LadybugTools_Engine/Python/tests/test_bhom/test_to_bhom.py b/LadybugTools_Engine/Python/tests/test_bhom/test_to_bhom.py
new file mode 100644
index 00000000..369f70ad
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_bhom/test_to_bhom.py
@@ -0,0 +1,202 @@
+from ladybug.datacollection import HourlyContinuousCollection
+from ladybug.header import Header, DataTypeBase
+from ladybug.datatype.temperature import Temperature
+from ladybug.analysisperiod import AnalysisPeriod
+from ladybug_geometry.geometry3d.pointvector import Point3D
+
+from honeybee_energy.material.opaque import EnergyMaterial, EnergyMaterialVegetation
+
+from ladybugtools_toolkit.bhom.to_bhom import (
+    energymaterialvegetation_to_bhom,
+    energymaterial_to_bhom,
+    point3d_to_bhom,
+    analysisperiod_to_bhom,
+    hourlycontinuouscollection_to_bhom,
+    header_to_bhom,
+    datatype_to_bhom,
+    location_to_bhom,
+    epw_to_bhom,
+    material_to_bhom,
+)
+from .. import EPW_OBJ
+
+ENERGY_MATERIAL_VEGETATION = EnergyMaterialVegetation(
+    identifier="test",
+    thickness=0.1,
+    conductivity=0.2,
+    density=0.3,
+    specific_heat=1000,
+    roughness="Smooth",
+    soil_thermal_absorptance=0.5,
+    soil_solar_absorptance=0.6,
+    soil_visible_absorptance=0.7,
+    plant_height=0.8,
+    leaf_area_index=0.9,
+    leaf_reflectivity=0.1,
+    leaf_emissivity=0.8,
+    min_stomatal_resist=100,
+)
+
+ENERGY_MATERIAL = EnergyMaterial(
+    identifier="test",
+    roughness="Smooth",
+    thickness=1.0,
+    conductivity=0.5,
+    density=2.0,
+    specific_heat=1000.0,
+    thermal_absorptance=0.6,
+    solar_absorptance=0.7,
+    visible_absorptance=0.8,
+)
+
+
+def test_energymaterialvegetation_to_bhom():
+    """_"""
+
+    result = energymaterialvegetation_to_bhom(ENERGY_MATERIAL_VEGETATION)
+
+    assert result["_t"] == "BH.oM.LadybugTools.EnergyMaterialVegetation"
+    assert result["Type"] == "EnergyMaterialVegetation"
+    assert result["Identifier"] == "test"
+    assert result["Thickness"] == 0.1
+    assert result["Conductivity"] == 0.2
+    assert result["Density"] == 0.3
+    assert result["SpecificHeat"] == 1000
+    assert result["Roughness"] == "Smooth"
+    assert result["SoilThermalAbsorptance"] == 0.5
+    assert result["SoilSolarAbsorptance"] == 0.6
+    assert result["SoilVisibleAbsorptance"] == 0.7
+    assert result["PlantHeight"] == 0.8
+    assert result["LeafAreaIndex"] == 0.9
+    assert result["LeafReflectivity"] == 0.1
+    assert result["LeafEmissivity"] == 0.8
+    assert result["MinStomatalResist"] == 100
+
+
+def test_energymaterial_to_bhom():
+    """_"""
+
+    result = energymaterial_to_bhom(ENERGY_MATERIAL)
+
+    assert result["_t"] == "BH.oM.LadybugTools.EnergyMaterial"
+    assert result["Type"] == "EnergyMaterial"
+    assert result["Identifier"] == "test"
+    assert result["Roughness"] == "Smooth"
+    assert result["Thickness"] == 1.0
+    assert result["Conductivity"] == 0.5
+    assert result["Density"] == 2.0
+    assert result["SpecificHeat"] == 1000.0
+    assert result["ThermalAbsorptance"] == 0.6
+    assert result["SolarAbsorptance"] == 0.7
+    assert result["VisibleAbsorptance"] == 0.8
+
+
+def test_material_to_bhom():
+    """_"""
+
+    assert material_to_bhom(ENERGY_MATERIAL)["Roughness"] == "Smooth"
+    assert material_to_bhom(ENERGY_MATERIAL_VEGETATION)["MinStomatalResist"] == 100
+
+
+def test_point3d_to_bhom():
+    """_"""
+    point3d = Point3D(1.0, 2.0, 3.0)
+
+    result = point3d_to_bhom(point3d)
+
+    assert result["_t"] == "BH.oM.Geometry.Point"
+    assert result["X"] == 1.0
+    assert result["Y"] == 2.0
+    assert result["Z"] == 3.0
+
+
+def test_analysisperiod_to_bhom():
+    """Test analysisperiod_to_bhom function."""
+    obj = AnalysisPeriod(
+        st_month=1,
+        st_day=1,
+        end_month=12,
+        end_day=31,
+        st_hour=0,
+        end_hour=23,
+        timestep=1,
+        is_leap_year=False,
+    )
+
+    result = analysisperiod_to_bhom(obj)
+
+    assert result["_t"] == "BH.oM.LadybugTools.AnalysisPeriod"
+    assert result["Type"] == "AnalysisPeriod"
+    assert result["StHour"] == 0
+    assert result["EndHour"] == 23
+    assert result["StDay"] == 1
+    assert result["EndDay"] == 31
+    assert result["StMonth"] == 1
+    assert result["EndMonth"] == 12
+    assert result["IsLeapYear"] is False
+    assert result["Timestep"] == 1
+
+
+def test_datatype_to_bhom():
+    """Test datatype_to_bhom function."""
+    obj = Temperature()
+    result = datatype_to_bhom(obj)
+
+    assert result["_t"] == "BH.oM.LadybugTools.DataType"
+    assert result["Type"] == "DataType"
+    assert result["Name"] == "Temperature"
+    assert result["Data_Type"] == "Temperature"
+
+
+def test_header_to_bhom():
+    """Test header_to_bhom function."""
+    obj = Header(
+        data_type=Temperature(),
+        unit="C",
+        analysis_period=AnalysisPeriod(),
+        metadata={},
+    )
+
+    result = header_to_bhom(obj)
+
+    assert result["_t"] == "BH.oM.LadybugTools.Header"
+    assert result["Type"] == "Header"
+    assert result["Unit"] == "C"
+    assert result["Metadata"] == {}
+
+
+def test_hourlycontinuouscollection_to_bhom():
+    """Test conversion of HourlyContinuousCollection to BHOM dictionary."""
+    header = Header(
+        analysis_period=AnalysisPeriod(),
+        data_type=Temperature(),
+        unit="C",
+    )
+    values = [0.0] * 8760
+    obj = HourlyContinuousCollection(header=header, values=values)
+
+    result = hourlycontinuouscollection_to_bhom(obj)
+
+    assert result["_t"] == "BH.oM.LadybugTools.HourlyContinuousCollection"
+    assert result["Type"] == "HourlyContinuous"
+    assert sum(result["Values"]) == 0
+
+
+def test_location_to_bhom():
+    """_"""
+    result = location_to_bhom(EPW_OBJ.location)
+
+    assert len(result) == 11
+    assert result["_t"] == "BH.oM.LadybugTools.Location"
+    assert result["Type"] == "Location"
+    assert result["City"] == "LONDON GATWICK"
+
+
+def test_epw_to_bhom():
+    """_"""
+    result = epw_to_bhom(EPW_OBJ)
+
+    assert len(result) == 5
+    assert result["_t"] == "BH.oM.LadybugTools.EPW"
+    assert result["Type"] == "EPW"
+    assert len(result["DataCollections"]) == 34
diff --git a/LadybugTools_Engine/Python/tests/test_categorical.py b/LadybugTools_Engine/Python/tests/test_categorical.py
new file mode 100644
index 00000000..8a99c65b
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_categorical.py
@@ -0,0 +1,178 @@
+import pandas as pd
+from ladybugtools_toolkit.categorical.categories import (
+    Categorical,
+    UTCI_DEFAULT_CATEGORIES,
+    ComfortClass,
+    CategoricalComfort,
+)
+from matplotlib.legend import Legend
+import matplotlib.pyplot as plt
+import numpy as np
+import pytest
+from ladybugtools_toolkit.ladybug_extension.datacollection import collection_to_series
+
+from . import EPW_OBJ
+
+TEST_TIMESERIES_DATA = collection_to_series(EPW_OBJ.dry_bulb_temperature)
+
+TEST_CATEGORICAL_UNBOUNDED = Categorical(
+    bins=[-np.inf, 0, np.inf],
+    colors=["blue", "red"],
+)
+TEST_CATEGORICAL_BOUNDED = Categorical(
+    bins=[-100, 0, 100],
+    colors=["blue", "red"],
+)
+
+
+def test_categorical():
+    """_"""
+    assert isinstance(Categorical(bins=[0, 1, 2]), Categorical)
+
+    with pytest.raises(ValueError):
+        Categorical(bins=[0, 1, 2], colors=["blue", "red", "green"])
+
+    assert isinstance(repr(TEST_CATEGORICAL_UNBOUNDED), str)
+    assert isinstance(str(TEST_CATEGORICAL_UNBOUNDED), str)
+
+
+def test_generic_categorical_properties():
+    """_"""
+    assert TEST_CATEGORICAL_UNBOUNDED.name == "GenericCategories"
+    assert TEST_CATEGORICAL_UNBOUNDED.bin_names == ["(-inf, 0.0]", "(0.0, inf]"]
+    assert TEST_CATEGORICAL_UNBOUNDED.colors == ("#0000ffff", "#ff0000ff")
+    assert TEST_CATEGORICAL_UNBOUNDED.bins == [-np.inf, 0, np.inf]
+    assert TEST_CATEGORICAL_UNBOUNDED.bins_finite == (0,)
+    assert TEST_CATEGORICAL_UNBOUNDED.bin_names_detailed == [
+        "(-inf, 0.0] (-inf to 0.0)",
+        "(0.0, inf] (0.0 to inf)",
+    ]
+    assert TEST_CATEGORICAL_UNBOUNDED.descriptions == ["-inf to 0.0", "0.0 to inf"]
+    assert len(TEST_CATEGORICAL_BOUNDED.lb_colors) == 2
+
+
+def test_categorical_computed_properties():
+    """_"""
+    assert TEST_CATEGORICAL_UNBOUNDED.color_from_bin_name("(-inf, 0.0]") == "#0000ffff"
+
+    with pytest.raises(ValueError):
+        TEST_CATEGORICAL_UNBOUNDED.get_color(value=3)
+    assert TEST_CATEGORICAL_BOUNDED.get_color(value=3) == "#ff0000ff"
+    assert isinstance(
+        TEST_CATEGORICAL_BOUNDED.interval_from_bin_name("(0, 100]"), pd.Interval
+    )
+
+    assert isinstance(TEST_CATEGORICAL_BOUNDED.categorise([3]), pd.Categorical)
+    assert (
+        TEST_CATEGORICAL_BOUNDED.summarise([-5, -2, 0, 10])
+        == '"(-100, 0]" occurs 3 times (75.0%).\n"(0, 100]" occurs 1 times (25.0%).'
+    )
+    assert isinstance(TEST_CATEGORICAL_BOUNDED.value_counts([-5, -2, 0, 10]), pd.Series)
+
+    with pytest.raises(ValueError):
+        TEST_CATEGORICAL_BOUNDED.get_color(-1000)
+
+
+def test_categorical_comfort():
+    """_"""
+    assert len(UTCI_DEFAULT_CATEGORIES.comfort_classes) == 10
+    assert isinstance(UTCI_DEFAULT_CATEGORIES.comfort_classes[0], ComfortClass)
+    assert len(UTCI_DEFAULT_CATEGORIES.simplify().comfort_classes) == 3
+
+    with pytest.raises(ValueError):
+        CategoricalComfort(
+            bins=(-np.inf, 9, 26, np.inf),
+            bin_names=("Too cold", "Comfortable", "Too hot"),
+            colors=("blue", "yellow", "red"),
+            name="example",
+            comfort_classes=[
+                ComfortClass.TOO_COLD,
+                ComfortClass.TOO_HOT,
+            ],
+        )
+        CategoricalComfort(
+            bins=(-np.inf, 9, 26, np.inf),
+            bin_names=("Too cold", "Comfortable", "Too hot"),
+            colors=("blue", "yellow", "red"),
+            name="example",
+        )
+        CategoricalComfort(
+            bins=(-np.inf, 9, 26, np.inf),
+            bin_names=("Too cold", "Comfortable", "Too hot"),
+            colors=("blue", "yellow", "red"),
+            name="example",
+            comfort_classes=[
+                ComfortClass.TOO_COLD,
+                ComfortClass.TOO_COLD,
+                ComfortClass.TOO_HOT,
+            ],
+        )
+
+
+def test_from():
+    """_"""
+    assert isinstance(
+        Categorical.from_cmap(bins=[0, 1, 2], cmap=plt.get_cmap("inferno")), Categorical
+    )
+    assert isinstance(
+        Categorical.from_cmap(bins=[-np.inf, 1, 2], cmap=plt.get_cmap("inferno")),
+        Categorical,
+    )
+    assert isinstance(
+        Categorical.from_cmap(bins=[0, 1, np.inf], cmap=plt.get_cmap("inferno")),
+        Categorical,
+    )
+
+
+def test_legend():
+    """_"""
+    assert isinstance(TEST_CATEGORICAL_UNBOUNDED.create_legend(), Legend)
+    plt.close("all")
+
+
+def test_timeseries_summary_monthly():
+    """_"""
+    s = TEST_CATEGORICAL_BOUNDED.timeseries_summary_monthly(TEST_TIMESERIES_DATA)
+    assert isinstance(s, pd.DataFrame)
+    assert s.shape == (12, 2)
+    assert s.sum().sum() == 8760
+    assert s.quantile(0.8).mean() == pytest.approx(392.7, abs=0.01)
+
+    with pytest.raises(ValueError):
+        TEST_CATEGORICAL_BOUNDED.timeseries_summary_monthly("not_a_timeseries_data")
+        TEST_CATEGORICAL_BOUNDED.timeseries_summary_monthly(
+            TEST_TIMESERIES_DATA.reset_index(drop=True)
+        )
+
+
+def test_annual_heatmap():
+    """_"""
+    assert isinstance(
+        TEST_CATEGORICAL_BOUNDED.annual_heatmap(TEST_TIMESERIES_DATA), plt.Axes
+    )
+    plt.close("all")
+
+
+def test_annual_monthly_histogram():
+    """_"""
+    assert isinstance(
+        TEST_CATEGORICAL_BOUNDED.annual_monthly_histogram(TEST_TIMESERIES_DATA),
+        plt.Axes,
+    )
+    plt.close("all")
+
+    assert isinstance(
+        TEST_CATEGORICAL_BOUNDED.annual_monthly_histogram(
+            TEST_TIMESERIES_DATA, show_labels=True
+        ),
+        plt.Axes,
+    )
+    plt.close("all")
+
+    assert isinstance(
+        TEST_CATEGORICAL_BOUNDED.annual_monthly_histogram(
+            TEST_TIMESERIES_DATA, show_legend=True
+        ),
+        plt.Axes,
+    )
+    plt.close("all")
diff --git a/LadybugTools_Engine/Python/tests/test_external_comfort/__init__.py b/LadybugTools_Engine/Python/tests/test_external_comfort/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/LadybugTools_Engine/Python/tests/test_external_comfort/test_externalcomfort.py b/LadybugTools_Engine/Python/tests/test_external_comfort/test_externalcomfort.py
new file mode 100644
index 00000000..03cd052d
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_external_comfort/test_externalcomfort.py
@@ -0,0 +1,124 @@
+from pathlib import Path
+from tempfile import gettempdir
+import matplotlib.pyplot as plt
+import pandas as pd
+import pytest
+from ladybugtools_toolkit.external_comfort.externalcomfort import ExternalComfort
+from ladybugtools_toolkit.external_comfort.typology import Typologies
+
+from .test_simulate import TEST_SIMULATION_RESULT
+from .test_typology import TEST_TYPOLOGY
+
+TEST_EXTERNAL_COMFORT = ExternalComfort(
+    simulation_result=TEST_SIMULATION_RESULT, typology=TEST_TYPOLOGY
+)
+
+
+def test_externalcomfort():
+    """_"""
+    assert isinstance(TEST_EXTERNAL_COMFORT, ExternalComfort)
+
+    assert isinstance(
+        ExternalComfort(
+            simulation_result=TEST_SIMULATION_RESULT, typology=Typologies.OPENFIELD
+        ),
+        ExternalComfort,
+    )
+
+    with pytest.raises(ValueError):
+        ExternalComfort(
+            simulation_result="not_a_simulation_result", typology=TEST_TYPOLOGY
+        )
+        ExternalComfort(
+            simulation_result=TEST_SIMULATION_RESULT, typology="not_a_typology"
+        )
+        ExternalComfort(
+            simulation_result=TEST_SIMULATION_RESULT,
+            typology=TEST_TYPOLOGY,
+            dry_bulb_temperature="not_a_collection",
+        )
+
+    assert isinstance(repr(TEST_EXTERNAL_COMFORT), str)
+
+
+def test_round_trip():
+    """_"""
+    tempfile = Path(gettempdir()) / "pytest_external_comfort.json"
+    ExternalComfort.from_dict(TEST_EXTERNAL_COMFORT.to_dict())
+    ExternalComfort.from_json(TEST_EXTERNAL_COMFORT.to_json())
+    ExternalComfort.from_file(TEST_EXTERNAL_COMFORT.to_file(tempfile))
+
+    tempfile.unlink()
+
+    with pytest.raises(ValueError):
+        TEST_EXTERNAL_COMFORT.to_file("./not_a_json_file.txt")
+
+
+def test_to_dataframe():
+    """_"""
+    assert isinstance(TEST_EXTERNAL_COMFORT.to_dataframe(), pd.DataFrame)
+
+
+def test_plot_utci_day_comfort_metrics():
+    """_"""
+
+    assert isinstance(TEST_EXTERNAL_COMFORT.plot_utci_day_comfort_metrics(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_utci_heatmap():
+    """_"""
+
+    assert isinstance(TEST_EXTERNAL_COMFORT.plot_utci_heatmap(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_utci_heatmap_histogram():
+    """_"""
+
+    assert isinstance(TEST_EXTERNAL_COMFORT.plot_utci_heatmap_histogram(), plt.Figure)
+    plt.close("all")
+
+
+def test_plot_utci_histogram():
+    """_"""
+
+    assert isinstance(TEST_EXTERNAL_COMFORT.plot_utci_histogram(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_utci_distance_to_comfortable():
+    """_"""
+
+    assert isinstance(
+        TEST_EXTERNAL_COMFORT.plot_utci_distance_to_comfortable(), plt.Axes
+    )
+    plt.close("all")
+
+
+def test_plot_dbt_heatmap():
+    """_"""
+
+    assert isinstance(TEST_EXTERNAL_COMFORT.plot_dbt_heatmap(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_rh_heatmap():
+    """_"""
+
+    assert isinstance(TEST_EXTERNAL_COMFORT.plot_rh_heatmap(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_ws_heatmap():
+    """_"""
+
+    assert isinstance(TEST_EXTERNAL_COMFORT.plot_ws_heatmap(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_mrt_heatmap():
+    """_"""
+
+    assert isinstance(TEST_EXTERNAL_COMFORT.plot_mrt_heatmap(), plt.Axes)
+    plt.close("all")
diff --git a/LadybugTools_Engine/Python/tests/test_external_comfort/test_material.py b/LadybugTools_Engine/Python/tests/test_external_comfort/test_material.py
new file mode 100644
index 00000000..36733a25
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_external_comfort/test_material.py
@@ -0,0 +1,69 @@
+"""Test functions for the "material" module."""
+
+import numpy as np
+import pytest
+from ladybugtools_toolkit.external_comfort.material import (
+    _custom_materials,
+    _ice_tool_materials,
+    _lbt_materials,
+    get_material,
+    materials,
+)
+
+TEST_GROUND_MATERIAL = get_material("Concrete Pavement")
+TEST_SHADE_MATERIAL = get_material("Fabric")
+
+
+def test_lbt_materials():
+    """_"""
+    _materials = _lbt_materials()
+    assert len(_materials) == 114
+    assert _materials[0].identifier == "Generic Roof Membrane"
+    assert _materials[-1].identifier == "Grassy Lawn"
+    assert np.array([i.conductivity for i in _materials]).mean() == pytest.approx(
+        4.06154874650346
+    )
+
+
+def test_ice_tool_materials():
+    """_"""
+    _materials = _ice_tool_materials()
+    assert len(_materials) == 39
+    assert _materials[0].identifier == "SD1 - Quartzite (Beige/brown/black New/Rough)"
+    assert _materials[-1].identifier == "WT1 - Water small (- -)"
+    assert np.array([i.conductivity for i in _materials]).mean() == pytest.approx(
+        1.498974358974359
+    )
+
+
+def test_custom_materials():
+    """_"""
+    _materials = _custom_materials()
+    assert len(_materials) == 3
+    assert _materials[0].identifier == "Fabric"
+    assert _materials[-1].identifier == "Travertine"
+    assert np.array([i.conductivity for i in _materials]).mean() == pytest.approx(
+        1.2033333333333334
+    )
+
+
+def test_materials():
+    """_"""
+    _materials = materials()
+    assert len(_materials) == 154
+    assert _materials[0].identifier == "Generic Roof Membrane"
+    assert _materials[-1].identifier == "WT1 - Water small (- -)"
+    assert np.array([i.conductivity for i in _materials]).mean() == pytest.approx(
+        3.4017114097493146
+    )
+
+
+def test_get_material():
+    """_"""
+    with pytest.raises(KeyError):
+        assert "Did you mean " in get_material("Dry Grass")
+
+    assert get_material("Dry Sand").identifier == "Dry Sand"
+    assert get_material("M01 100mm brick").specific_heat == pytest.approx(
+        789.4906206515565
+    )
diff --git a/LadybugTools_Engine/Python/tests/test_external_comfort/test_model.py b/LadybugTools_Engine/Python/tests/test_external_comfort/test_model.py
new file mode 100644
index 00000000..3a60830e
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_external_comfort/test_model.py
@@ -0,0 +1,173 @@
+"""Test functions for the "model" module."""
+
+import pytest
+from honeybee.model import Model
+from honeybee_energy.construction.opaque import OpaqueConstruction
+from honeybee_energy.construction.shade import ShadeConstruction
+from ladybugtools_toolkit.external_comfort.model import (
+    _GROUND_THICKNESS,
+    _SHADE_HEIGHT_ABOVE_GROUND,
+    _SHADE_THICKNESS,
+    _ZONE_DEPTH,
+    _ZONE_WIDTH,
+    _ground_zone,
+    _shade_valence,
+    _shade_zone,
+    create_model,
+    get_ground_material,
+    get_ground_reflectance,
+    get_shade_material,
+    model_equality,
+    opaque_to_shade,
+    single_layer_construction,
+)
+
+from .test_material import TEST_GROUND_MATERIAL, TEST_SHADE_MATERIAL
+
+
+def test_single_layer_construction():
+    """_"""
+    construction = single_layer_construction(TEST_SHADE_MATERIAL)
+    assert isinstance(construction, OpaqueConstruction)
+    assert construction.identifier == TEST_SHADE_MATERIAL.identifier
+    assert construction.materials[0].identifier == TEST_SHADE_MATERIAL.identifier
+
+    with pytest.raises(TypeError):
+        single_layer_construction("not_a_material")
+
+
+def test_opaque_to_shade():
+    """_"""
+    opaque = single_layer_construction(TEST_GROUND_MATERIAL)
+    shade = opaque_to_shade(opaque)
+    assert isinstance(shade, ShadeConstruction)
+    assert shade.identifier == f"{TEST_GROUND_MATERIAL.identifier}_shade"
+    assert shade.solar_reflectance == opaque.outside_solar_reflectance
+
+
+def test_ground_zone():
+    """_"""
+    assert (
+        _ground_zone(single_layer_construction(TEST_GROUND_MATERIAL)).volume
+        == _GROUND_THICKNESS * _ZONE_DEPTH * _ZONE_WIDTH
+    )
+
+
+def test_ground_zone_bad():
+    """_"""
+    with pytest.raises((AssertionError, TypeError)):
+        _ground_zone("not_a_construction")
+
+
+def test_shade_valence():
+    """_"""
+    assert (
+        _shade_valence(single_layer_construction(TEST_SHADE_MATERIAL))[0].area
+        == _ZONE_DEPTH * _SHADE_HEIGHT_ABOVE_GROUND
+    )
+
+
+def test_shade_valence_bad():
+    with pytest.raises((AssertionError, TypeError)):
+        _shade_valence("not_a_construction")
+
+
+def test_shade_zone():
+    """_"""
+    assert (
+        _shade_zone(single_layer_construction(TEST_SHADE_MATERIAL)).volume
+        == _ZONE_DEPTH * _ZONE_WIDTH * _SHADE_THICKNESS
+    )
+
+
+def test_shade_zone_bad():
+    with pytest.raises((AssertionError, TypeError)):
+        _shade_zone("not_a_construction")
+
+
+def test_create_model():
+    """_"""
+    default_model = create_model(
+        identifier="test_name",
+        ground_material=TEST_GROUND_MATERIAL,
+        shade_material=TEST_SHADE_MATERIAL,
+    )
+    assert isinstance(default_model, Model)
+    assert default_model.identifier == "test_name"
+
+
+def test_get_ground_material():
+    """_"""
+    model = create_model(
+        identifier="test_name",
+        ground_material=TEST_GROUND_MATERIAL,
+        shade_material=TEST_SHADE_MATERIAL,
+    )
+    assert get_ground_material(model) == TEST_GROUND_MATERIAL
+
+    with pytest.raises(TypeError):
+        get_ground_material("not_a_model")
+
+
+def test_get_ground_reflectance():
+    """_"""
+    model = create_model(
+        identifier="test_name",
+        ground_material=TEST_GROUND_MATERIAL,
+        shade_material=TEST_SHADE_MATERIAL,
+    )
+    assert get_ground_reflectance(model) == TEST_GROUND_MATERIAL.solar_reflectance
+
+    with pytest.raises(TypeError):
+        get_ground_reflectance("not_a_model")
+
+
+def test_get_shade_material():
+    """_"""
+    model = create_model(
+        identifier="test_name",
+        ground_material=TEST_GROUND_MATERIAL,
+        shade_material=TEST_SHADE_MATERIAL,
+    )
+    assert get_shade_material(model) == TEST_SHADE_MATERIAL
+
+    with pytest.raises(TypeError):
+        get_shade_material("not_a_model")
+
+
+def test_model_equality():
+    """_"""
+    model_1 = create_model(
+        identifier="test_name",
+        ground_material=TEST_GROUND_MATERIAL,
+        shade_material=TEST_SHADE_MATERIAL,
+    )
+    model_2 = create_model(
+        identifier="other_name",
+        ground_material=TEST_GROUND_MATERIAL,
+        shade_material=TEST_SHADE_MATERIAL,
+    )
+    model_3 = create_model(
+        identifier="test_name",
+        ground_material=TEST_GROUND_MATERIAL,
+        shade_material=TEST_GROUND_MATERIAL,
+    )
+    model_4 = create_model(
+        identifier="test_name",
+        ground_material=TEST_SHADE_MATERIAL,
+        shade_material=TEST_SHADE_MATERIAL,
+    )
+    model_5 = create_model(
+        identifier="test_name",
+        ground_material=TEST_SHADE_MATERIAL,
+        shade_material=TEST_GROUND_MATERIAL,
+    )
+
+    with pytest.raises(TypeError):
+        model_equality("not_a_model", model_1)
+
+    assert model_equality(model_1, model_1)  # equal
+    assert not model_equality(model_1, model_2)  # identifier different
+    assert not model_equality(model_1, model_4)  # ground material different
+    assert not model_equality(model_1, model_3)  # shade material different
+    assert not model_equality(model_1, model_5)  # both materials different
diff --git a/LadybugTools_Engine/Python/tests/test_external_comfort/test_moisture.py b/LadybugTools_Engine/Python/tests/test_external_comfort/test_moisture.py
new file mode 100644
index 00000000..0e0f0718
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_external_comfort/test_moisture.py
@@ -0,0 +1,157 @@
+from pathlib import Path
+from tempfile import gettempdir
+import pytest
+
+import numpy as np
+
+from ladybugtools_toolkit.external_comfort.spatial.moisture import (
+    MoistureSource,
+    DecayMethod,
+    Point2D,
+    spatial_evaporative_cooling_effect,
+)
+from .. import EPW_OBJ
+
+EVAP_CLG_EFFECT_0 = np.where(np.arange(8760) % 2 == 0, 0.1, 0.9)
+EVAP_CLG_EFFECT_1 = np.where(np.arange(8760) % 5 == 0, 0.9, 0.0)
+
+n = 101
+x = np.linspace(-50, 50, 11)
+y = np.linspace(-50, 50, 11)
+xx, yy = np.meshgrid(x, y)
+POINTS = np.concatenate(np.stack([xx, yy], axis=2))
+SOURCE = POINTS[60]
+
+
+def test_round_trip():
+    """_"""
+    test_moisture_source = MoistureSource(
+        identifier="example",
+        evaporative_cooling_effect=EVAP_CLG_EFFECT_0,
+        decay_method=DecayMethod.LINEAR,
+        max_decay_distance=25,
+        plume_angle=35,
+    )
+
+    tempfile = Path(gettempdir()) / "pytest_moisture_source.json"
+    MoistureSource.from_dict(test_moisture_source.to_dict())
+    MoistureSource.from_json(test_moisture_source.to_json())
+    MoistureSource.from_file(test_moisture_source.to_file(tempfile))
+
+    tempfile.unlink()
+
+
+def test_moisture_source_bad():
+    """_"""
+
+    # non 8760 length evap clg effect
+    with pytest.raises(ValueError):
+        MoistureSource(
+            identifier="example",
+            evaporative_cooling_effect=[1],
+            decay_method=DecayMethod.LINEAR,
+            max_decay_distance=25,
+            plume_angle=35,
+        )
+
+    # non numeric evap clg effect
+    with pytest.raises(ValueError):
+        MoistureSource(
+            identifier="example",
+            evaporative_cooling_effect=["hi"] * 8760,
+            decay_method=DecayMethod.LINEAR,
+            max_decay_distance=25,
+            plume_angle=35,
+        )
+
+    # negative evap clg effect
+    with pytest.raises(ValueError):
+        MoistureSource(
+            identifier="example",
+            evaporative_cooling_effect=[-1] * 8760,
+            decay_method=DecayMethod.LINEAR,
+            max_decay_distance=25,
+            plume_angle=35,
+        )
+
+    # >1 evap clg effect
+    with pytest.raises(ValueError):
+        MoistureSource(
+            identifier="example",
+            evaporative_cooling_effect=[1.1] * 8760,
+            decay_method=DecayMethod.LINEAR,
+            max_decay_distance=25,
+            plume_angle=35,
+        )
+
+    # bad decay method
+    with pytest.raises(ValueError):
+        MoistureSource(
+            identifier="example",
+            evaporative_cooling_effect=[1.1] * 8760,
+            decay_method="not a decay method",
+            max_decay_distance=25,
+            plume_angle=35,
+        )
+
+    # bad max_decay_distance
+    with pytest.raises(ValueError):
+        MoistureSource(
+            identifier="example",
+            evaporative_cooling_effect=[1.1] * 8760,
+            decay_method=DecayMethod.LINEAR,
+            max_decay_distance=0,
+            plume_angle=35,
+        )
+        MoistureSource(
+            identifier="example",
+            evaporative_cooling_effect=[1.1] * 8760,
+            decay_method=DecayMethod.LINEAR,
+            max_decay_distance="yo",
+            plume_angle=35,
+        )
+
+
+def test_spatial_evaporative_cooling_effect():
+    """_"""
+    test_moisture_source = MoistureSource(
+        identifier="example",
+        evaporative_cooling_effect=EVAP_CLG_EFFECT_0,
+        decay_method=DecayMethod.LINEAR,
+        max_decay_distance=25,
+        plume_angle=35,
+    )
+    df = test_moisture_source.spatial_evaporative_cooling_effect(
+        epw=EPW_OBJ, source=SOURCE, points=POINTS
+    )
+    assert df.values.shape == (8760, 121)
+    assert df.values.sum() == pytest.approx(4090.620731753589, rel=0.001)
+    assert df.values.mean() == pytest.approx(0.003859221793042746, rel=0.0001)
+
+
+def test_composite_spatial_evaporative_cooling_effect():
+    """_"""
+    test_moisture_source_0 = MoistureSource(
+        identifier="example_0",
+        evaporative_cooling_effect=EVAP_CLG_EFFECT_0,
+        decay_method=DecayMethod.LINEAR,
+        max_decay_distance=25,
+        plume_angle=35,
+    )
+    test_moisture_source_1 = MoistureSource(
+        identifier="example_1",
+        evaporative_cooling_effect=EVAP_CLG_EFFECT_1,
+        decay_method=DecayMethod.PARABOLIC,
+        max_decay_distance=35,
+        plume_angle=25,
+    )
+
+    df = spatial_evaporative_cooling_effect(
+        moisture_sources=[test_moisture_source_0, test_moisture_source_1],
+        epw=EPW_OBJ,
+        points=POINTS,
+        sources=[Point2D(15, 15), Point2D(0, 0)],
+    )
+    assert df.values.shape == (8760, 121)
+    assert df.values.sum() == pytest.approx(1610.3179, rel=0.001)
+    assert df.values.mean() == pytest.approx(0.0015192252, rel=0.0001)
diff --git a/LadybugTools_Engine/Python/tests/test_external_comfort/test_shelter.py b/LadybugTools_Engine/Python/tests/test_external_comfort/test_shelter.py
new file mode 100644
index 00000000..f56215fd
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_external_comfort/test_shelter.py
@@ -0,0 +1,106 @@
+from pathlib import Path
+from tempfile import gettempdir
+import numpy as np
+import pytest
+from ladybug.epw import EPW
+from ladybugtools_toolkit.external_comfort._shelterbase import (
+    Point3D,
+    Shelter,
+    annual_sky_exposure,
+    annual_sun_exposure,
+    annual_wind_speed,
+    write_shelters_to_hbjson,
+)
+
+
+from ladybugtools_toolkit.external_comfort.shelter import TreeShelter
+from ladybugtools_toolkit.ladybug_extension.epw import sun_position_list
+
+from .. import EPW_FILE
+
+EPW_OBJ = EPW(EPW_FILE)
+TEST_SHELTER = Shelter(
+    vertices=[
+        Point3D(-10, -10, 5),
+        Point3D(-10, 10, 5),
+        Point3D(10, 10, 5),
+        Point3D(10, -10, 5),
+    ]
+)
+SUNS = sun_position_list(EPW_OBJ)
+
+
+def test_tree_species():
+    """_"""
+    for species in TreeShelter:
+        assert isinstance(species.shelter(), Shelter)
+
+    assert isinstance(TreeShelter.ACER_PLATANOIDES.shelter(), Shelter)
+    assert isinstance(
+        TreeShelter.ACER_PLATANOIDES.shelter(northern_hemisphere=False), Shelter
+    )
+
+
+def test_round_trip():
+    """_"""
+    tempfile = Path(gettempdir()) / "pytest_shelter.json"
+    Shelter.from_dict(TEST_SHELTER.to_dict())
+    Shelter.from_json(TEST_SHELTER.to_json())
+    Shelter.from_file(TEST_SHELTER.to_file(tempfile))
+
+    tempfile.unlink()
+
+
+def test_set_porosity():
+    """Test whether porosity setting works."""
+    assert list(TEST_SHELTER.wind_porosity) == [0] * 8760
+    assert list(TEST_SHELTER.radiation_porosity) == [0] * 8760
+    assert list(TEST_SHELTER.set_porosity([0.5] * 8760).wind_porosity) == [0.5] * 8760
+    assert (
+        list(TEST_SHELTER.set_porosity([0.5] * 8760).radiation_porosity) == [0.5] * 8760
+    )
+
+
+def test_sky_exposure():
+    """Test amount of sky exposure."""
+    assert sum(TEST_SHELTER.annual_sky_exposure()) == pytest.approx(2732.7556325820833)
+
+
+def test_sun_exposure():
+    """Test sun exposure"""
+    assert np.nansum(TEST_SHELTER.annual_sun_exposure(EPW_OBJ)) == 2041
+
+
+def test_wind_adjustment():
+    """Test wind speed adjustment"""
+    assert sum(TEST_SHELTER.annual_wind_speed(EPW_OBJ)) == pytest.approx(
+        28398.899999998048
+    )
+
+
+def test_annual_sky_exposure():
+    """_"""
+    assert sum(annual_sky_exposure([TEST_SHELTER])) == pytest.approx(2732.7556325820833)
+
+
+def test_annual_wind_speed():
+    """_"""
+    assert sum(annual_wind_speed([TEST_SHELTER], EPW_OBJ)) == pytest.approx(
+        28398.899999998048
+    )
+
+
+def test_annual_sun_exposure():
+    """_"""
+    assert np.nansum(annual_sun_exposure([TEST_SHELTER], EPW_OBJ)) == pytest.approx(
+        2041
+    )
+
+
+def test_write_shelters_to_hbjson():
+    """_"""
+    tempfile = Path(gettempdir()) / "pytest_shelter.hbjson"
+    write_shelters_to_hbjson([TEST_SHELTER], tempfile)
+    assert tempfile.exists()
+
+    tempfile.unlink()
diff --git a/LadybugTools_Engine/Python/tests/test_external_comfort/test_simulate.py b/LadybugTools_Engine/Python/tests/test_external_comfort/test_simulate.py
new file mode 100644
index 00000000..f99e16ed
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_external_comfort/test_simulate.py
@@ -0,0 +1,163 @@
+from pathlib import Path
+from tempfile import gettempdir
+from ladybug.epw import HourlyContinuousCollection
+from ladybugtools_toolkit.external_comfort._simulatebase import (
+    SimulationResult,
+    radiant_temperature,
+    simulate_surface_temperatures,
+    simulation_directory,
+    simulation_id,
+)
+
+from ladybugtools_toolkit.external_comfort.model import create_model
+import pytest
+
+from .. import EPW_FILE, EPW_OBJ, EXTERNAL_COMFORT_IDENTIFIER
+from .test_material import TEST_GROUND_MATERIAL, TEST_SHADE_MATERIAL
+
+TEST_SIMULATION_RESULT = SimulationResult(
+    epw_file=EPW_FILE,
+    ground_material=TEST_GROUND_MATERIAL,
+    shade_material=TEST_SHADE_MATERIAL,
+    identifier=EXTERNAL_COMFORT_IDENTIFIER,
+)
+TEST_MODEL = create_model(
+    ground_material=TEST_GROUND_MATERIAL,
+    shade_material=TEST_SHADE_MATERIAL,
+    identifier=EXTERNAL_COMFORT_IDENTIFIER,
+)
+
+
+def test_simulation_directory():
+    """_"""
+
+    wd = simulation_directory(model=TEST_MODEL)
+    assert wd.exists()
+    assert wd.is_dir()
+    assert wd.name == EXTERNAL_COMFORT_IDENTIFIER
+
+
+def test_simulation_id():
+    """_"""
+    assert isinstance(
+        simulation_id(
+            epw_file=EPW_FILE,
+            ground_material=TEST_GROUND_MATERIAL,
+            shade_material=TEST_SHADE_MATERIAL,
+        ),
+        str,
+    )
+
+
+def test_radiant_temperature():
+    """_"""
+    assert (
+        radiant_temperature(
+            [EPW_OBJ.dry_bulb_temperature * 0.5, EPW_OBJ.dry_bulb_temperature * 2],
+            view_factors=[0.25, 0.75],
+        ).average
+        == 16.924573102344482
+    )
+
+    with pytest.raises(ValueError):
+        radiant_temperature(
+            [EPW_OBJ.dry_bulb_temperature * 2],
+            view_factors=[0.25, 0.75],
+        )
+        radiant_temperature(
+            [
+                EPW_OBJ.dry_bulb_temperature,
+                EPW_OBJ.dry_bulb_temperature,
+                EPW_OBJ.dry_bulb_temperature * 2,
+            ],
+            view_factors=[0.25, 0.75, 0.5],
+        )
+
+
+def test_simulate_surface_temperature():
+    """_"""
+    with pytest.raises(ValueError):
+        simulate_surface_temperatures(model="not_a_model", epw_file=EPW_FILE)
+        simulate_surface_temperatures(model=TEST_MODEL, epw_file="not_an_epw_file.epw")
+
+    result = simulate_surface_temperatures(
+        model=TEST_MODEL, epw_file=EPW_FILE, remove_dir=True
+    )
+    assert isinstance(result, dict)
+    for key in [
+        "unshaded_up_temperature",
+        "shaded_up_temperature",
+        "unshaded_down_temperature",
+        "shaded_down_temperature",
+    ]:
+        assert key in result
+        assert isinstance(result[key], HourlyContinuousCollection)
+    assert result["unshaded_up_temperature"] == EPW_OBJ.sky_temperature
+
+    # reload old results and check if they are the same
+    reloaded_result = simulate_surface_temperatures(
+        model=TEST_MODEL, epw_file=EPW_FILE, remove_dir=False
+    )
+    assert (
+        reloaded_result["unshaded_up_temperature"] == result["unshaded_up_temperature"]
+    )
+    assert reloaded_result["shaded_up_temperature"] == result["shaded_up_temperature"]
+    assert (
+        reloaded_result["unshaded_down_temperature"]
+        == result["unshaded_down_temperature"]
+    )
+    assert (
+        reloaded_result["shaded_down_temperature"] == result["shaded_down_temperature"]
+    )
+
+
+def test_round_trip():
+    """Test whether an object can be converted to a dictionary, and json and back."""
+    tempfile = Path(gettempdir()) / "pytest_simulation_result.json"
+    SimulationResult.from_dict(TEST_SIMULATION_RESULT.to_dict())
+    SimulationResult.from_json(TEST_SIMULATION_RESULT.to_json())
+    SimulationResult.from_file(TEST_SIMULATION_RESULT.to_file(tempfile))
+
+    tempfile.unlink()
+
+    with pytest.raises(ValueError):
+        TEST_SIMULATION_RESULT.to_file("./not_a_json_file.txt")
+
+
+def test_simulation_result():
+    """_"""
+
+    assert isinstance(repr(TEST_SIMULATION_RESULT), str)
+    assert isinstance(str(TEST_SIMULATION_RESULT), str)
+
+    assert (
+        TEST_SIMULATION_RESULT.description(include_shade_material=False)
+        == f"{TEST_SIMULATION_RESULT.epw_file.name} - {TEST_SIMULATION_RESULT.ground_material.identifier} (ground material)"
+    )
+
+    with pytest.raises(ValueError):
+        SimulationResult(
+            epw_file="not_an_epw_file.epw",
+            ground_material=TEST_GROUND_MATERIAL,
+            shade_material=TEST_SHADE_MATERIAL,
+            identifier=EXTERNAL_COMFORT_IDENTIFIER,
+        )
+        SimulationResult(
+            epw_file=EPW_FILE,
+            ground_material="not_a_material",
+            shade_material=TEST_SHADE_MATERIAL,
+            identifier=EXTERNAL_COMFORT_IDENTIFIER,
+        )
+        SimulationResult(
+            epw_file=EPW_FILE,
+            ground_material=TEST_GROUND_MATERIAL,
+            shade_material="not a material",
+            identifier=EXTERNAL_COMFORT_IDENTIFIER,
+        )
+        SimulationResult(
+            epw_file=EPW_FILE,
+            ground_material=TEST_GROUND_MATERIAL,
+            shade_material=TEST_SHADE_MATERIAL,
+            identifier=EXTERNAL_COMFORT_IDENTIFIER,
+            shaded_down_temperature="not_a_collection",
+        )
diff --git a/LadybugTools_Engine/Python/tests/test_external_comfort/test_spatial_comfort.py b/LadybugTools_Engine/Python/tests/test_external_comfort/test_spatial_comfort.py
new file mode 100644
index 00000000..9cfa9da0
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_external_comfort/test_spatial_comfort.py
@@ -0,0 +1,79 @@
+import shutil
+from pathlib import Path
+
+import pandas as pd
+import pytest
+from ladybug.wea import AnalysisPeriod, Wea
+from ladybugtools_toolkit.external_comfort.spatial.spatial_comfort import SpatialComfort
+from ladybugtools_toolkit.honeybee_extension.simulation.radiance import HoneybeeRadiance
+
+from .. import (
+    CFD_DIRECTORY,
+    EPW_FILE,
+    SPATIAL_COMFORT_MODEL_OBJ,
+    SPATIAL_COMFORT_DIRECTORY,
+)
+from .test_simulate import TEST_SIMULATION_RESULT
+
+SPATIAL_COMFORT_DIRECTORY.mkdir(parents=True, exist_ok=True)
+CFD_LOCAL_DIRECTORY = SPATIAL_COMFORT_DIRECTORY / "cfd"
+CFD_LOCAL_DIRECTORY.mkdir(parents=True, exist_ok=True)
+
+
+# copy cfd values into local directory for testing
+for file in list(CFD_DIRECTORY.glob("**/*")):
+    shutil.copy(file, CFD_LOCAL_DIRECTORY)
+
+
+def test_run_spatial_annual_irradiance():
+    """Run spatial annual-irradiance simulation and return working directory."""
+
+    hr = HoneybeeRadiance(SPATIAL_COMFORT_MODEL_OBJ)
+    wea = Wea.from_epw_file(EPW_FILE)
+    with pytest.warns(UserWarning):
+        hr.simulate_annual_irradiance(wea=wea)
+    assert isinstance(hr.annual_irradiance(), pd.DataFrame)
+
+
+def test_run_spatial_sky_view() -> Path:
+    """Run spatial sky-view simulation and return working directory."""
+
+    hr = HoneybeeRadiance(SPATIAL_COMFORT_MODEL_OBJ)
+    hr.simulate_sky_view()
+    assert isinstance(hr.sky_view(), pd.DataFrame)
+
+
+def test_spatial_comfort():
+    """_"""
+
+    assert isinstance(
+        SpatialComfort(SPATIAL_COMFORT_DIRECTORY, TEST_SIMULATION_RESULT),
+        SpatialComfort,
+    )
+
+
+def test_spatial_comfort_processing():
+    """_"""
+
+    spatial_comfort = SpatialComfort(SPATIAL_COMFORT_DIRECTORY, TEST_SIMULATION_RESULT)
+
+    # remove existing files
+    for fp in spatial_comfort.spatial_simulation_directory.glob("*.parquet"):
+        fp.unlink()
+
+    # with pytest.warns((pd.errors.PerformanceWarning)):
+    assert isinstance(
+        spatial_comfort.universal_thermal_climate_index_calculated, pd.DataFrame
+    )
+
+
+def test_spatial_comfort_summary():
+    """_"""
+
+    spatial_comfort = SpatialComfort(SPATIAL_COMFORT_DIRECTORY, TEST_SIMULATION_RESULT)
+
+    # remove analsyis periods fo testing
+    spatial_comfort.analysis_periods = [
+        AnalysisPeriod(st_month=3, end_month=3, st_day=21, end_day=21)
+    ]
+    spatial_comfort.run_all()
diff --git a/LadybugTools_Engine/Python/tests/test_external_comfort/test_typology.py b/LadybugTools_Engine/Python/tests/test_external_comfort/test_typology.py
new file mode 100644
index 00000000..2c7589a4
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_external_comfort/test_typology.py
@@ -0,0 +1,68 @@
+from pathlib import Path
+from tempfile import gettempdir
+import pytest
+from ladybug.epw import EPW
+from ladybugtools_toolkit.external_comfort.typology import (
+    Typology,
+    combine_typologies,
+    Typologies,
+)
+
+from .. import EPW_FILE, EXTERNAL_COMFORT_IDENTIFIER
+from .test_shelter import TEST_SHELTER
+from .test_simulate import TEST_SIMULATION_RESULT
+
+EPW_OBJ = EPW(EPW_FILE)
+
+TEST_TYPOLOGY = Typology(
+    identifier=EXTERNAL_COMFORT_IDENTIFIER,
+    shelters=[TEST_SHELTER],
+    evaporative_cooling_effect=[0.1] * 8760,
+)
+
+
+def test_round_trip():
+    """_"""
+    tempfile = Path(gettempdir()) / "pytest_typology.json"
+    Typology.from_dict(TEST_TYPOLOGY.to_dict())
+    Typology.from_json(TEST_TYPOLOGY.to_json())
+    Typology.from_file(TEST_TYPOLOGY.to_file(tempfile))
+    tempfile.unlink()
+
+
+def test_combine_typologies():
+    """_"""
+    assert isinstance(
+        combine_typologies(
+            [Typologies.SKY_SHELTER.value, Typologies.SOUTH_SHELTER.value]
+        ),
+        Typology,
+    )
+
+
+def test_dry_bulb_temperature():
+    """_"""
+    assert TEST_TYPOLOGY.dry_bulb_temperature(EPW_OBJ).average == pytest.approx(
+        10.036858349164525, rel=0.1
+    )
+
+
+def test_relative_humidity():
+    """_"""
+    assert TEST_TYPOLOGY.relative_humidity(EPW_OBJ).average == pytest.approx(
+        81.36280821917846, rel=0.1
+    )
+
+
+def test_wind_speed():
+    """_"""
+    assert TEST_TYPOLOGY.wind_speed(EPW_OBJ).average == pytest.approx(
+        3.2418835616436126, rel=0.1
+    )
+
+
+def test_mean_radiant_temperature():
+    """_"""
+    assert TEST_TYPOLOGY.mean_radiant_temperature(
+        TEST_SIMULATION_RESULT
+    ).average == pytest.approx(15.64262493993061, rel=0.5)
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/test_utci.py b/LadybugTools_Engine/Python/tests/test_external_comfort/test_utci.py
similarity index 87%
rename from LadybugTools_Engine/Python/tests/external_comfort/test_utci.py
rename to LadybugTools_Engine/Python/tests/test_external_comfort/test_utci.py
index f505b398..a6e99047 100644
--- a/LadybugTools_Engine/Python/tests/external_comfort/test_utci.py
+++ b/LadybugTools_Engine/Python/tests/test_external_comfort/test_utci.py
@@ -2,12 +2,12 @@
 from ladybug.epw import EPW
 from ladybug_comfort.collection.utci import UTCI
 from ladybugtools_toolkit.categorical.categories import UTCI_DEFAULT_CATEGORIES
-from ladybugtools_toolkit.external_comfort.utci.calculate import utci
-from ladybugtools_toolkit.external_comfort.utci.postprocess import (
+from ladybugtools_toolkit.external_comfort.utci import (
     compare_monthly_utci,
     distance_to_comfortable,
     feasible_utci_limits,
     shade_benefit_category,
+    utci,
 )
 
 from .. import EPW_FILE
@@ -40,7 +40,6 @@ def test_compare_monthly_utci():
         utci_categories=UTCI_DEFAULT_CATEGORIES,
         identifiers=["test1", "test2"],
         density=True,
-        simplify=False,
     )
     assert a.shape == (12, 20)
     assert a.sum().sum() == 24
@@ -51,11 +50,8 @@ def test_compare_monthly_utci():
         utci_categories=UTCI_DEFAULT_CATEGORIES,
         identifiers=["test1", "test2"],
         density=True,
-        simplify=True,
     )
-    assert a.shape == (12, 6)
-
-    # TODO - add tests for lack of comfortclass attributes raising errors
+    assert a.shape == (12, 20)
 
 
 def test_shade_benefit_category():
@@ -77,7 +73,7 @@ def test_shade_benefit_category():
 def test_distance_to_comfortable():
     """_"""
     assert distance_to_comfortable(LB_UTCI_COLLECTION).average == pytest.approx(
-        -12.27, rel=0.01
+        5.228092141408169, rel=0.01
     )
 
 
diff --git a/LadybugTools_Engine/Python/tests/test_helpers.py b/LadybugTools_Engine/Python/tests/test_helpers.py
index 17ed108e..29d88a4b 100644
--- a/LadybugTools_Engine/Python/tests/test_helpers.py
+++ b/LadybugTools_Engine/Python/tests/test_helpers.py
@@ -1,62 +1,145 @@
 from datetime import timedelta
+import warnings  # pylint: disable=E0401
+
 
 import numpy as np
 import pandas as pd
 import pytest
-from ladybug.epw import EPW
 from ladybugtools_toolkit.helpers import (
+    air_pressure_at_height,
     AnalysisPeriod,
     angle_from_cardinal,
     angle_from_north,
     cardinality,
+    chunks,
     circular_weighted_mean,
     decay_rate_smoother,
+    DecayMethod,
     default_analysis_periods,
     default_combined_analysis_periods,
     default_hour_analysis_periods,
     default_month_analysis_periods,
-    evaporative_cooling_effect,
+    dry_bulb_temperature_at_height,
+    epw_wind_vectors,
     evaporative_cooling_effect_collection,
+    evaporative_cooling_effect,
     month_hour_binned_series,
+    OpenMeteoVariable,
     proximity_decay,
     radiation_at_height,
     remove_leap_days,
     rolling_window,
     sanitise_string,
+    scrape_openmeteo,
+    sunrise_sunset,
     target_wind_speed_collection,
     temperature_at_height,
     timedelta_tostring,
-    weibull_pdf,
-    wind_direction_average,
+    validate_timeseries,
     wind_speed_at_height,
 )
 
-from . import EPW_FILE
+from . import EPW_OBJ
+
 
-EPW_OBJ = EPW(EPW_FILE)
+def test_sunrise_sunset():
+    """_"""
+    assert sunrise_sunset(EPW_OBJ.location).shape == (365, 9)
 
 
-def test_wind_direction_average():
+def test_openmeteo_variable():
     """_"""
-    # Test empty list
-    assert np.isnan(wind_direction_average([]))
+    assert (
+        OpenMeteoVariable.from_string("windgusts_10m")
+        == OpenMeteoVariable.WINDGUSTS_10M
+    )
+    assert OpenMeteoVariable.TEMPERATURE_2M.openmeteo_unit == "°C"
+    assert OpenMeteoVariable.TEMPERATURE_2M.target_name == "Dry Bulb Temperature"
+    assert OpenMeteoVariable.TEMPERATURE_2M.target_unit == "C"
+    assert OpenMeteoVariable.TEMPERATURE_2M.target_multiplier == 1
+    assert OpenMeteoVariable.TEMPERATURE_2M.openmeteo_name == "temperature_2m"
+    assert (
+        OpenMeteoVariable.TEMPERATURE_2M.target_table_name == "Dry Bulb Temperature (C)"
+    )
+    assert (
+        OpenMeteoVariable.TEMPERATURE_2M.openmeteo_table_name == "temperature_2m (°C)"
+    )
+    assert OpenMeteoVariable.TEMPERATURE_2M.convert(1) == 1
 
-    # Test single angle
-    assert wind_direction_average([90]) == pytest.approx(90, rel=0.05)
 
-    # Test two angles
-    assert wind_direction_average([0, 180]) == pytest.approx(90, rel=0.05)
+def test_epw_wind_vectors():
+    """_"""
+    assert len(epw_wind_vectors(EPW_OBJ)) == 8760
+    assert len(epw_wind_vectors(EPW_OBJ, normalise=True)) == 8760
 
-    # Test three angles
-    assert wind_direction_average([90, 100, 110]) == pytest.approx(100.3, rel=0.05)
 
-    # Test angles outside of expected range
+def test_scrape_openmeteo():
+    """_"""
     with pytest.raises(ValueError):
-        wind_direction_average([-10, 0, 90, 180, 270, 360, 400])
+        # latitude out of range
+        scrape_openmeteo(
+            latitude=180,
+            longitude=EPW_OBJ.location.longitude,
+            convert_units=True,
+            start_date="2021-01-01",
+            end_date="2021-02-01",
+            variables=[
+                OpenMeteoVariable.DEWPOINT_2M,
+            ],
+        )
+        # longitude out of range
+        scrape_openmeteo(
+            latitude=EPW_OBJ.location.latitude,
+            longitude=-1000,
+            convert_units=True,
+            start_date="2021-01-01",
+            end_date="2021-02-01",
+            variables=[
+                OpenMeteoVariable.DEWPOINT_2M,
+            ],
+        )
+        # start date after end date
+        scrape_openmeteo(
+            latitude=EPW_OBJ.location.latitude,
+            longitude=EPW_OBJ.location.longitude,
+            convert_units=True,
+            start_date="2021-02-01",
+            end_date="2021-01-01",
+            variables=[
+                OpenMeteoVariable.DEWPOINT_2M,
+                OpenMeteoVariable.SOIL_MOISTURE_28_TO_100CM,
+                OpenMeteoVariable.TEMPERATURE_2M,
+            ],
+        )
+        # invalid variable
+        scrape_openmeteo(
+            latitude=EPW_OBJ.location.latitude,
+            longitude=EPW_OBJ.location.longitude,
+            convert_units=True,
+            start_date="2021-01-01",
+            end_date="2021-02-01",
+            variables=[
+                "not a variable",
+            ],
+        )
+
+    assert scrape_openmeteo(
+        latitude=EPW_OBJ.location.latitude,
+        longitude=EPW_OBJ.location.longitude,
+        convert_units=True,
+        start_date="2021-01-01",
+        end_date="2021-02-01",
+        variables=[
+            OpenMeteoVariable.WINDSPEED_10M,
+            OpenMeteoVariable.WINDDIRECTION_10M,
+            OpenMeteoVariable.TEMPERATURE_2M,
+        ],
+    ).shape == (768, 3)
+
 
-    # Test large number of angles
-    angles = [i for i in range(10, 350, 5)]
-    assert wind_direction_average(angles) == pytest.approx(178, rel=0.05)
+def test_chunks():
+    """_"""
+    assert len(list(chunks([1, 2, 3, 4, 5, 6, 7, 8, 9], 2))) == 5
 
 
 def test_radiation_at_height():
@@ -91,35 +174,6 @@ def test_temperature_at_height():
     assert temperature_at_height(10, 10, 200, lapse_rate=0.009) == 8.29
 
 
-def test_weibull_pdf():
-    """_"""
-    # Test case 1: normal input
-    wind_speeds = [1, 2, 3, 4, 5]
-    k, loc, c = weibull_pdf(wind_speeds)
-    assert isinstance(k, float)
-    assert isinstance(loc, float)
-    assert isinstance(c, float)
-
-    # Test case 2: input with zeros
-    wind_speeds = [0, 1, 2, 3, 4, 5]
-    k, loc, c = weibull_pdf(wind_speeds)
-    assert isinstance(k, float)
-    assert isinstance(loc, float)
-    assert isinstance(c, float)
-
-    # Test case 3: input with NaNs
-    wind_speeds = [1, 2, 3, 4, 5, float("nan")]
-    k, loc, c = weibull_pdf(wind_speeds)
-    assert isinstance(k, float)
-    assert isinstance(loc, float)
-    assert isinstance(c, float)
-
-    # Test case 4: input with negative values
-    wind_speeds = [-1, 1, 2, 3, 4, 5]
-    with pytest.raises(ValueError):
-        k, loc, c = weibull_pdf(wind_speeds)
-
-
 def test_circular_weighted_mean():
     """_"""
 
@@ -129,6 +183,12 @@ def test_circular_weighted_mean():
     with pytest.raises(ValueError):
         circular_weighted_mean(angles, weights)
 
+    # Test with number of weights not equal to number of angles
+    angles = [0, 90, 180, 270]
+    weights = [0.2, 0.2, 0.2, 0.2, 0.2]
+    with pytest.raises(ValueError):
+        circular_weighted_mean(angles, weights)
+
     # Test with negative angles
     angles = [-90, 0, 90, 180, 270]
     weights = [0.2, 0.2, 0.2, 0.2, 0.2]
@@ -140,6 +200,16 @@ def test_circular_weighted_mean():
     weights = [1 / 3, 1 / 3, 1 / 3]
     assert np.isclose(circular_weighted_mean(angles, weights), 180)
 
+    # Test equally distributed, with equal weighting
+    angles = [45, 135, 225, 315]
+    with pytest.warns(UserWarning):
+        circular_weighted_mean(angles)
+
+    # Test without weights specified
+    angles = [0, 90, 180, 270]
+    with pytest.warns(UserWarning):
+        assert isinstance(circular_weighted_mean(angles), float)
+
     # Test with different weights
     angles = [90, 180, 270]
     weights = [0.3, 0.3, 0.4]
@@ -148,12 +218,13 @@ def test_circular_weighted_mean():
     # Test about 0
     angles = [355, 5]
     weights = [0.5, 0.5]
-    assert np.isclose(circular_weighted_mean(angles, weights), 360)
+    assert np.isclose(circular_weighted_mean(angles, weights), 0)
 
     # Test opposing
     angles = [0, 180]
     weights = [0.5, 0.5]
-    assert np.isclose(circular_weighted_mean(angles, weights), 90)
+    with pytest.warns(UserWarning):
+        assert isinstance(circular_weighted_mean(angles, weights), float)
 
 
 def test_sanitise_string():
@@ -289,6 +360,7 @@ def test_cardinality_bad():
     """_"""
     with pytest.raises(ValueError):
         cardinality(370, directions=16)
+        cardinality(12, directions=7)
 
 
 def test_decay_rate_smoother():
@@ -303,12 +375,31 @@ def test_proximity_decay_good():
     """_"""
     assert (
         proximity_decay(
-            value=10,
-            distance_to_value=5,
-            max_distance=10,
-            decay_method="parabolic",
+            value=3,
+            distance_to_value=1.5,
+            max_distance=3,
+            decay_method=DecayMethod.LINEAR,
         )
-        == 7.5
+        == 1.5
+    )
+
+    assert (
+        proximity_decay(
+            value=3,
+            distance_to_value=1.5,
+            max_distance=3,
+            decay_method=DecayMethod.SIGMOID,
+        )
+        == 1.5
+    )
+    assert (
+        proximity_decay(
+            value=3,
+            distance_to_value=1.5,
+            max_distance=3,
+            decay_method=DecayMethod.PARABOLIC,
+        )
+        == 2.25
     )
 
 
@@ -319,7 +410,7 @@ def test_proximity_decay_bad():
             value=10,
             distance_to_value=5,
             max_distance=10,
-            decay_method="unknown",
+            decay_method="not a decay method",
         )
 
 
@@ -328,6 +419,9 @@ def test_evaporative_cooling_effect():
     dbt, rh = evaporative_cooling_effect(20, 50, 0.5)
     assert (dbt == pytest.approx(16.9, rel=0.1)) and (rh == pytest.approx(75, rel=0.1))
 
+    dbt, rh = evaporative_cooling_effect(20, 110, 0.5)
+    assert (dbt == pytest.approx(20, rel=0.1)) and (rh == pytest.approx(100, rel=0.1))
+
 
 def test_evaporative_cooling_effect_collection():
     """_"""
@@ -365,6 +459,22 @@ def test_wind_speed_at_height():
     ) == pytest.approx(1.5891948094037045, rel=0.0001)
 
 
+def test_air_pressure_at_height():
+    """_"""
+    assert air_pressure_at_height(
+        reference_value=2,
+        reference_height=10,
+        target_height=2,
+    ) == pytest.approx(2.001897379377387, rel=0.0001)
+
+
+def test_dry_bulb_temperature_at_height():
+    """_"""
+    assert sum(
+        dry_bulb_temperature_at_height(epw=EPW_OBJ, target_height=50).values
+    ) == pytest.approx(87388.7999999973, rel=0.0001)
+
+
 def test_remove_leap_days():
     """_"""
     # Test with a DataFrame containing leap days
@@ -407,6 +517,10 @@ def test_remove_leap_days():
     s = remove_leap_days(s)
     assert len(s) == 728
 
+    # Test with a series without a datetime index
+    with pytest.raises(ValueError):
+        remove_leap_days(pd.Series([1, 2, 3]))
+
 
 def test_month_hour_binned_series():
     """_"""
diff --git a/LadybugTools_Engine/Python/tests/test_honeybee_extension/__init__.py b/LadybugTools_Engine/Python/tests/test_honeybee_extension/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_model_geometry.py b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_model_geometry.py
new file mode 100644
index 00000000..cb91bb31
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_model_geometry.py
@@ -0,0 +1,98 @@
+from ladybug_geometry.geometry3d import Vector3D, Point3D, Plane
+
+from ladybugtools_toolkit.honeybee_extension.model import (
+    plane_intersections,
+    HbModelGeometry,
+    linesegments_to_polycollection,
+    slice_geometry,
+    slice_model,
+    PolyCollection,
+)
+from matplotlib import pyplot as plt
+
+from .. import TEST_DAYLIGHT_MODEL
+
+
+def test_slice_model():
+    """_"""
+    assert isinstance(
+        slice_model(
+            model=TEST_DAYLIGHT_MODEL,
+            plane=Plane(o=Point3D(0, 0, 1), n=Vector3D(0, 0, 1)),
+        ),
+        plt.Axes,
+    )
+
+
+def test_slice_geometry():
+    """_"""
+    assert isinstance(
+        slice_geometry(
+            hb_objects=TEST_DAYLIGHT_MODEL.faces,
+            plane=Plane(o=Point3D(0, 0, 1), n=Vector3D(0, 0, 1)),
+        ),
+        plt.Axes,
+    )
+
+
+def test_plane_intersections():
+    """_"""
+    assert (
+        len(
+            plane_intersections(
+                [i.geometry for i in TEST_DAYLIGHT_MODEL.faces],
+                plane=Plane(o=Point3D(0, 0, 2), n=Vector3D(0, 1, 1)),
+            )
+        )
+        == 8
+    )
+
+
+def test_linesegments_to_polycollection():
+    """_"""
+    assert isinstance(
+        linesegments_to_polycollection(
+            plane_intersections(
+                [i.geometry for i in TEST_DAYLIGHT_MODEL.faces],
+                plane=Plane(o=Point3D(0, 0, 2), n=Vector3D(0, 1, 1)),
+            )
+        ),
+        PolyCollection,
+    )
+
+
+def test_model_get_geometry():
+    """_"""
+
+    assert len(HbModelGeometry.AIRBOUNDARY.get_geometry(TEST_DAYLIGHT_MODEL)) == 0
+    assert len(HbModelGeometry.APERTURE.get_geometry(TEST_DAYLIGHT_MODEL)) == 32
+    assert len(HbModelGeometry.DOOR.get_geometry(TEST_DAYLIGHT_MODEL)) == 0
+    assert len(HbModelGeometry.FLOOR.get_geometry(TEST_DAYLIGHT_MODEL)) == 8
+    assert len(HbModelGeometry.ROOFCEILING.get_geometry(TEST_DAYLIGHT_MODEL)) == 8
+    assert len(HbModelGeometry.SHADE.get_geometry(TEST_DAYLIGHT_MODEL)) == 192
+    assert len(HbModelGeometry.WALL.get_geometry(TEST_DAYLIGHT_MODEL)) == 32
+
+    assert HbModelGeometry.WALL.alpha == 1
+    assert HbModelGeometry.WALL.edgecolor == "#E6B43C"
+    assert HbModelGeometry.WALL.facecolor == "#E6B43C"
+    assert HbModelGeometry.WALL.linewidth == 1
+    assert HbModelGeometry.WALL.zorder == 6
+
+    assert (
+        len(
+            HbModelGeometry.WALL.slice_polycollection(
+                model=TEST_DAYLIGHT_MODEL,
+                plane=Plane(o=Point3D(0, 0, 3), n=Vector3D(0, 0, 1)),
+            ).get_paths()
+        )
+        == 32
+    )
+    assert (
+        len(
+            HbModelGeometry.APERTURE.slice_polycollection(
+                model=TEST_DAYLIGHT_MODEL,
+                plane=Plane(o=Point3D(0, 0, 2), n=Vector3D(0, 1, 1)),
+            ).get_paths()
+        )
+        == 6
+    )
diff --git a/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_radiance.py b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_radiance.py
new file mode 100644
index 00000000..d40db9ac
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_radiance.py
@@ -0,0 +1,135 @@
+import pytest
+from pathlib import Path
+from ladybugtools_toolkit.honeybee_extension.simulation.radiance import (
+    radiance_parameters,
+    HoneybeeRadiance,
+)
+from ladybug.futil import nukedir
+from ladybugtools_toolkit.ladybug_extension.analysisperiod import (
+    describe_analysis_period,
+    AnalysisPeriod,
+)
+
+from .. import TEST_DAYLIGHT_MODEL, TEST_WEA
+
+TEST_HB_RAD = HoneybeeRadiance(TEST_DAYLIGHT_MODEL)
+
+
+def test_object_init():
+    """_"""
+
+    assert TEST_HB_RAD.model_name == "LBTBHoM_pytest_daylight"
+    assert TEST_HB_RAD.output_directory.parent.name == "simulation"
+
+
+def test_simulate_sky_view():
+    """_"""
+
+    nukedir(TEST_HB_RAD.output_directory / "sky_view", rmdir=True)
+
+    with pytest.raises(ValueError):
+        TEST_HB_RAD.sky_view()
+
+    assert isinstance(TEST_HB_RAD.simulate_sky_view(), Path)
+
+    assert TEST_HB_RAD.sky_view().shape == (25, 8)
+    assert TEST_HB_RAD.sky_view().values.sum() == pytest.approx(658, rel=10)
+
+
+def test_simulate_daylight_factor():
+    """_"""
+
+    nukedir(TEST_HB_RAD.output_directory / "daylight_factor", rmdir=True)
+
+    with pytest.raises(ValueError):
+        TEST_HB_RAD.daylight_factor()
+
+    assert isinstance(TEST_HB_RAD.simulate_daylight_factor(), Path)
+
+    assert TEST_HB_RAD.daylight_factor().shape == (25, 8)
+    assert TEST_HB_RAD.daylight_factor().values.sum() == pytest.approx(658, rel=10)
+
+
+def test_simulate_annual_daylight():
+    """_"""
+
+    nukedir(TEST_HB_RAD.output_directory / "annual_daylight", rmdir=True)
+
+    with pytest.raises(ValueError):
+        TEST_HB_RAD.annual_daylight()
+
+    with pytest.warns(UserWarning):
+        assert isinstance(TEST_HB_RAD.simulate_annual_daylight(wea=TEST_WEA), Path)
+
+    assert TEST_HB_RAD.annual_daylight().shape == (4398, 200)
+    assert TEST_HB_RAD.annual_daylight().values.sum() == pytest.approx(
+        902620740.0, rel=10000
+    )
+
+
+def test_simulate_annual_irradiance():
+    """_"""
+
+    nukedir(TEST_HB_RAD.output_directory / "annual_irradiance", rmdir=True)
+
+    with pytest.raises(ValueError):
+        TEST_HB_RAD.annual_irradiance()
+
+    with pytest.warns(UserWarning):
+        assert isinstance(TEST_HB_RAD.simulate_annual_irradiance(wea=TEST_WEA), Path)
+
+    assert TEST_HB_RAD.annual_irradiance().shape == (4398, 200)
+    assert TEST_HB_RAD.annual_irradiance().values.sum() == pytest.approx(
+        8228457, rel=1000
+    )
+
+
+def test_direct_sun_hours():
+    """_"""
+    ap = AnalysisPeriod(st_month=3)
+
+    nukedir(
+        TEST_HB_RAD.output_directory
+        / f"direct_sun_hours_{describe_analysis_period(ap, save_path=True, include_timestep=True)}",
+        rmdir=True,
+    )
+
+    with pytest.raises(ValueError):
+        TEST_HB_RAD.direct_sun_hours()
+
+    assert isinstance(
+        TEST_HB_RAD.simulate_direct_sun_hours(wea=TEST_WEA, analysis_period=ap), Path
+    )
+
+    assert TEST_HB_RAD.direct_sun_hours().shape == (25, 8)
+    assert TEST_HB_RAD.direct_sun_hours().values.sum() == 75444.0
+    assert (
+        TEST_HB_RAD.direct_sun_hours().columns.get_level_values(0)[0]
+        == "direct_sun_hours_0301_1231_00_23_01"
+    )
+
+
+def test_radiance_parameters():
+    """_"""
+
+    assert (
+        radiance_parameters(
+            TEST_DAYLIGHT_MODEL, detail_dim=0.1, recipe_type="daylight-factor"
+        )
+        == "-aa 0.25 -ab 2 -ad 512 -ar 25 -as 128 -dc 0.25 -dj 0.0 -dp 64 -dr 0 -ds 0.5 -dt 0.5 -lr 4 -lw 0.05 -ss 0.0 -st 0.85"
+    )
+
+    with pytest.warns(UserWarning):
+        assert (
+            radiance_parameters(
+                TEST_DAYLIGHT_MODEL, detail_dim=0.1, recipe_type="annual-daylight"
+            )
+            == "-ab 3 -ad 5000 -ar 10 -as 128 -c 1 -dc 0.25 -dp 64 -dr 0 -ds 0.5 -dt 0.5 -lr 4 -lw 2e-06 -ss 0.0 -st 0.85"
+        )
+
+    assert (
+        radiance_parameters(
+            TEST_DAYLIGHT_MODEL, detail_dim=0.1, recipe_type="sky-view", detail_level=1
+        )
+        == "-aa 0.2 -ab 3 -ad 2048 -ar 20 -as 2048 -dc 0.5 -dj 0.5 -dp 256 -dr 1 -ds 0.25 -dt 0.25 -lr 6 -lw 0.01 -ss 0.7 -st 0.5"
+    )
diff --git a/LadybugTools_Engine/Python/tests/honeybee_extension/test_results.py b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_results.py
similarity index 73%
rename from LadybugTools_Engine/Python/tests/honeybee_extension/test_results.py
rename to LadybugTools_Engine/Python/tests/test_honeybee_extension/test_results.py
index c15a6e61..c005ad82 100644
--- a/LadybugTools_Engine/Python/tests/honeybee_extension/test_results.py
+++ b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_results.py
@@ -16,10 +16,9 @@ def test_load_ill():
     assert load_ill([ILL_FILE]).sum().sum() == pytest.approx(5779077.539738757, rel=1)
 
 
-# def test_load_npy():
-#     """_"""
-#     TODO - make this method worl with sun-up-hours file in any location
-#     assert load_npy([NPY_FILE]).sum().sum() == pytest.approx(5779077.539738757, rel=1)
+def test_load_npy():
+    """_"""
+    assert load_npy([NPY_FILE]).sum().sum() == pytest.approx(887941060.0, rel=1)
 
 
 def test_load_sql():
@@ -39,4 +38,4 @@ def test_load_pts():
 
 def test_make_annual():
     """_"""
-    assert make_annual(load_ill([ILL_FILE])).shape == (8760, 100)
+    assert make_annual(load_ill([ILL_FILE])).shape == (8760, 25)
diff --git a/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_sensorgrid.py b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_sensorgrid.py
new file mode 100644
index 00000000..4e62e392
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_sensorgrid.py
@@ -0,0 +1,73 @@
+import pytest
+from ladybugtools_toolkit.honeybee_extension.simulation.sensorgrids import (
+    is_planar,
+    get_plane,
+    position_array,
+    vector_array,
+    estimate_spacing,
+    as_triangulation,
+    as_patchcollection,
+    plot_values,
+    get_limits,
+)
+from matplotlib.collections import PatchCollection
+from matplotlib.tri import Triangulation
+import matplotlib.pyplot as plt
+import numpy as np
+from ladybug_geometry.geometry3d import Vector3D, Plane, Point3D
+
+from .. import TEST_DAYLIGHT_MODEL
+
+TEST_SENSORGRID = TEST_DAYLIGHT_MODEL.properties.radiance.sensor_grids[0]
+
+
+def test_is_planar():
+    """_"""
+    assert is_planar(TEST_SENSORGRID)
+
+
+def test_get_plane():
+    """_"""
+    assert isinstance(get_plane(TEST_SENSORGRID), Plane)
+
+
+def test_position_array():
+    """_"""
+    assert position_array(TEST_SENSORGRID).sum() == pytest.approx(145, rel=1)
+    assert position_array(TEST_SENSORGRID).shape == (25, 3)
+
+
+def test_vector_array():
+    """_"""
+    assert vector_array(TEST_SENSORGRID).sum() == pytest.approx(25, rel=0.01)
+    assert vector_array(TEST_SENSORGRID).shape == (25, 3)
+
+
+def test_estimate_spacing():
+    """_"""
+    assert estimate_spacing(TEST_SENSORGRID) == 1
+
+
+def test_as_triangulation():
+    """_"""
+    assert isinstance(
+        as_triangulation(TEST_SENSORGRID, alpha_adjust=0.1), Triangulation
+    )
+
+
+def test_as_patchcollection():
+    """_"""
+    assert isinstance(as_patchcollection(TEST_SENSORGRID), PatchCollection)
+
+
+def test_plot_values():
+    """_"""
+    assert isinstance(
+        plot_values(sensorgrid=TEST_SENSORGRID, values=np.random.randint(0, 100, 25)),
+        plt.Axes,
+    )
+
+
+def test_get_limits():
+    """_"""
+    assert get_limits(sensorgrids=[TEST_SENSORGRID]) == ((-1.5, 6.5), (-1.5, 6.5))
diff --git a/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_sri.py b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_sri.py
new file mode 100644
index 00000000..edc77f04
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_honeybee_extension/test_sri.py
@@ -0,0 +1,41 @@
+import pytest
+from honeybee_energy.construction.opaque import OpaqueConstruction
+from honeybee_energy.material.opaque import EnergyMaterial
+from ladybugtools_toolkit.honeybee_extension.sri import (
+    calculate_sri,
+    construction_sri,
+    material_sri,
+)
+
+MATERIAL = EnergyMaterial(
+    "sri_material", 1, 1, 100, 100, solar_absorptance=0.5, thermal_absorptance=0.9
+)
+CONSTRUCTION = OpaqueConstruction("sri_construction", [MATERIAL])
+
+
+def test_calculate_sri() -> None:
+    """_"""
+    assert calculate_sri(
+        solar_reflectance=0.35, thermal_emittance=0.85, wind_speed=4
+    ) == pytest.approx(36.354029062087186, 0.001)
+    assert calculate_sri(
+        solar_reflectance=0.35, thermal_emittance=0.85, wind_speed=0, air_temperature=40
+    ) == pytest.approx(33.394936240990575, 0.001)
+    assert calculate_sri(
+        solar_reflectance=0.35,
+        thermal_emittance=0.85,
+        wind_speed=0,
+        air_temperature=40,
+        sky_temperature=0.5,
+        insolation=540,
+    ) == pytest.approx(33.83853104051297, 0.001)
+
+
+def test_construction_sri() -> None:
+    """_"""
+    assert construction_sri(CONSTRUCTION) == pytest.approx(58.414795244385736, 0.001)
+
+
+def test_material_sri() -> None:
+    """_"""
+    assert material_sri(MATERIAL) == pytest.approx(58.414795244385736, 0.001)
diff --git a/LadybugTools_Engine/Python/tests/honeybee_extension/__init__.py b/LadybugTools_Engine/Python/tests/test_ladybug_extension/__init__.py
similarity index 100%
rename from LadybugTools_Engine/Python/tests/honeybee_extension/__init__.py
rename to LadybugTools_Engine/Python/tests/test_ladybug_extension/__init__.py
diff --git a/LadybugTools_Engine/Python/tests/ladybug_extension/test_analysis_period.py b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_analysis_period.py
similarity index 96%
rename from LadybugTools_Engine/Python/tests/ladybug_extension/test_analysis_period.py
rename to LadybugTools_Engine/Python/tests/test_ladybug_extension/test_analysis_period.py
index 7fd83bf1..e6cf0468 100644
--- a/LadybugTools_Engine/Python/tests/ladybug_extension/test_analysis_period.py
+++ b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_analysis_period.py
@@ -3,7 +3,7 @@
 
 import pytest
 from ladybug.analysisperiod import AnalysisPeriod
-from ladybugtools_toolkit.ladybug_extension.analysis_period import (
+from ladybugtools_toolkit.ladybug_extension.analysisperiod import (
     analysis_period_from_datetimes,
     analysis_period_to_datetimes,
     describe_analysis_period,
diff --git a/LadybugTools_Engine/Python/tests/ladybug_extension/test_datacollection.py b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_datacollection.py
similarity index 95%
rename from LadybugTools_Engine/Python/tests/ladybug_extension/test_datacollection.py
rename to LadybugTools_Engine/Python/tests/test_ladybug_extension/test_datacollection.py
index a7eec71b..c29ef0a1 100644
--- a/LadybugTools_Engine/Python/tests/ladybug_extension/test_datacollection.py
+++ b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_datacollection.py
@@ -6,9 +6,7 @@
     collection_to_series,
 )
 
-from .. import EPW_FILE
-
-EPW_OBJ = EPW(EPW_FILE)
+from .. import EPW_OBJ
 
 SERIES_GOOD = pd.Series(
     data=EPW_OBJ.dry_bulb_temperature.values,
diff --git a/LadybugTools_Engine/Python/tests/ladybug_extension/test_dt.py b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_dt.py
similarity index 100%
rename from LadybugTools_Engine/Python/tests/ladybug_extension/test_dt.py
rename to LadybugTools_Engine/Python/tests/test_ladybug_extension/test_dt.py
diff --git a/LadybugTools_Engine/Python/tests/ladybug_extension/test_epw.py b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_epw.py
similarity index 93%
rename from LadybugTools_Engine/Python/tests/ladybug_extension/test_epw.py
rename to LadybugTools_Engine/Python/tests/test_ladybug_extension/test_epw.py
index 9358229a..cf66d366 100644
--- a/LadybugTools_Engine/Python/tests/ladybug_extension/test_epw.py
+++ b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_epw.py
@@ -24,9 +24,7 @@
     wet_bulb_temperature,
 )
 
-from .. import EPW_FILE
-
-EPW_OBJ = EPW(EPW_FILE)
+from .. import EPW_OBJ
 
 
 def test_to_dataframe():
@@ -37,10 +35,7 @@ def test_to_dataframe():
 
 def test_from_dataframe():
     """_"""
-    CSV_FILE = EPW_FILE.with_suffix(".csv")
     df = epw_to_dataframe(EPW_OBJ, include_additional=False)
-    df.to_csv(CSV_FILE)
-    df = pd.read_csv(CSV_FILE, index_col=0, parse_dates=True, header=[0, 1, 2])
     assert isinstance(epw_from_dataframe(df), EPW)
 
 
diff --git a/LadybugTools_Engine/Python/tests/external_comfort/test_ground_temperature.py b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_groundtemperature.py
similarity index 80%
rename from LadybugTools_Engine/Python/tests/external_comfort/test_ground_temperature.py
rename to LadybugTools_Engine/Python/tests/test_ladybug_extension/test_groundtemperature.py
index 7b7fd893..b0a54746 100644
--- a/LadybugTools_Engine/Python/tests/external_comfort/test_ground_temperature.py
+++ b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_groundtemperature.py
@@ -1,13 +1,11 @@
 from ladybug.epw import EPW
-from ladybugtools_toolkit.external_comfort.ground_temperature import (
+from ladybugtools_toolkit.ladybug_extension.groundtemperature import (
     ground_temperature_at_depth,
     hourly_ground_temperature,
     monthly_ground_temperature,
 )
 
-from .. import EPW_FILE
-
-EPW_OBJ = EPW(EPW_FILE)
+from .. import EPW_OBJ
 
 
 def test_ground_temperature_at_depth():
diff --git a/LadybugTools_Engine/Python/tests/ladybug_extension/test_header.py b/LadybugTools_Engine/Python/tests/test_ladybug_extension/test_header.py
similarity index 100%
rename from LadybugTools_Engine/Python/tests/ladybug_extension/test_header.py
rename to LadybugTools_Engine/Python/tests/test_ladybug_extension/test_header.py
diff --git a/LadybugTools_Engine/Python/tests/test_plot.py b/LadybugTools_Engine/Python/tests/test_plot.py
index 391dd34e..4ded032d 100644
--- a/LadybugTools_Engine/Python/tests/test_plot.py
+++ b/LadybugTools_Engine/Python/tests/test_plot.py
@@ -1,20 +1,23 @@
-import warnings
-
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 import pytest
 from ladybug.analysisperiod import AnalysisPeriod
-from ladybug.epw import EPW
 from ladybug_comfort.collection.utci import UTCI
-from ladybugtools_toolkit.external_comfort.material import Materials
 from ladybugtools_toolkit.ladybug_extension.datacollection import collection_to_series
-from ladybugtools_toolkit.plot import diurnal, heatmap, sunpath, windrose
+from ladybugtools_toolkit.plot._diurnal import diurnal, stacked_diurnals
+from ladybugtools_toolkit.plot._heatmap import heatmap
+from ladybugtools_toolkit.plot._sunpath import sunpath
+from ladybugtools_toolkit.plot._degree_days import (
+    cooling_degree_days,
+    heating_degree_days,
+    degree_days,
+)
+from ladybugtools_toolkit.plot._psychrometric import psychrometric
 from ladybugtools_toolkit.plot._utci import (
     utci_comfort_band_comparison,
     utci_comparison_diurnal_day,
     utci_day_comfort_metrics,
-    utci_distance_to_comfortable,
-    utci_heatmap,
     utci_heatmap_difference,
     utci_heatmap_histogram,
     utci_histogram,
@@ -29,12 +32,12 @@
     lighten_color,
     relative_luminance,
 )
+from ladybugtools_toolkit.plot._radiant_cooling_potential import (
+    radiant_cooling_potential,
+)
 
-from . import EPW_FILE
-
-EPW_OBJ = EPW(EPW_FILE)
+from . import EPW_OBJ
 
-GROUND_MATERIAL = Materials.LBT_AsphaltPavement.value
 LB_UTCI_COLLECTION = UTCI(
     EPW_OBJ.dry_bulb_temperature,
     EPW_OBJ.relative_humidity,
@@ -43,6 +46,21 @@
 ).universal_thermal_climate_index
 
 
+def test_cooling_degree_days():
+    """_"""
+    assert isinstance(cooling_degree_days(epw=EPW_OBJ), plt.Axes)
+
+
+def test_heating_degree_days():
+    """_"""
+    assert isinstance(heating_degree_days(epw=EPW_OBJ), plt.Axes)
+
+
+def test_degree_days():
+    """_"""
+    assert isinstance(degree_days(epw=EPW_OBJ), plt.Figure)
+
+
 def test_create_triangulation():
     """_"""
     # Test with valid input
@@ -124,6 +142,19 @@ def test_spatial_heatmap():
     plt.close("all")
 
 
+def test_radiant_cooling_potential():
+    """_"""
+    # create a pandas series of dew point temperature
+    dpt = collection_to_series(EPW_OBJ.dew_point_temperature)
+
+    # call the function
+    ax = radiant_cooling_potential(dpt)
+
+    # check that the returned object is a matplotlib axes
+    assert isinstance(ax, plt.Axes)
+    plt.close("all")
+
+
 def test_sunpath():
     """_"""
     assert isinstance(
@@ -138,6 +169,17 @@ def test_sunpath():
         plt.Axes,
     )
     plt.close("all")
+    assert isinstance(
+        sunpath(
+            EPW_OBJ.location,
+            analysis_period=AnalysisPeriod(
+                st_month=3, end_month=4, st_hour=9, end_hour=18
+            ),
+            cmap="inferno",
+        ),
+        plt.Axes,
+    )
+    plt.close("all")
 
 
 def test_timeseries_diurnal():
@@ -159,8 +201,32 @@ def test_timeseries_diurnal():
     )
     with pytest.raises(ValueError):
         diurnal(collection_to_series(EPW_OBJ.dry_bulb_temperature), period="decadely")
+        diurnal(
+            collection_to_series(EPW_OBJ.dry_bulb_temperature).reset_index(drop=True),
+            period="monthly",
+        )
+        diurnal(
+            collection_to_series(EPW_OBJ.dry_bulb_temperature),
+            period="monthly",
+            minmax_range=[0.95, 0.05],
+        )
+        diurnal(
+            collection_to_series(EPW_OBJ.dry_bulb_temperature),
+            period="monthly",
+            quantile_range=[0.95, 0.05],
+        )
     plt.close("all")
 
+    assert isinstance(
+        stacked_diurnals(
+            datasets=[
+                collection_to_series(EPW_OBJ.dry_bulb_temperature),
+                collection_to_series(EPW_OBJ.relative_humidity),
+            ]
+        ),
+        plt.Figure,
+    )
+
 
 def test_heatmap():
     """_"""
@@ -169,6 +235,28 @@ def test_heatmap():
     )
     plt.close("all")
 
+    mask = np.random.random(8760) > 0.5
+    assert isinstance(
+        heatmap(collection_to_series(EPW_OBJ.dry_bulb_temperature), mask=mask), plt.Axes
+    )
+    plt.close("all")
+
+    mask_bad = np.random.random(10) > 0.5
+    with pytest.raises(ValueError):
+        heatmap(collection_to_series(EPW_OBJ.dry_bulb_temperature), mask=mask_bad)
+    plt.close("all")
+
+    assert isinstance(
+        heatmap(
+            pd.Series(
+                np.random.random(21000),
+                index=pd.date_range("2000-01-01", periods=21000, freq="H"),
+            )
+        ),
+        plt.Axes,
+    )
+    plt.close("all")
+
 
 def test_utci_comparison_diurnal():
     """_"""
@@ -185,8 +273,8 @@ def test_utci_day_comfort_metrics():
         utci_day_comfort_metrics(
             collection_to_series(LB_UTCI_COLLECTION),
             collection_to_series(EPW_OBJ.dry_bulb_temperature),
-            collection_to_series(EPW_OBJ.dry_bulb_temperature).rename(
-                "Mean Radiant Temperature (C)"
+            collection_to_series(
+                EPW_OBJ.dry_bulb_temperature, "Mean Radiant Temperature (C)"
             ),
             collection_to_series(EPW_OBJ.relative_humidity),
             collection_to_series(EPW_OBJ.wind_speed),
@@ -198,12 +286,6 @@ def test_utci_day_comfort_metrics():
     plt.close("all")
 
 
-def test_utci_distance_to_comfortable():
-    """_"""
-    assert isinstance(utci_distance_to_comfortable(LB_UTCI_COLLECTION), plt.Axes)
-    plt.close("all")
-
-
 def test_utci_heatmap_difference():
     """_"""
     assert isinstance(
@@ -218,12 +300,6 @@ def test_utci_heatmap_histogram():
     plt.close("all")
 
 
-def test_utci_heatmap():
-    """_"""
-    assert isinstance(utci_heatmap(LB_UTCI_COLLECTION), plt.Axes)
-    plt.close("all")
-
-
 def test_utci_journey():
     """_"""
     assert isinstance(
@@ -257,21 +333,9 @@ def test_utci_comfort_band_comparison():
     plt.close("all")
 
 
-def test_windrose():
+def test_psychrometric():
     """_"""
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        assert isinstance(
-            windrose(
-                wind_directions=EPW_OBJ.wind_direction.values,
-                data=EPW_OBJ.wind_speed.values,
-                ax=None,
-                data_bins=[0, 90, 180, 270, 360],
-                include_legend=False,
-                include_percentages=True,
-            ),
-            plt.Axes,
-        )
+    assert isinstance(psychrometric(epw=EPW_OBJ), plt.Figure)
     plt.close("all")
 
 
diff --git a/LadybugTools_Engine/Python/tests/test_solar.py b/LadybugTools_Engine/Python/tests/test_solar.py
new file mode 100644
index 00000000..123433fc
--- /dev/null
+++ b/LadybugTools_Engine/Python/tests/test_solar.py
@@ -0,0 +1,52 @@
+import matplotlib.pyplot as plt
+from ladybugtools_toolkit.solar import Solar
+import pytest
+
+from . import EPW_OBJ, EPW_FILE
+
+
+def test_solar_from_epw():
+    """_"""
+
+    assert isinstance(Solar.from_epw(EPW_OBJ), Solar)
+    assert isinstance(Solar.from_epw(EPW_FILE), Solar)
+
+
+def test_altitudes():
+    """_"""
+
+    with pytest.raises(ValueError):
+        Solar.altitudes(n="not_an_int")
+        Solar.altitudes(n=2)
+
+
+def test_azimuths():
+    """_"""
+
+    with pytest.raises(ValueError):
+        Solar.azimuths(n="not_an_int")
+        Solar.azimuths(n=2)
+
+
+def test_solar_calc():
+    solar = Solar.from_epw(EPW_OBJ)
+
+    assert solar.detailed_irradiance_matrix(location=EPW_OBJ.location).shape == (
+        8760,
+        36,
+    )
+    assert solar.directional_irradiance_matrix(location=EPW_OBJ.location).shape == (
+        8760,
+        128,
+    )
+
+
+def test_solar_plot():
+    solar = Solar.from_epw(EPW_OBJ)
+
+    assert isinstance(
+        solar.plot_tilt_orientation_factor(location=EPW_OBJ.location), plt.Axes
+    )
+    assert isinstance(
+        solar.plot_directional_irradiance(location=EPW_OBJ.location), plt.Axes
+    )
diff --git a/LadybugTools_Engine/Python/tests/test_wind.py b/LadybugTools_Engine/Python/tests/test_wind.py
index f6c2eaf8..5c62e496 100644
--- a/LadybugTools_Engine/Python/tests/test_wind.py
+++ b/LadybugTools_Engine/Python/tests/test_wind.py
@@ -1,50 +1,29 @@
+import matplotlib.pyplot as plt
 import pandas as pd
+from pathlib import Path
+from tempfile import gettempdir
+import numpy as np
 import pytest
-from ladybug.epw import EPW, AnalysisPeriod
+from ladybug.epw import AnalysisPeriod
 from ladybugtools_toolkit.ladybug_extension.datacollection import collection_to_series
-from ladybugtools_toolkit.wind.wind import DirectionBins, Wind
+from ladybugtools_toolkit.wind import Wind
 
-from . import EPW_FILE
+from . import EPW_OBJ, EPW_FILE
 
-EPW_OBJ = EPW(EPW_FILE)
+TEST_WIND = Wind.from_epw(EPW_OBJ)
 
 
-def test_direction_bins():
-    """."""
-    db = DirectionBins()
-    assert db.bin_width == 45
-    assert db.directions == 8
-    assert db.bins.sum() == 3240
-    assert db.cardinal_directions == ["N", "NE", "E", "SE", "S", "SW", "W", "NW"]
-
-    db = DirectionBins(directions=16)
-    assert db.bin_width == 22.5
-    assert db.directions == 16
-    assert db.bins.sum() == 6120
-    assert db.cardinal_directions == [
-        "N",
-        "NNE",
-        "NE",
-        "ENE",
-        "E",
-        "ESE",
-        "SE",
-        "SSE",
-        "S",
-        "SSW",
-        "SW",
-        "WSW",
-        "W",
-        "WNW",
-        "NW",
-        "NNW",
-    ]
-
-    db = DirectionBins(directions=7, centered=False)
-    assert db.bin_width == pytest.approx(51.42857142857143, rel=0.01)
-    assert db.directions == 7
-    assert db.bins.sum() == 2520.0
-    assert db.cardinal_directions == ["NNE", "EbN", "SEbE", "S", "SWbW", "WbN", "NNW"]
+def test_round_trip():
+    """_"""
+    tempfile = Path(gettempdir()) / "pytest_wind.json"
+    Wind.from_dict(TEST_WIND.to_dict())
+    Wind.from_json(TEST_WIND.to_json())
+    Wind.from_file(TEST_WIND.to_file(tempfile))
+
+    tempfile.unlink()
+
+    with pytest.raises(ValueError):
+        TEST_WIND.to_file("./not_a_json_file.txt")
 
 
 def test_wind_good():
@@ -65,10 +44,53 @@ def test_wind_bad():
     ws = collection_to_series(EPW_OBJ.wind_speed)
     wd = collection_to_series(EPW_OBJ.wind_direction)
     with pytest.raises(ValueError):
+        # height at 0
+        Wind(
+            wind_speeds=ws.values,
+            wind_directions=wd.values,
+            datetimes=ws.index,
+            height_above_ground=0,
+        )
+        # speed and direction not same length
         Wind(
             wind_speeds=ws.values[0:10],
             wind_directions=wd.values,
             datetimes=ws.index,
+            height_above_ground=10,
+        )
+        # index not same length as speed and direction
+        Wind(
+            wind_speeds=ws.values[0:1],
+            wind_directions=wd.values[0:1],
+            datetimes=ws.index,
+            height_above_ground=10,
+        )
+        # index not datetime
+        Wind(
+            wind_speeds=[1, 1, 1, 1, 1],
+            wind_directions=[1, 1, 1, 1, 1],
+            datetimes=[ws.index[0]] * 5,
+            height_above_ground=10,
+        )
+        # speed contains NaN
+        Wind(
+            wind_speeds=np.where(ws < 5, np.nan, ws),
+            wind_directions=wd.values,
+            datetimes=ws.index,
+            height_above_ground=0,
+        )
+        # direction contains NaN
+        Wind(
+            wind_speeds=ws.values,
+            wind_directions=np.where(wd < 180, np.nan, wd),
+            datetimes=ws.index,
+            height_above_ground=0,
+        )
+        # direction contains negative values
+        Wind(
+            wind_speeds=ws.values,
+            wind_directions=np.where(wd < 180, -1, wd),
+            datetimes=ws.index,
             height_above_ground=0,
         )
 
@@ -76,6 +98,8 @@ def test_wind_bad():
 def test_wind_from_epw():
     """."""
     assert isinstance(Wind.from_epw(EPW_OBJ), Wind)
+    assert isinstance(Wind.from_epw(EPW_FILE), Wind)
+    assert Wind.from_epw(EPW_FILE).source == EPW_FILE.name
 
 
 def test_wind_from_dataframe():
@@ -94,48 +118,158 @@ def test_wind_from_dataframe():
         Wind,
     )
 
+    with pytest.raises(ValueError):
+        Wind.from_dataframe(
+            df="not_a_dataframe",
+            wind_speed_column="speed",
+            wind_direction_column="direction",
+            height_above_ground=10,
+        )
+        Wind.from_dataframe(
+            df=df.reset_index(drop=True),
+            wind_speed_column="speed",
+            wind_direction_column="direction",
+            height_above_ground=10,
+        )
+
 
 def test_wind_from_openmeteo():
     """."""
+    w = Wind.from_openmeteo(
+        latitude=EPW_OBJ.location.latitude,
+        longitude=EPW_OBJ.location.longitude,
+        start_date="2021-01-01",
+        end_date="2021-02-01",
+    )
     assert isinstance(
-        Wind.from_openmeteo(
-            latitude=51.5074,
-            longitude=0.1278,
-            start_date="2020-01-01",
-            end_date="2020-01-02",
-        ),
+        w,
         Wind,
     )
+    assert w.source == "OpenMeteo"
 
 
-def test_wind_functions():
-    """."""
-    w = Wind.from_epw(EPW_OBJ)
+def test_wind_from_uv():
+    """_"""
+    u, v = TEST_WIND.uv.values.T
+    with pytest.warns(UserWarning):
+        assert isinstance(Wind.from_uv(u=u, v=v, datetimes=TEST_WIND.datetimes), Wind)
 
-    assert (
-        len(w.filter_by_analysis_period(AnalysisPeriod(st_month=3, end_month=5)))
-        == 2208
+
+def test_calm():
+    """_"""
+    assert TEST_WIND.calm(threshold=1) == pytest.approx(
+        0.08607305936073059, rel=0.01
+    )  # TODO - check against LBT WR
+
+
+def test_calm_datetimes():
+    """_"""
+    assert isinstance(TEST_WIND.calm_datetimes, list)
+
+
+def test_exceedance_probability():
+    """_"""
+    assert TEST_WIND.exceedance(limit_value=3.5).values.sum() == pytest.approx(
+        5.1, rel=0.1
     )
-    assert w.calm(threshold=1) == pytest.approx(0.19988584474885845, rel=0.01)
-    assert w.exceedance(limit_value=3.5).values.sum() == pytest.approx(
-        51.16172035633674, rel=0.01
+
+
+def test_bin_data():
+    """_"""
+    assert TEST_WIND.bin_data().shape == (8760, 2)
+
+
+def test_prevailing():
+    """_"""
+    assert TEST_WIND.prevailing()[0] == (355.0, 5.0)
+
+
+def test_probabilities():
+    """_"""
+    assert TEST_WIND.probabilities().columns.tolist() == ["50.0%", "95.0%"]
+    assert TEST_WIND.probabilities(directions=4).values.mean() == pytest.approx(
+        5.0875, rel=0.01
     )
-    assert len(w.filter_by_direction(left_angle=350, right_angle=94)) == 3185
-    assert len(w.filter_by_speed(min_speed=0.5, max_speed=4.5)) == 5806
-    assert w.frequency_table().values.sum() == 8162
-    assert w.max() == 17.5
-    assert w.min() == 0
-    assert w.mean() == pytest.approx(3.2418835616438364, rel=0.01)
-    assert w.median() == 3.1
-    assert w.percentile(0.25) == 1.5
-    assert w.prevailing()[0] == "SW"
-    assert w.wind_matrix().shape == (24, 24)
-    assert w.to_height(target_height=52).ws.sum() == pytest.approx(
-        48732.60735555029, rel=0.01
+
+
+def test_stats():
+    """_"""
+    assert TEST_WIND.max_speed == 17.5
+    assert TEST_WIND.min_speed == 0
+    assert TEST_WIND.mean_speed() == pytest.approx(0.43, rel=0.01)
+    assert TEST_WIND.mean_speed(remove_calm=True) == pytest.approx(0.46, rel=0.01)
+    assert TEST_WIND.median_speed == 3.1
+    assert TEST_WIND.percentile(0.25) == 1.5
+
+
+def test_matrix():
+    """_"""
+    assert TEST_WIND.wind_matrix().shape == (24, 24)
+
+
+def test_to_height():
+    """_"""
+    assert TEST_WIND.to_height(target_height=52).ws.sum() == pytest.approx(
+        48732.607, rel=0.01
     )
 
-    db = DirectionBins(directions=4)
+
+def test_directional_factors():
+    """_"""
+    assert TEST_WIND.apply_directional_factors(
+        directions=4, factors=[0.5] * 4
+    ).mean_speed() == pytest.approx(0.215, rel=0.01)
+
+
+def test_filters():
+    """_"""
+    assert (
+        len(
+            TEST_WIND.filter_by_analysis_period(AnalysisPeriod(st_month=3, end_month=5))
+        )
+        == 2208
+    )
+    assert len(TEST_WIND.filter_by_direction(left_angle=350, right_angle=94)) == 3185
+    assert len(TEST_WIND.filter_by_speed(min_speed=0.5, max_speed=4.5)) == 5806
+    assert (
+        len(
+            TEST_WIND.filter_by_boolean_mask(
+                [i % 25 == 0 for i in range(len(TEST_WIND))]
+            )
+        )
+        == 351
+    )
     assert (
-        w.apply_directional_factors(direction_bins=db, factors=[0, 1, 20, 0]).ws.sum()
-        == 201354.6
+        len(TEST_WIND.filter_by_time(months=[3, 4], hours=[4, 6, 19, 21], years=[2017]))
+        == 244
     )
+
+
+def test_plot_windrose():
+    """_"""
+    assert isinstance(TEST_WIND.plot_windrose(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_timeseries():
+    """_"""
+    assert isinstance(TEST_WIND.plot_timeseries(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_wind_matrix():
+    """_"""
+    assert isinstance(TEST_WIND.plot_windmatrix(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_densityfunction():
+    """_"""
+    assert isinstance(TEST_WIND.plot_densityfunction(), plt.Axes)
+    plt.close("all")
+
+
+def test_plot_windhistogram():
+    """_"""
+    assert isinstance(TEST_WIND.plot_windhistogram(), plt.Axes)
+    plt.close("all")
diff --git a/LadybugTools_Engine/Query/GetMaterial.cs b/LadybugTools_Engine/Query/GetMaterial.cs
deleted file mode 100644
index bbf0ddaf..00000000
--- a/LadybugTools_Engine/Query/GetMaterial.cs
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * This file is part of the Buildings and Habitats object Model (BHoM)
- * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
- *
- * Each contributor holds copyright over their respective contributions.
- * The project versioning (Git) records all such contribution source information.
- *
- *
- * The BHoM is free software: you can redistribute it and/or modify
- * it under the terms of the GNU Lesser General Public License as published by
- * the Free Software Foundation, either version 3.0 of the License, or
- * (at your option) any later version.
- *
- * The BHoM is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * GNU Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public License
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.
- */
-
-using BH.Engine.Python;
-using BH.oM.Base.Attributes;
-using BH.oM.LadybugTools;
-using System.Linq;
-using System.Collections.Generic;
-using System.ComponentModel;
-using BH.oM.Python;
-
-namespace BH.Engine.LadybugTools
-{
-    public static partial class Query
-    {
-        [Description("Returns a list of materials from the Python Materials list.")]
-        [Input("filter", "Text to filter the resultant list by. Filter applies to the material identifier. Leave blank to return all materials.")]
-        [Output("materials", "A list of materials.")]
-        public static List<ILadybugToolsMaterial> GetMaterial(string filter = "")
-        {
-            if (string.IsNullOrEmpty(filter))
-                filter = "";
-
-            PythonEnvironment env = Compute.InstallPythonEnv_LBT(true);
-
-            string pythonScript = string.Join("\n", new List<string>()
-            {
-
-                "import traceback",
-                "try:",
-                "    from ladybugtools_toolkit.external_comfort.material import Materials",
-                $"    materials = [material.value.to_json() for material in Materials if \"{filter}\".lower() in material.value.identifier.lower()]",
-                "    materials = f\"[{', '.join(materials)}]\"",
-                "    print(materials)",
-                "except Exception as exc:",
-                "    print(traceback.format_exc())",
-            });
-
-            string result = env.RunPythonString(pythonScript).Trim();
-
-            List<object> lbtMaterials = Serialiser.Convert.FromJsonArray(result).ToList();
-
-            return lbtMaterials.Select(m => m as ILadybugToolsMaterial).Where(m => m != null).ToList();
-        }
-    }
-}
-
-
diff --git a/LadybugTools_Engine/Query/GetTypology.cs b/LadybugTools_Engine/Query/GetTypology.cs
deleted file mode 100644
index f25c6b2f..00000000
--- a/LadybugTools_Engine/Query/GetTypology.cs
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * This file is part of the Buildings and Habitats object Model (BHoM)
- * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
- *
- * Each contributor holds copyright over their respective contributions.
- * The project versioning (Git) records all such contribution source information.
- *
- *
- * The BHoM is free software: you can redistribute it and/or modify
- * it under the terms of the GNU Lesser General Public License as published by
- * the Free Software Foundation, either version 3.0 of the License, or
- * (at your option) any later version.
- *
- * The BHoM is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * GNU Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public License
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.
- */
-
-using BH.Engine.Python;
-using BH.oM.Base.Attributes;
-using BH.oM.LadybugTools;
-using System.Linq;
-using System.Collections.Generic;
-using System.ComponentModel;
-using BH.oM.Python;
-
-namespace BH.Engine.LadybugTools
-{
-    public static partial class Query
-    {
-        [Description("Returns a list of Typology objects from the Python predefined Typologies list.")]
-        [Input("filter", "Text to filter the resultant list by. Filter applies to the typology name. Leave blank to return all typologies.")]
-        [Output("typologies", "A list of Typology objects.")]
-        public static List<Typology> GetTypology(string filter = "")
-        {
-            if (string.IsNullOrEmpty(filter))
-                filter = "";
-
-            PythonEnvironment env = Compute.InstallPythonEnv_LBT(true);
-
-            string pythonScript = string.Join("\n", new List<string>()
-            {
-                "import traceback",
-                "",
-                "try:",
-                "    from ladybugtools_toolkit.external_comfort.typology import Typologies",
-                $"    typologies = [typology.value.to_json() for typology in Typologies if \"{filter}\".lower() in typology.value.name.lower()]",
-                "    typologies = f\"[{', '.join(typologies)}]\"",
-                "    print(typologies)",
-                "except Exception as exc:",
-                "    print(traceback.format_exc())",
-            });
-
-            string result = env.RunPythonString(pythonScript).Trim();
-
-            List<object> lbtTypologies = Serialiser.Convert.FromJsonArray(result).ToList();
-
-            List<Typology> typologies = lbtTypologies.Select(t => t as Typology).Where(t => t != null).ToList();
-
-            return typologies;
-        }
-    }
-}
-
-
diff --git a/LadybugTools_Engine/Query/IsPollinationInstalled.cs b/LadybugTools_Engine/Query/IsPollinationInstalled.cs
new file mode 100644
index 00000000..d9fa3f7d
--- /dev/null
+++ b/LadybugTools_Engine/Query/IsPollinationInstalled.cs
@@ -0,0 +1,62 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *
+ *
+ * The BHoM is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License as published by
+ * the Free Software Foundation, either version 3.0 of the License, or
+ * (at your option) any later version.
+ *
+ * The BHoM is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ * GNU Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public License
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.
+ */
+
+using BH.oM.Base.Attributes;
+using BH.oM.LadybugTools;
+using System.Linq;
+using System.Collections.Generic;
+using System.ComponentModel;
+using BH.oM.Python;
+using System.IO;
+using System;
+using System.Diagnostics;
+
+namespace BH.Engine.LadybugTools
+{
+    public static partial class Query
+    {
+        [Description("Return True if Pollination is installed to the currently supported version.")]
+        [Input("targetPollinationVersion", "The target Pollination version that BHoM currently supports.")]
+        [Output("bool", "True if Pollination is installed to the currently supported version.")]
+        public static bool IsPollinationInstalled(string targetPollinationVersion = "1.35.14")
+        {
+            // check if referenced Python is installed
+            string referencedExecutable = System.Environment.GetFolderPath(System.Environment.SpecialFolder.ProgramFiles) + @"\ladybug_tools\python\python.exe";
+            if (!File.Exists(referencedExecutable))
+            {
+                Base.Compute.RecordError($"Could not find referenced python executable at {referencedExecutable}. Please install Pollination version {targetPollinationVersion} and try again.");
+                return false;
+            }
+
+            // obtain version of pollination installed
+            string referencedUninstaller = System.Environment.GetFolderPath(System.Environment.SpecialFolder.ProgramFiles) + @"\pollination\uninstall.exe";
+            FileVersionInfo versionInfo = FileVersionInfo.GetVersionInfo(referencedUninstaller);
+            if (versionInfo.ProductVersion != targetPollinationVersion)
+            {
+                Base.Compute.RecordError($"Pollination version installed ({versionInfo.ProductVersion}) is not the same as the version required for this code to function correctly ({targetPollinationVersion}).");
+                return false;
+            }
+
+            return true;
+        }
+    }
+}
diff --git a/LadybugTools_Engine/Versioning_70.json b/LadybugTools_Engine/Versioning_70.json
new file mode 100644
index 00000000..09257534
--- /dev/null
+++ b/LadybugTools_Engine/Versioning_70.json
@@ -0,0 +1,13 @@
+{
+  "MessageForDeleted": {
+    "BH.Engine.LadybugTools.Convert.FromHoneybeeSurface(System.Object)": "This method has been removed as part of the removal of the dependency from Rhino in the LadybugTools_Toolkit.",
+    "BH.Engine.LadybugTools.Create.Typology(System.Collections.Generic.List<BH.oM.LadybugTools.Shelter>, System.String, System.Double, System.Double, System.Double)": "This method only did validation for creating the object, which has been moved to methods that take a Typology as an input.",
+    "BH.Engine.LadybugTools.Convert.SnakeCaseToPascalCase(System.String)": "This method and other case conversion methods in the LBT Toolkit have been replaced by a single method that can do case conversions when given a case type.",
+    "BH.Engine.LadybugTools.Convert.PascalCaseToSnakeCase(System.String)": "This method and other case conversion methods in the LBT Toolkit have been replaced by a single method that can do case conversions when given a case type.",
+    "BH.Engine.LadybugTools.Compute.EPWtoCustomObject(System.String)": "Due to inclusion of EPW as an object, this method is no longer required and has been removed.",
+    "BH.Engine.LadybugTools.Query.GetMaterial(System.String)": "All methods that involve serialisation of Ladybug Python objects have been converted into adapter Execute Commands, to access, use a LadybugToolsAdapter with the Execute method.",
+    "BH.Engine.LadybugTools.Query.GetTypology(System.String)": "All methods that involve serialisation of Ladybug Python objects have been converted into adapter Execute Commands, to access, use a LadybugToolsAdapter with the Execute method.",
+    "BH.Engine.LadybugTools.Compute.ExternalComfort(BH.oM.LadybugTools.SimulationResult, BH.oM.LadybugTools.Typology)": "All methods that involve serialisation of Ladybug Python objects have been converted into adapter Execute Commands, to access, use a LadybugToolsAdapter with the Execute method.",
+    "BH.Engine.LadybugTools.Compute.SimulationResult(System.String, BH.oM.LadybugTools.ILadybugToolsMaterial, BH.oM.LadybugTools.ILadybugToolsMaterial)": "All methods that involve serialisation of Ladybug Python objects have been converted into adapter Execute Commands, to access, use a LadybugToolsAdapter with the Execute method."
+  }
+}
\ No newline at end of file
diff --git a/LadybugTools_Engine/packages.config b/LadybugTools_Engine/packages.config
deleted file mode 100644
index bd92a9ef..00000000
--- a/LadybugTools_Engine/packages.config
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<packages>
-  <package id="RhinoCommon" version="6.33.20343.16431" targetFramework="net472" />
-</packages>
\ No newline at end of file
diff --git a/LadybugTools_Toolkit.sln b/LadybugTools_Toolkit.sln
index 42112650..2267205a 100644
--- a/LadybugTools_Toolkit.sln
+++ b/LadybugTools_Toolkit.sln
@@ -1,13 +1,13 @@
 
 Microsoft Visual Studio Solution File, Format Version 12.00
-# Visual Studio Version 16
-VisualStudioVersion = 16.0.30011.22
+# Visual Studio Version 17
+VisualStudioVersion = 17.7.34202.233
 MinimumVisualStudioVersion = 10.0.40219.1
-Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "LadybugTools_Engine", "LadybugTools_Engine\LadybugTools_Engine.csproj", "{68AB279E-3E50-4EE3-9E9E-C5C100DF5C58}"
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "LadybugTools_oM", "LadybugTools_oM\LadybugTools_oM.csproj", "{ABC2CD49-3DCF-46C3-9249-EB09C88ECFFD}"
 EndProject
-Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "LadybugTools_oM", "LadybugTools_oM\LadybugTools_oM.csproj", "{3778EE3E-F314-4EB5-B99F-CF4A8C254E80}"
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "LadybugTools_Engine", "LadybugTools_Engine\LadybugTools_Engine.csproj", "{0A409572-CA4B-4267-95AD-F9A4A699DB89}"
 EndProject
-Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "LadybugTools_Adapter", "LadybugTools_Adapter\LadybugTools_Adapter.csproj", "{D1014F13-0D21-47D4-BE0C-0ED2779F387F}"
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "LadybugTools_Adapter", "LadybugTools_Adapter\LadybugTools_Adapter.csproj", "{FA39A3F6-100F-4824-A91B-A2AEC4660B4E}"
 EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
@@ -15,18 +15,18 @@ Global
 		Release|Any CPU = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(ProjectConfigurationPlatforms) = postSolution
-		{68AB279E-3E50-4EE3-9E9E-C5C100DF5C58}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{68AB279E-3E50-4EE3-9E9E-C5C100DF5C58}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{68AB279E-3E50-4EE3-9E9E-C5C100DF5C58}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{68AB279E-3E50-4EE3-9E9E-C5C100DF5C58}.Release|Any CPU.Build.0 = Release|Any CPU
-		{3778EE3E-F314-4EB5-B99F-CF4A8C254E80}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{3778EE3E-F314-4EB5-B99F-CF4A8C254E80}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{3778EE3E-F314-4EB5-B99F-CF4A8C254E80}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{3778EE3E-F314-4EB5-B99F-CF4A8C254E80}.Release|Any CPU.Build.0 = Release|Any CPU
-		{D1014F13-0D21-47D4-BE0C-0ED2779F387F}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{D1014F13-0D21-47D4-BE0C-0ED2779F387F}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{D1014F13-0D21-47D4-BE0C-0ED2779F387F}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{D1014F13-0D21-47D4-BE0C-0ED2779F387F}.Release|Any CPU.Build.0 = Release|Any CPU
+		{ABC2CD49-3DCF-46C3-9249-EB09C88ECFFD}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{ABC2CD49-3DCF-46C3-9249-EB09C88ECFFD}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{ABC2CD49-3DCF-46C3-9249-EB09C88ECFFD}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{ABC2CD49-3DCF-46C3-9249-EB09C88ECFFD}.Release|Any CPU.Build.0 = Release|Any CPU
+		{0A409572-CA4B-4267-95AD-F9A4A699DB89}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{0A409572-CA4B-4267-95AD-F9A4A699DB89}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{0A409572-CA4B-4267-95AD-F9A4A699DB89}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{0A409572-CA4B-4267-95AD-F9A4A699DB89}.Release|Any CPU.Build.0 = Release|Any CPU
+		{FA39A3F6-100F-4824-A91B-A2AEC4660B4E}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{FA39A3F6-100F-4824-A91B-A2AEC4660B4E}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{FA39A3F6-100F-4824-A91B-A2AEC4660B4E}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{FA39A3F6-100F-4824-A91B-A2AEC4660B4E}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE
diff --git a/LadybugTools_oM/Collections/EPW.cs b/LadybugTools_oM/Collections/EPW.cs
new file mode 100644
index 00000000..bdc506ee
--- /dev/null
+++ b/LadybugTools_oM/Collections/EPW.cs
@@ -0,0 +1,42 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+
+using BH.oM.Base;
+using System.Collections.Generic;
+using System.ComponentModel;
+
+namespace BH.oM.LadybugTools
+{
+    public class EPW : BHoMObject, ILadybugTools
+    {
+        [Description("The Location of this EPW.")]
+        public virtual Location Location { get; set; } = new Location();
+
+        [Description("The data collections within this EPW.")]
+        public virtual List<HourlyContinuousCollection> DataCollections { get; set; } = new List<HourlyContinuousCollection>();
+
+        [Description("Metadata associated with this EPW.")]
+        public virtual Dictionary<string, object> Metadata { get; set; } = new Dictionary<string, object>();
+
+    }
+}
diff --git a/LadybugTools_Engine/Convert/SnakeCaseToPascalCase.cs b/LadybugTools_oM/Collections/HourlyContinuousCollection.cs
similarity index 75%
rename from LadybugTools_Engine/Convert/SnakeCaseToPascalCase.cs
rename to LadybugTools_oM/Collections/HourlyContinuousCollection.cs
index f8f0d89f..bcc75fed 100644
--- a/LadybugTools_Engine/Convert/SnakeCaseToPascalCase.cs
+++ b/LadybugTools_oM/Collections/HourlyContinuousCollection.cs
@@ -20,22 +20,20 @@
  * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
  */
 
-using BH.oM.Base.Attributes;
+
 using System.ComponentModel;
-using System.Threading;
+using System.Collections.Generic;
 using System.Linq;
+using BH.oM.Base;
 
-namespace BH.Engine.LadybugTools
+namespace BH.oM.LadybugTools
 {
-    public static partial class Convert
+    public class HourlyContinuousCollection : BHoMObject, ILadybugTools
     {
-        [Description("Convert the case of the given text.")]
-        [Input("str", "Text in \"snake_case\".")]
-        [Output("str", "Text in \"PascalCase\".")]
-        public static string SnakeCaseToPascalCase(this string str)
-        {
-            return string.Concat(str.Split('_').Select(Thread.CurrentThread.CurrentCulture.TextInfo.ToTitleCase));
-        }
+        [Description("An approximation of a Ladybug Header object.")]
+        public virtual Header Header { get; set; } = new Header();
+
+        [Description("A list of values.")]
+        public virtual List<string> Values { get; set; } = Enumerable.Repeat("0.0", 8760).ToList();
     }
 }
-
diff --git a/LadybugTools_oM/Config/LadybugConfig.cs b/LadybugTools_oM/Config/LadybugConfig.cs
new file mode 100644
index 00000000..63ba99cd
--- /dev/null
+++ b/LadybugTools_oM/Config/LadybugConfig.cs
@@ -0,0 +1,37 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Adapter;
+using System;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Text;
+
+namespace BH.oM.LadybugTools
+{
+    [Description("The action config for the LadybugTools Adapter.")]
+    public class LadybugConfig : ActionConfig
+    {
+        [Description("File settings for the json file to pull/push to.")]
+        public virtual FileSettings JsonFile { get; set; } = null;
+    }
+}
diff --git a/LadybugTools_oM/OpaqueMaterial.cs b/LadybugTools_oM/Constructions/EnergyMaterial.cs
similarity index 71%
rename from LadybugTools_oM/OpaqueMaterial.cs
rename to LadybugTools_oM/Constructions/EnergyMaterial.cs
index 100a0c22..93947ade 100644
--- a/LadybugTools_oM/OpaqueMaterial.cs
+++ b/LadybugTools_oM/Constructions/EnergyMaterial.cs
@@ -21,43 +21,43 @@
  */
 
 
-using BH.oM.Base;
 using System.ComponentModel;
+using BH.oM.Base;
+using BH.oM.Base.Attributes;
+using BH.oM.Quantities.Attributes;
 
 namespace BH.oM.LadybugTools
 {
-    public class OpaqueMaterial : ILadybugToolsMaterial
+    [NoAutoConstructor]
+    public class EnergyMaterial : BHoMObject, IEnergyMaterialOpaque
     {
         [Description("The name of this EnergyMaterial.")]
-        public virtual string Identifier { get; set; } = "Generic Opaque Material";
-        
-        [Description("The source for this object and it's properties.")]
-        public virtual string Source { get; set; } = string.Empty;
-
-        [Description("The roughness of the material.")]
-        public virtual Roughness Roughness { get; set; } = Roughness.MediumRough;
+        public override string Name { get; set; } = string.Empty;
 
         [Description("Thickness of material (m).")]
-        public virtual double Thickness { get; set; } = 0.1;
+        [Length]
+        public virtual double Thickness { get; set; }
 
         [Description("Conductivity of material (W/mK).")]
-        public virtual double Conductivity { get; set; } = 2;
+        public virtual double Conductivity { get; set; }
 
         [Description("Density of material (kg/m3).")]
-        public virtual double Density { get; set; } = 2000;
+        [Density]
+        public virtual double Density { get; set; }
 
         [Description("Specific heat capacity of material (J/kgK).")]
-        public virtual double SpecificHeat { get; set; } = 800;
+        public virtual double SpecificHeat { get; set; }
+
+        [Description("The roughness of the material.")]
+        public virtual Roughness Roughness { get; set; }
 
         [Description("Thermal absorptivity (emissivity) of material (0-1).")]
-        public virtual double ThermalAbsorptance { get; set; } = 0.9;
+        public virtual double ThermalAbsorptance { get; set; }
 
         [Description("Solar absorptivity of material (0-1).")]
-        public virtual double SolarAbsorptance { get; set; } = 0.7;
+        public virtual double SolarAbsorptance { get; set; }
 
         [Description("Light absorptivity (1 - albedo) of material (0-1).")]
-        public virtual double VisibleAbsorptance { get; set; } = 0.7;
+        public virtual double VisibleAbsorptance { get; set; }
     }
 }
-
-
diff --git a/LadybugTools_oM/OpaqueVegetationMaterial.cs b/LadybugTools_oM/Constructions/EnergyMaterialVegetation.cs
similarity index 77%
rename from LadybugTools_oM/OpaqueVegetationMaterial.cs
rename to LadybugTools_oM/Constructions/EnergyMaterialVegetation.cs
index 08dbe9bf..c30f58ac 100644
--- a/LadybugTools_oM/OpaqueVegetationMaterial.cs
+++ b/LadybugTools_oM/Constructions/EnergyMaterialVegetation.cs
@@ -21,57 +21,58 @@
  */
 
 
-using BH.oM.Base;
 using System.ComponentModel;
+using BH.oM.Base;
+using BH.oM.Base.Attributes;
+using BH.oM.Quantities.Attributes;
 
 namespace BH.oM.LadybugTools
 {
-    public class OpaqueVegetationMaterial : ILadybugToolsMaterial
+    [NoAutoConstructor]
+    public class EnergyMaterialVegetation : BHoMObject, IEnergyMaterialOpaque
     {
         [Description("The name of this EnergyMaterialVegetation.")]
-        public virtual string Identifier { get; set; } = "Generic Opaque Vegetation Material";
-        
-        [Description("The source for this object and it's properties.")]
-        public virtual string Source { get; set; } = string.Empty;
-
-        [Description("The roughness of the material.")]
-        public virtual Roughness Roughness { get; set; } = Roughness.MediumRough;
+        public override string Name { get; set; } = string.Empty;
 
         [Description("Thickness of material (m).")]
-        public virtual double Thickness { get; set; } = 0.1;
+        [Length]
+        public virtual double Thickness { get; set; }
 
         [Description("Conductivity of material (W/mK).")]
-        public virtual double Conductivity { get; set; } = 0.35;
+        public virtual double Conductivity { get; set; }
 
         [Description("Density of material (kg/m3).")]
-        public virtual double Density { get; set; } = 1100;
+        [Density]
+        public virtual double Density { get; set; }
 
         [Description("Specific heat capacity of material (J/kgK).")]
-        public virtual double SpecificHeat { get; set; } = 1200;
+        public virtual double SpecificHeat { get; set; }
+
+        [Description("The roughness of the material.")]
+        public virtual Roughness Roughness { get; set; }
 
         [Description("A number between 0 and 1 for the fraction of incident long wavelength radiation that is absorbed by the soil material.")]
-        public virtual double SoilThermalAbsorptance { get; set; } = 0.9;
+        public virtual double SoilThermalAbsorptance { get; set; }
 
         [Description("A number between 0 and 1 for the fraction of incident solar radiation absorbed by the soil material.")]
-        public virtual double SoilSolarAbsorptance { get; set; } = 0.7;
+        public virtual double SoilSolarAbsorptance { get; set; }
 
         [Description("A number between 0 and 1 for the fraction of incident visible wavelength radiation absorbed by the soil material.")]
-        public virtual double SoilVisibleAbsorptance { get; set; } = 0.7;
+        public virtual double SoilVisibleAbsorptance { get; set; }
 
         [Description("A number between 0.005 and 1.0 for the height of plants in the vegetation layer [m].")]
-        public virtual double PlantHeight { get; set; } = 0.2;
+        public virtual double PlantHeight { get; set; }
 
         [Description("A number between 0.001 and 5.0 for the projected leaf area per unit area of soil surface (aka. Leaf Area Index or LAI). Note that the fraction of vegetation cover is calculated directly from LAI using an empirical relation.")]
-        public virtual double LeafAreaIndex { get; set; } = 1.0;
+        public virtual double LeafAreaIndex { get; set; }
 
         [Description("A number between 0.05 and 0.5 for the fraction of incident solar radiation that is reflected by the leaf surfaces. Solar radiation includes the visible spectrum as well as infrared and ultraviolet wavelengths. Typical values are 0.18 to 0.25.")]
-        public virtual double LeafReflectivity { get; set; } = 0.22;
+        public virtual double LeafReflectivity { get; set; }
 
         [Description("A number between 0.8 and 1.0 for the ratio of thermal radiation emitted from leaf surfaces to that emitted by an ideal black body at the same temperature.")]
-        public virtual double LeafEmissivity { get; set; } = 0.95;
+        public virtual double LeafEmissivity { get; set; }
 
         [Description("A number between 50 and 300 for the resistance of the plants to moisture transport [s/m]. Plants with low values of stomatal resistance will result in higher evapotranspiration rates than plants with high resistance.")]
-        public virtual double MinStomatalResist { get; set; } = 180;
+        public virtual double MinimumStomatalResistance { get; set; }
     }
 }
-
diff --git a/LadybugTools_oM/ILadybugToolsMaterial.cs b/LadybugTools_oM/Constructions/IEnergyMaterialOpaque.cs
similarity index 85%
rename from LadybugTools_oM/ILadybugToolsMaterial.cs
rename to LadybugTools_oM/Constructions/IEnergyMaterialOpaque.cs
index 8696d982..3cd8d8f6 100644
--- a/LadybugTools_oM/ILadybugToolsMaterial.cs
+++ b/LadybugTools_oM/Constructions/IEnergyMaterialOpaque.cs
@@ -26,13 +26,8 @@
 
 namespace BH.oM.LadybugTools
 {
-    public interface ILadybugToolsMaterial : IObject
+    [Description("An interface for opaque energy materials.")]
+    public interface IEnergyMaterialOpaque : ILadybugTools
     {
-        [Description("The identifier for this material object.")]
-        string Identifier { get; set; }
-        [Description("The source for this material object.")]
-        string Source { get; set; }
     }
 }
-
-
diff --git a/LadybugTools_oM/Enum/Roughness.cs b/LadybugTools_oM/Enum/Roughness.cs
index f624f09c..bf840ea7 100644
--- a/LadybugTools_oM/Enum/Roughness.cs
+++ b/LadybugTools_oM/Enum/Roughness.cs
@@ -20,8 +20,11 @@
  * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
  */
 
+using System.ComponentModel;
+
 namespace BH.oM.LadybugTools
-{ 
+{
+    [Description("An enum for the roughness of an EnergyMaterial.")]
     public enum Roughness
     {
         Undefined,
diff --git a/LadybugTools_oM/Enum/StringCase.cs b/LadybugTools_oM/Enum/StringCase.cs
new file mode 100644
index 00000000..b26d59b2
--- /dev/null
+++ b/LadybugTools_oM/Enum/StringCase.cs
@@ -0,0 +1,32 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+namespace BH.oM.LadybugTools
+{ 
+    public enum StringCase
+    {
+        Undefined,
+        Camel,
+        Pascal,
+        Snake,
+    };
+}
diff --git a/LadybugTools_oM/ExecuteCommands/GetMaterialCommand.cs b/LadybugTools_oM/ExecuteCommands/GetMaterialCommand.cs
new file mode 100644
index 00000000..f50b2004
--- /dev/null
+++ b/LadybugTools_oM/ExecuteCommands/GetMaterialCommand.cs
@@ -0,0 +1,38 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Adapter;
+using BH.oM.Base;
+using System;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Text;
+
+namespace BH.oM.LadybugTools
+{
+    [Description("Command that when executed with the LadybugTools Adapter, returns a list of materials from the Python Materials list.")]
+    public class GetMaterialCommand : IExecuteCommand, IObject
+    {
+        [Description("Text to filter the resultant list by. Filter applies to the Material Name. Leave blank to return all Materials.")]
+        public virtual string Filter { get; set; } = "";
+    }
+}
diff --git a/LadybugTools_oM/ExecuteCommands/GetTypologyCommand.cs b/LadybugTools_oM/ExecuteCommands/GetTypologyCommand.cs
new file mode 100644
index 00000000..9f2f091a
--- /dev/null
+++ b/LadybugTools_oM/ExecuteCommands/GetTypologyCommand.cs
@@ -0,0 +1,38 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Adapter;
+using BH.oM.Base;
+using System;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Text;
+
+namespace BH.oM.LadybugTools
+{
+    [Description("Command that when executed with the LadybugTools Adapter, returns a list of Typology objects from the Python predefined Typologies list.")]
+    public class GetTypologyCommand : IExecuteCommand, IObject
+    {
+        [Description("Text to filter the resultant list by. Filter applies to the Typology Name. Leave blank to return all Typologies.")]
+        public virtual string Filter { get; set; } = "";
+    }
+}
diff --git a/LadybugTools_oM/ExecuteCommands/RunExternalComfortCommand.cs b/LadybugTools_oM/ExecuteCommands/RunExternalComfortCommand.cs
new file mode 100644
index 00000000..38c52b37
--- /dev/null
+++ b/LadybugTools_oM/ExecuteCommands/RunExternalComfortCommand.cs
@@ -0,0 +1,41 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Adapter;
+using BH.oM.Base;
+using System;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Text;
+
+namespace BH.oM.LadybugTools
+{
+    [Description("Command that when executed with the LadybugTools Adapter, runs an External Comfort simulation and returns an ExternalComfort object containing results.")]
+    public class RunExternalComfortCommand : IExecuteCommand, IObject
+    {
+        [Description("A SimulationResult object generated by the RunSimulation Execute command.")]
+        public virtual SimulationResult SimulationResult { get; set; } = null;
+
+        [Description("A Typology object.")]
+        public virtual Typology Typology { get; set; } = null;
+    }
+}
\ No newline at end of file
diff --git a/LadybugTools_oM/ExecuteCommands/RunSimulationCommand.cs b/LadybugTools_oM/ExecuteCommands/RunSimulationCommand.cs
new file mode 100644
index 00000000..b8d16e47
--- /dev/null
+++ b/LadybugTools_oM/ExecuteCommands/RunSimulationCommand.cs
@@ -0,0 +1,45 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+using BH.oM.Adapter;
+using BH.oM.Base;
+using BH.oM.LadybugTools;
+using System;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Text;
+
+namespace BH.oM.LadybugTools
+{
+    [Description("Command that when executed with the LadybugTools Adapter, runs a simulation and return a SimulationResult containing hourly data.")]
+    public class RunSimulationCommand : IExecuteCommand, IObject
+    {
+        [Description("FileSettings for an EPW file to run the simulation with.")]
+        public virtual FileSettings EpwFile { get; set; } = new FileSettings();
+
+        [Description("The ground material for the simulation to use.")]
+        public virtual IEnergyMaterialOpaque GroundMaterial { get; set; } = null;
+
+        [Description("The shade material for the simulation to use.")]
+        public virtual IEnergyMaterialOpaque ShadeMaterial { get; set; } = null;
+    }
+}
\ No newline at end of file
diff --git a/LadybugTools_oM/Shelter.cs b/LadybugTools_oM/Geometry/Shelter.cs
similarity index 80%
rename from LadybugTools_oM/Shelter.cs
rename to LadybugTools_oM/Geometry/Shelter.cs
index 4fc52748..9c08d102 100644
--- a/LadybugTools_oM/Shelter.cs
+++ b/LadybugTools_oM/Geometry/Shelter.cs
@@ -22,23 +22,23 @@
 
 
 using BH.oM.Base;
+using BH.oM.Base.Attributes;
 using BH.oM.Geometry;
 using System.Collections.Generic;
 using System.ComponentModel;
 
 namespace BH.oM.LadybugTools
 {
-    public class Shelter : BHoMObject
+    [NoAutoConstructor]
+    public class Shelter : BHoMObject, ILadybugTools
     {
-        
-        [Description("Wind porosity (0-1).")]
-        public virtual double WindPorosity { get; set; } = 0.0;
+        [Description("Hourly wind porosity (0-1).")]
+        public virtual List<double> WindPorosity { get; set; }
 
-        [Description("Radiation porosity (0-1).")]
-        public virtual double RadiationPorosity { get; set; } = 0.0;
+        [Description("Hourly radiation porosity (0-1).")]
+        public virtual List<double> RadiationPorosity { get; set; }
 
         [Description("The vertices of this shelter object.")]
-        public virtual List<Point> Vertices { get; set; } = new List<Point>();
+        public virtual List<Point> Vertices { get; set; }
     }
 }
-
diff --git a/LadybugTools_oM/ILadybugTools.cs b/LadybugTools_oM/ILadybugTools.cs
new file mode 100644
index 00000000..d130ecf6
--- /dev/null
+++ b/LadybugTools_oM/ILadybugTools.cs
@@ -0,0 +1,32 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+
+using BH.oM.Base;
+using System.ComponentModel;
+
+namespace BH.oM.LadybugTools
+{
+    public interface ILadybugTools : IBHoMObject
+    {
+    }
+}
diff --git a/LadybugTools_oM/LadybugTools_oM.csproj b/LadybugTools_oM/LadybugTools_oM.csproj
index c11556c3..ee402bf8 100644
--- a/LadybugTools_oM/LadybugTools_oM.csproj
+++ b/LadybugTools_oM/LadybugTools_oM.csproj
@@ -1,114 +1,47 @@
-<?xml version="1.0" encoding="utf-8"?>
-<Project ToolsVersion="15.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
-  <Import Project="$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props" Condition="Exists('$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props')" />
+<Project Sdk="Microsoft.NET.Sdk">
+
   <PropertyGroup>
-    <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
-    <Platform Condition=" '$(Platform)' == '' ">AnyCPU</Platform>
-    <ProjectGuid>{3778EE3E-F314-4EB5-B99F-CF4A8C254E80}</ProjectGuid>
-    <OutputType>Library</OutputType>
-    <RootNamespace>LadybugTools_oM</RootNamespace>
-    <AssemblyName>LadybugTools_oM</AssemblyName>
-    <TargetFrameworkVersion>v4.7.2</TargetFrameworkVersion>
-    <FileAlignment>512</FileAlignment>
-    <AutoGenerateBindingRedirects>true</AutoGenerateBindingRedirects>
-    <Deterministic>true</Deterministic>
-    <PublishUrl>publish\</PublishUrl>
-    <Install>true</Install>
-    <InstallFrom>Disk</InstallFrom>
-    <UpdateEnabled>false</UpdateEnabled>
-    <UpdateMode>Foreground</UpdateMode>
-    <UpdateInterval>7</UpdateInterval>
-    <UpdateIntervalUnits>Days</UpdateIntervalUnits>
-    <UpdatePeriodically>false</UpdatePeriodically>
-    <UpdateRequired>false</UpdateRequired>
-    <MapFileExtensions>true</MapFileExtensions>
-    <ApplicationRevision>0</ApplicationRevision>
-    <ApplicationVersion>1.0.0.%2a</ApplicationVersion>
-    <IsWebBootstrapper>false</IsWebBootstrapper>
-    <UseApplicationTrust>false</UseApplicationTrust>
-    <BootstrapperEnabled>true</BootstrapperEnabled>
-    <TargetFrameworkProfile />
-  </PropertyGroup>
-  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' ">
-    <PlatformTarget>AnyCPU</PlatformTarget>
-    <DebugSymbols>true</DebugSymbols>
-    <DebugType>full</DebugType>
-    <Optimize>false</Optimize>
-    <OutputPath>..\Build\</OutputPath>
-    <DefineConstants>DEBUG;TRACE</DefineConstants>
-    <ErrorReport>prompt</ErrorReport>
-    <WarningLevel>4</WarningLevel>
-  </PropertyGroup>
-  <PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|AnyCPU' ">
-    <PlatformTarget>AnyCPU</PlatformTarget>
-    <DebugType>pdbonly</DebugType>
-    <Optimize>true</Optimize>
+    <TargetFramework>netstandard2.0</TargetFramework>
+    <AssemblyVersion>7.0.0.0</AssemblyVersion>
+    <Description>https://github.com/BHoM/LadybugTools_Toolkit</Description>
+    <Version>6.0.0</Version>
+    <Authors>BHoM</Authors>
+    <Copyright>Copyright © https://github.com/BHoM</Copyright>
+    <RootNamespace>BH.oM.LadybugTools</RootNamespace>
+    <FileVersion>7.0.0.0</FileVersion>
     <OutputPath>..\Build\</OutputPath>
-    <DefineConstants>TRACE</DefineConstants>
-    <ErrorReport>prompt</ErrorReport>
-    <WarningLevel>4</WarningLevel>
-  </PropertyGroup>
-  <PropertyGroup>
-    <StartupObject />
   </PropertyGroup>
+
   <ItemGroup>
+    <Reference Include="Adapter_oM">
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Adapter_oM.dll</HintPath>
+      <Private>False</Private>
+      <SpecificVersion>False</SpecificVersion>
+    </Reference>
     <Reference Include="BHoM">
       <HintPath>$(ProgramData)\BHoM\Assemblies\BHoM.dll</HintPath>
       <Private>False</Private>
       <SpecificVersion>False</SpecificVersion>
     </Reference>
     <Reference Include="Environment_oM">
-      <SpecificVersion>False</SpecificVersion>
       <HintPath>$(ProgramData)\BHoM\Assemblies\Environment_oM.dll</HintPath>
       <Private>False</Private>
+      <SpecificVersion>False</SpecificVersion>
     </Reference>
     <Reference Include="Geometry_oM">
       <HintPath>$(ProgramData)\BHoM\Assemblies\Geometry_oM.dll</HintPath>
       <Private>False</Private>
       <SpecificVersion>False</SpecificVersion>
     </Reference>
-    <Reference Include="Newtonsoft.Json, Version=13.0.0.0, Culture=neutral, PublicKeyToken=30ad4fe6b2a6aeed, processorArchitecture=MSIL">
-      <HintPath>..\packages\Newtonsoft.Json.13.0.1\lib\net45\Newtonsoft.Json.dll</HintPath>
+    <Reference Include="Quantities_oM">
+      <HintPath>$(ProgramData)\BHoM\Assemblies\Quantities_oM.dll</HintPath>
+      <Private>False</Private>
+      <SpecificVersion>False</SpecificVersion>
     </Reference>
-    <Reference Include="System" />
-    <Reference Include="System.Core" />
-    <Reference Include="System.Xml.Linq" />
-    <Reference Include="System.Data.DataSetExtensions" />
-    <Reference Include="Microsoft.CSharp" />
-    <Reference Include="System.Data" />
-    <Reference Include="System.Net.Http" />
-    <Reference Include="System.Xml" />
-  </ItemGroup>
-  <ItemGroup>
-    <Compile Include="ILadybugToolsMaterial.cs" />
-    <Compile Include="OpaqueVegetationMaterial.cs" />
-    <Compile Include="Enum\Roughness.cs" />
-    <Compile Include="OpaqueMaterial.cs" />
-    <Compile Include="ExternalComfort.cs" />
-    <Compile Include="SimulationResult.cs" />
-    <Compile Include="Typology.cs" />
-    <Compile Include="Shelter.cs" />
-    <Compile Include="Properties\AssemblyInfo.cs" />
-  </ItemGroup>
-  <ItemGroup>
-    <None Include="packages.config" />
-  </ItemGroup>
-  <ItemGroup>
-    <BootstrapperPackage Include=".NETFramework,Version=v4.5.2">
-      <Visible>False</Visible>
-      <ProductName>Microsoft .NET Framework 4.5.2 %28x86 and x64%29</ProductName>
-      <Install>true</Install>
-    </BootstrapperPackage>
-    <BootstrapperPackage Include="Microsoft.Net.Framework.3.5.SP1">
-      <Visible>False</Visible>
-      <ProductName>.NET Framework 3.5 SP1</ProductName>
-      <Install>false</Install>
-    </BootstrapperPackage>
   </ItemGroup>
-  <ItemGroup />
-  <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
-  <PropertyGroup>
-    <PostBuildEvent>
-      xcopy "$(TargetDir)$(TargetFileName)"  "C:\ProgramData\BHoM\Assemblies" /C /Y</PostBuildEvent>
-  </PropertyGroup>
-</Project>
\ No newline at end of file
+
+  <Target Name="PostBuild" AfterTargets="PostBuildEvent">
+    <Exec Command="xcopy $(TargetDir)$(TargetFileName) $(ProgramData)\BHoM\Assemblies /Y" />
+  </Target>
+
+</Project>
diff --git a/LadybugTools_oM/MetaData/AnalysisPeriod.cs b/LadybugTools_oM/MetaData/AnalysisPeriod.cs
new file mode 100644
index 00000000..cb1becd3
--- /dev/null
+++ b/LadybugTools_oM/MetaData/AnalysisPeriod.cs
@@ -0,0 +1,57 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+
+using BH.oM.Base;
+using BH.oM.Base.Attributes;
+using System.ComponentModel;
+
+namespace BH.oM.LadybugTools
+{
+    [NoAutoConstructor]
+    public class AnalysisPeriod : BHoMObject, ILadybugTools
+    {
+        [Description("The start month.")]
+        public virtual int StartMonth { get; set; } = 1;
+
+        [Description("The start day.")]
+        public virtual int StartDay { get; set; } = 1;
+
+        [Description("The start hour.")]
+        public virtual int StartHour { get; set; } = 0;
+
+        [Description("The end month.")]
+        public virtual int EndMonth { get; set; } = 12;
+
+        [Description("The end day.")]
+        public virtual int EndDay { get; set; } = 31;
+
+        [Description("The end hour.")]
+        public virtual int EndHour { get; set; } = 23;
+
+        [Description("Boolean flag for whether this represents a leap year.")]
+        public virtual bool IsLeapYear { get; set; } = false;
+
+        [Description("The number of timesteps per hour.")]
+        public virtual int TimeStep { get; set; } = 1;
+    }
+}
diff --git a/LadybugTools_oM/MetaData/DataType.cs b/LadybugTools_oM/MetaData/DataType.cs
new file mode 100644
index 00000000..98f066f4
--- /dev/null
+++ b/LadybugTools_oM/MetaData/DataType.cs
@@ -0,0 +1,41 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+
+using BH.oM.Base;
+using System.ComponentModel;
+
+namespace BH.oM.LadybugTools
+{
+    public class DataType : BHoMObject, ILadybugTools
+    {
+        
+        [Description("The name of this data type.")]
+        public override string Name { get; set; } = string.Empty;
+        
+        [Description("The data type of this data type.")]
+        public virtual string Data_Type { get; set; } = string.Empty;
+
+        [Description(@"The base type of this data type. This is used if Data_Type is set to ""GenericDataType"".")]
+        public virtual string BaseUnit { get; set; } = string.Empty;
+    }
+}
diff --git a/LadybugTools_oM/MetaData/Header.cs b/LadybugTools_oM/MetaData/Header.cs
new file mode 100644
index 00000000..c6f75af2
--- /dev/null
+++ b/LadybugTools_oM/MetaData/Header.cs
@@ -0,0 +1,44 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+
+using BH.oM.Base;
+using System.Collections.Generic;
+using System.ComponentModel;
+
+namespace BH.oM.LadybugTools
+{
+    public class Header : BHoMObject, ILadybugTools
+    {
+        [Description("The data type the data associated with this header object represents.")]
+        public virtual DataType DataType { get; set; } = new DataType();
+        
+        [Description("The unit for this header object.")]
+        public virtual string Unit { get; set; } = string.Empty;
+        
+        [Description("The analysis period associated with this header object.")]
+        public virtual AnalysisPeriod AnalysisPeriod { get; set; } = new AnalysisPeriod();
+
+        [Description("The metadata associated with this header object.")]
+        public virtual Dictionary<string, object> Metadata { get; set; } = new Dictionary<string, object>();
+    }
+}
diff --git a/LadybugTools_oM/MetaData/Location.cs b/LadybugTools_oM/MetaData/Location.cs
new file mode 100644
index 00000000..95e93555
--- /dev/null
+++ b/LadybugTools_oM/MetaData/Location.cs
@@ -0,0 +1,59 @@
+/*
+ * This file is part of the Buildings and Habitats object Model (BHoM)
+ * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
+ *
+ * Each contributor holds copyright over their respective contributions.
+ * The project versioning (Git) records all such contribution source information.
+ *                                           
+ *                                                                              
+ * The BHoM is free software: you can redistribute it and/or modify         
+ * it under the terms of the GNU Lesser General Public License as published by  
+ * the Free Software Foundation, either version 3.0 of the License, or          
+ * (at your option) any later version.                                          
+ *                                                                              
+ * The BHoM is distributed in the hope that it will be useful,              
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of               
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
+ * GNU Lesser General Public License for more details.                          
+ *                                                                            
+ * You should have received a copy of the GNU Lesser General Public License     
+ * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
+ */
+
+
+using System.ComponentModel;
+using BH.oM.Base;
+using BH.oM.Base.Attributes;
+
+namespace BH.oM.LadybugTools
+{
+    public class Location : BHoMObject, ILadybugTools
+    {
+        [Description("The City of this object.")]
+        public virtual string City { get; set; } = "";
+
+        [Description("The State of this object.")]
+        public virtual string State { get; set; } = "";
+
+        [Description("The Country of this object.")]
+        public virtual string Country { get; set; } = "";
+
+        [Description("The Latitude of this object.")]
+        public virtual double Latitude { get; set; } = 0;
+
+        [Description("The Longitude of this object.")]
+        public virtual double Longitude { get; set; } = 0;
+
+        [Description("The Time Zone of this object.")]
+        public virtual double TimeZone { get; set; } = 0;
+
+        [Description("The Elevation (in m) of this object.")]
+        public virtual double Elevation { get; set; } = 0;
+
+        [Description("The Station ID of this object.")]
+        public virtual string StationId { get; set; } = "";
+
+        [Description("The Source of this object.")]
+        public virtual string Source { get; set; } = "";
+    }
+}
diff --git a/LadybugTools_oM/Properties/AssemblyInfo.cs b/LadybugTools_oM/Properties/AssemblyInfo.cs
deleted file mode 100644
index a08172d7..00000000
--- a/LadybugTools_oM/Properties/AssemblyInfo.cs
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * This file is part of the Buildings and Habitats object Model (BHoM)
- * Copyright (c) 2015 - 2023, the respective contributors. All rights reserved.
- *
- * Each contributor holds copyright over their respective contributions.
- * The project versioning (Git) records all such contribution source information.
- *                                           
- *                                                                              
- * The BHoM is free software: you can redistribute it and/or modify         
- * it under the terms of the GNU Lesser General Public License as published by  
- * the Free Software Foundation, either version 3.0 of the License, or          
- * (at your option) any later version.                                          
- *                                                                              
- * The BHoM is distributed in the hope that it will be useful,              
- * but WITHOUT ANY WARRANTY; without even the implied warranty of               
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the                 
- * GNU Lesser General Public License for more details.                          
- *                                                                            
- * You should have received a copy of the GNU Lesser General Public License     
- * along with this code. If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.      
- */
-
-using System.Reflection;
-using System.Runtime.CompilerServices;
-using System.Runtime.InteropServices;
-
-// General Information about an assembly is controlled through the following
-// set of attributes. Change these attribute values to modify the information
-// associated with an assembly.
-[assembly: AssemblyTitle("LadybugTools_oM")]
-[assembly: AssemblyDescription("https://github.com/BHoM/LadybugTools_Toolkit")]
-[assembly: AssemblyConfiguration("")]
-[assembly: AssemblyCompany("")]
-[assembly: AssemblyProduct("LadybugTools_oM")]
-[assembly: AssemblyCopyright("Copyright ©  2021")]
-[assembly: AssemblyTrademark("")]
-[assembly: AssemblyCulture("")]
-
-// Setting ComVisible to false makes the types in this assembly not visible
-// to COM components.  If you need to access a type in this assembly from
-// COM, set the ComVisible attribute to true on that type.
-[assembly: ComVisible(false)]
-
-// The following GUID is for the ID of the typelib if this project is exposed to COM
-[assembly: Guid("3778ee3e-f314-4eb5-b99f-cf4a8c254e80")]
-
-// Version information for an assembly consists of the following four values:
-//
-//      Major Version
-//      Minor Version
-//      Build Number
-//      Revision
-//
-// You can specify all the values or you can default the Build and Revision Numbers
-// by using the '*' as shown below:
-// [assembly: AssemblyVersion("1.0.*")]
-[assembly: AssemblyVersion("6.0.0.0")]
-[assembly: AssemblyFileVersion("6.3.0.0")]
-
diff --git a/LadybugTools_oM/ExternalComfort.cs b/LadybugTools_oM/Simulation/ExternalComfort.cs
similarity index 73%
rename from LadybugTools_oM/ExternalComfort.cs
rename to LadybugTools_oM/Simulation/ExternalComfort.cs
index 672d1c29..2be8884d 100644
--- a/LadybugTools_oM/ExternalComfort.cs
+++ b/LadybugTools_oM/Simulation/ExternalComfort.cs
@@ -22,29 +22,35 @@
 
 
 using BH.oM.Base;
+using BH.oM.Base.Attributes;
 using System.ComponentModel;
 
 namespace BH.oM.LadybugTools
 {
-    public class ExternalComfort : BHoMObject
+    [NoAutoConstructor]
+    public class ExternalComfort : BHoMObject, ILadybugTools
     {
         [Description("The SimulationResult associated with this object.")]
-        public virtual SimulationResult SimulationResult { get; set; } = new SimulationResult();
+        public virtual SimulationResult SimulationResult { get; set; }
+
         [Description("The typology in the processing of this object.")]
-        public virtual Typology Typology { get; set; } = new Typology();
+        public virtual Typology Typology { get; set; }
 
         // simulated properties
 
         [Description("The calculated property from this object.")]
-        public virtual CustomObject DryBulbTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection DryBulbTemperature { get; set; }
+
         [Description("The calculated property from this object.")]
-        public virtual CustomObject RelativeHumidity { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection RelativeHumidity { get; set; }
+
         [Description("The calculated property from this object.")]
-        public virtual CustomObject WindSpeed { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection WindSpeed { get; set; }
+
         [Description("The calculated property from this object.")]
-        public virtual CustomObject MeanRadiantTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection MeanRadiantTemperature { get; set; }
+
         [Description("The calculated property from this object.")]
-        public virtual CustomObject UniversalThermalClimateIndex { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection UniversalThermalClimateIndex { get; set; }
     }
 }
-
diff --git a/LadybugTools_oM/SimulationResult.cs b/LadybugTools_oM/Simulation/SimulationResult.cs
similarity index 62%
rename from LadybugTools_oM/SimulationResult.cs
rename to LadybugTools_oM/Simulation/SimulationResult.cs
index 59669831..c34b0c89 100644
--- a/LadybugTools_oM/SimulationResult.cs
+++ b/LadybugTools_oM/Simulation/SimulationResult.cs
@@ -22,47 +22,62 @@
 
 
 using BH.oM.Base;
+using BH.oM.Base.Attributes;
 using System.ComponentModel;
 
 namespace BH.oM.LadybugTools
 {
-    public class SimulationResult : BHoMObject
+    [NoAutoConstructor]
+    public class SimulationResult : BHoMObject, ILadybugTools
     {
         [Description("The EPW file associated with this object.")]
-        public virtual string EpwFile { get; set; } = string.Empty;
+        public virtual string EpwFile { get; set; }
+
         [Description("The ground material used in the processing of this object.")]
-        public virtual ILadybugToolsMaterial GroundMaterial { get; set; } = new OpaqueMaterial();
+        public virtual IEnergyMaterialOpaque GroundMaterial { get; set; }
+
         [Description("The shade material used in the processing of this object.")]
-        public virtual ILadybugToolsMaterial ShadeMaterial { get; set; } = new OpaqueMaterial();
+        public virtual IEnergyMaterialOpaque ShadeMaterial { get; set; }
+
         [Description("The identifier used to distinguish existing results for this object.")]
-        public virtual string Identifier { get; set; } = string.Empty;
+        public override string Name { get; set; }
 
         // simulated properties
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject ShadedDownTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection ShadedDownTemperature { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject ShadedUpTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection ShadedUpTemperature { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject ShadedRadiantTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection ShadedRadiantTemperature { get; set; } = null;     
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject ShadedLongwaveMeanRadiantTemperatureDelta { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection ShadedLongwaveMeanRadiantTemperatureDelta { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject ShadedShortwaveMeanRadiantTemperatureDelta { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection ShadedShortwaveMeanRadiantTemperatureDelta { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject ShadedMeanRadiantTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection ShadedMeanRadiantTemperature { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject UnshadedDownTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection UnshadedDownTemperature { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject UnshadedUpTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection UnshadedUpTemperature { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject UnshadedRadiantTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection UnshadedRadiantTemperature { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject UnshadedLongwaveMeanRadiantTemperatureDelta { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection UnshadedLongwaveMeanRadiantTemperatureDelta { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject UnshadedShortwaveMeanRadiantTemperatureDelta { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection UnshadedShortwaveMeanRadiantTemperatureDelta { get; set; } = null;
+
         [Description("The simulated property from this object.")]
-        public virtual CustomObject UnshadedMeanRadiantTemperature { get; set; } = new CustomObject();
+        public virtual HourlyContinuousCollection UnshadedMeanRadiantTemperature { get; set; } = null;
     }
 }
-
-
diff --git a/LadybugTools_oM/Typology.cs b/LadybugTools_oM/Simulation/Typology.cs
similarity index 85%
rename from LadybugTools_oM/Typology.cs
rename to LadybugTools_oM/Simulation/Typology.cs
index f058d2d9..377755d7 100644
--- a/LadybugTools_oM/Typology.cs
+++ b/LadybugTools_oM/Simulation/Typology.cs
@@ -30,19 +30,21 @@
 namespace BH.oM.LadybugTools
 {
     [NoAutoConstructor]
-    public class Typology : BHoMObject
+    public class Typology : BHoMObject, ILadybugTools
     {
         [Description("The name of this Typology.")]
-        public override string Name { get; set; } = "GenericTypology";
+        public override string Name { get; set; } = string.Empty;
+
         [Description("The shelters for this Typology.")]
         public virtual List<Shelter> Shelters { get; set; } = new List<Shelter>();
+        
         [Description("The proportion of evaporative cooling to add to this Typology.")]
-        public virtual List<double> EvaporativeCoolingEffect { get; set; } = Enumerable.Repeat(0.0, 8760).ToList();
+        public virtual List<double> EvaporativeCoolingEffect { get; set; } = Enumerable.Repeat<double>(0.0, 8760).ToList();
+        
         [Description("A multiplier to apply to the wind speed component of this Typology.")]
-        public virtual List<double> WindSpeedMultiplier { get; set; } = Enumerable.Repeat(1.0, 8760).ToList();
+        public virtual List<double?> TargetWindSpeed { get; set; } = Enumerable.Repeat<double?>(null, 8760).ToList();
+        
         [Description("A reduction or increase in MRT to be applied to results generated using this Typology.")]
-        public virtual List<double> RadiantTemperatureAdjustment { get; set; } = Enumerable.Repeat(0.0, 8760).ToList();
+        public virtual List<double> RadiantTemperatureAdjustment { get; set; } = Enumerable.Repeat<double>(0.0, 8760).ToList();
     }
 }
-
-
diff --git a/LadybugTools_oM/Versioning_70.json b/LadybugTools_oM/Versioning_70.json
new file mode 100644
index 00000000..5bc6665e
--- /dev/null
+++ b/LadybugTools_oM/Versioning_70.json
@@ -0,0 +1,24 @@
+{
+  "Type": {
+    "ToNew": {
+      "BH.oM.LadybugTools.OpaqueMaterial": "BH.oM.LadybugTools.EnergyMaterial",
+      "BH.oM.LadybugTools.OpaqueVegetationMaterial": "BH.oM.LadybugTools.EnergyMaterialVegetation",
+      "BH.oM.LadybugTools.ILadybugToolsMaterial": "BH.oM.LadybugTools.IEnergyMaterialOpaque"
+    },
+    "ToOld": {
+      "BH.oM.LadybugTools.EnergyMaterial": "BH.oM.LadybugTools.OpaqueMaterial",
+      "BH.oM.LadybugTools.EnergyMaterialVegetation": "BH.oM.LadybugTools.OpaqueVegetationMaterial",
+      "BH.oM.LadybugTools.IEnergyMaterialOpaque": "BH.oM.LadybugTools.ILadybugToolsMaterial"
+    }
+  },
+  "Property": {
+    "ToNew": {
+      "BH.oM.LadybugTools.Typology.WindSpeedMultiplier": "BH.oM.LadybugTools.Typology.TargetWindSpeed",
+      "BH.oM.LadybugTools.SimulationResult.Identifier": "BH.oM.LadybugTools.SimulationResult.Name"
+    },
+    "ToOld": {
+      "BH.oM.LadybugTools.Typology.TargetWindSpeed": "BH.oM.LadybugTools.Typology.WindSpeedMultiplier",
+      "BH.oM.LadybugTools.SimulationResult.Name": "BH.oM.LadybugTools.SimulationResult.Identifier"
+    }
+  }
+}
\ No newline at end of file
diff --git a/LadybugTools_oM/packages.config b/LadybugTools_oM/packages.config
deleted file mode 100644
index 5eaa239b..00000000
--- a/LadybugTools_oM/packages.config
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<packages>
-  <package id="Newtonsoft.Json" version="13.0.1" targetFramework="net472" />
-</packages>
\ No newline at end of file
diff --git a/README.md b/README.md
index fd40669e..2259cb40 100644
--- a/README.md
+++ b/README.md
@@ -19,12 +19,12 @@ Grab the [latest installer](https://bhom.xyz/) and a selection of [sample script
 ## Getting Started for Developers 🤖 
 
 If you want to build the BHoM and the Toolkits from source, it's hopefully easy! 😄 
-Do take a look at our specific wiki pages here: [Getting Started for Developers](https://github.com/BHoM/documentation/wiki/Getting-started-for-developers)
+Do take a look at our specific wiki pages here: [Getting Started for Developers](https://bhom.xyz/documentation/Contributing/Getting-started-for-developers/)
 
 
 ## Want to Contribute? ##
 
-BHoM is an open-source project and would be nothing without its community. Take a look at our contributing guidelines and tips [here](https://github.com/BHoM/BHoM/blob/master/CONTRIBUTING.md).
+BHoM is an open-source project and would be nothing without its community. Take a look at our contributing guidelines and tips [here](https://github.com/BHoM/BHoM/blob/main/CONTRIBUTING.md).
 
 
 ## Licence ##
@@ -32,4 +32,4 @@ BHoM is an open-source project and would be nothing without its community. Take
 BHoM is free software licenced under GNU Lesser General Public Licence - [https://www.gnu.org/licenses/lgpl-3.0.html](https://www.gnu.org/licenses/lgpl-3.0.html)  
 Each contributor holds copyright over their respective contributions.
 The project versioning (Git) records all such contribution source information.
-See [LICENSE](https://github.com/BHoM/BHoM/blob/master/LICENSE) and [COPYRIGHT_HEADER](https://github.com/BHoM/BHoM/blob/master/COPYRIGHT_HEADER.txt).
+See [LICENSE](https://github.com/BHoM/BHoM/blob/main/LICENSE) and [COPYRIGHT_HEADER](https://github.com/BHoM/BHoM/blob/main/COPYRIGHT_HEADER.txt).
diff --git a/dependencies.txt b/dependencies.txt
index 7edef084..311d7f1a 100644
--- a/dependencies.txt
+++ b/dependencies.txt
@@ -1,5 +1,4 @@
 BHoM/BHoM
 BHoM/BHoM_Engine
 BHoM/BHoM_Adapter
-BHoM/Rhinoceros_Toolkit
 BHoM/Python_Toolkit
\ No newline at end of file