diff --git a/DebyeCalculator/debye_calculator.py b/DebyeCalculator/debye_calculator.py
index dcd3863..87e483e 100644
--- a/DebyeCalculator/debye_calculator.py
+++ b/DebyeCalculator/debye_calculator.py
@@ -20,11 +20,11 @@
 from ase.build import make_supercell
 from ase.build.tools import sort as ase_sort
 
-from profiling import Profiler
+from utility.profiling import Profiler
 
 import ipywidgets as widgets
-from IPython.display import display, HTML, clear_output, Math
-from ipywidgets import interact, interact_manual, HBox, VBox, Layout
+from IPython.display import display, HTML, clear_output
+from ipywidgets import HBox, VBox, Layout
 from tqdm.auto import tqdm
 
 import collections
@@ -93,8 +93,6 @@ def __init__(
         self.biso = biso
 
         # Initialise ranges
-        #self.q = torch.linspace(self.qmin, self.qmax-self.qstep, int((self.qmax+self.qstep) / self.qstep)).unsqueeze(-1).to(device=self.device)
-        #self.r = torch.linspace(self.rmin, self.rmax-self.rstep, int((self.rmax+self.rstep) / self.rstep)).unsqueeze(-1).to(device=self.device)
         self.q = torch.arange(self.qmin, self.qmax, self.qstep).unsqueeze(-1).to(device=self.device)
         self.r = torch.arange(self.rmin, self.rmax, self.rstep).unsqueeze(-1).to(device=self.device)
 
@@ -143,10 +141,6 @@ def update_parameters(
             
         # Re-initialise ranges
         if np.any([k in ['qmin','qmax','qstep','rmin', 'rmax', 'rstep'] for k in kwargs.keys()]):
-            #self.q = torch.linspace(self.qmin, self.qmax-self.qstep, int(self.qmax / self.qstep)).unsqueeze(-1).to(device=self.device)
-            #self.r = torch.linspace(self.rmin, self.rmax-self.rstep, int(self.rmax / self.rstep)).unsqueeze(-1).to(device=self.device)
-            #self.q = torch.linspace(self.qmin, self.qmax-self.qstep, int((self.qmax+self.qstep) / self.qstep)).unsqueeze(-1).to(device=self.device)
-            #self.r = torch.linspace(self.rmin, self.rmax-self.rstep, int((self.rmax+self.rstep) / self.rstep)).unsqueeze(-1).to(device=self.device)
             self.q = torch.arange(self.qmin, self.qmax, self.qstep).unsqueeze(-1).to(device=self.device)
             self.r = torch.arange(self.rmin, self.rmax, self.rstep).unsqueeze(-1).to(device=self.device)
 
@@ -729,9 +723,9 @@ def interact(
         radiation_type = self.radiation_type
         profile = False
 
-        with open('images/choose_hardware.png', 'rb') as f:
+        with open('display_assets/choose_hardware.png', 'rb') as f:
             choose_hardware_img = f.read()
-        with open('images/batch_size.png', 'rb') as f:
+        with open('display_assets/batch_size.png', 'rb') as f:
             batch_size_img = f.read()
         
         """ Utility widgets """
@@ -742,16 +736,16 @@ def interact(
 
         """ File Selection Tab """
         
-        # Load diplay images
-        with open('images/enter_path.png', 'rb') as f:
+        # Load diplay display_assets
+        with open('display_assets/enter_path.png', 'rb') as f:
             enter_path_img = f.read()
-        with open('images/select_files.png', 'rb') as f:
+        with open('display_assets/select_files.png', 'rb') as f:
             select_files_img = f.read()
-        with open('images/radius_a.png', 'rb') as f:
+        with open('display_assets/radius_a.png', 'rb') as f:
             radius_a_img = f.read()
-        with open('images/file_1.png', 'rb') as f:
+        with open('display_assets/file_1.png', 'rb') as f:
             file_1_img = f.read()
-        with open('images/file_2.png', 'rb') as f:
+        with open('display_assets/file_2.png', 'rb') as f:
             file_2_img = f.read()
 
         # Layout
@@ -823,30 +817,30 @@ def interact(
         
         """ Scattering Options Tab """
 
-        # Load images
-        with open('images/qslider.png', 'rb') as f:
+        # Load display_assets
+        with open('display_assets/qslider.png', 'rb') as f:
             qslider_img = f.read()
-        with open('images/rslider.png', 'rb') as f:
+        with open('display_assets/rslider.png', 'rb') as f:
             rslider_img = f.read()
-        with open('images/qdamp.png', 'rb') as f:
+        with open('display_assets/qdamp.png', 'rb') as f:
             qdamp_img = f.read()
-        with open('images/global_biso.png', 'rb') as f:
+        with open('display_assets/global_biso.png', 'rb') as f:
             global_biso_img = f.read()
-        with open('images/a.png', 'rb') as f:
+        with open('display_assets/a.png', 'rb') as f:
             a_img = f.read()
-        with open('images/a_inv.png', 'rb') as f:
+        with open('display_assets/a_inv.png', 'rb') as f:
             a_inv_img = f.read()
-        with open('images/a_sq.png', 'rb') as f:
+        with open('display_assets/a_sq.png', 'rb') as f:
             a_sq_img = f.read()
-        with open('images/qstep.png', 'rb') as f:
+        with open('display_assets/qstep.png', 'rb') as f:
             qstep_img = f.read()
-        with open('images/rstep.png', 'rb') as f:
+        with open('display_assets/rstep.png', 'rb') as f:
             rstep_img = f.read()
-        with open('images/rthres.png', 'rb') as f:
+        with open('display_assets/rthres.png', 'rb') as f:
             rthres_img = f.read()
-        with open('images/radiation_type.png', 'rb') as f:
+        with open('display_assets/radiation_type.png', 'rb') as f:
             radiation_type_img = f.read()
-        with open('images/scattering_parameters.png', 'rb') as f:
+        with open('display_assets/scattering_parameters.png', 'rb') as f:
             scattering_parameters_img = f.read()
 
         # Radiation 
@@ -1011,20 +1005,20 @@ def interact(
 
         """ Plotting Options """
 
-        # Load display images
-        with open('images/iq_scaling.png', 'rb') as f:
+        # Load display display_assets
+        with open('display_assets/iq_scaling.png', 'rb') as f:
             iq_scaling_img = f.read()
-        with open('images/show_hide.png', 'rb') as f:
+        with open('display_assets/show_hide.png', 'rb') as f:
             show_hide_img = f.read()
-        with open('images/max_norm.png', 'rb') as f:
+        with open('display_assets/max_norm.png', 'rb') as f:
             max_norm_img = f.read()
-        with open('images/iq.png', 'rb') as f:
+        with open('display_assets/iq.png', 'rb') as f:
             iq_img = f.read()
-        with open('images/sq.png', 'rb') as f:
+        with open('display_assets/sq.png', 'rb') as f:
             sq_img = f.read()
-        with open('images/fq.png', 'rb') as f:
+        with open('display_assets/fq.png', 'rb') as f:
             fq_img = f.read()
-        with open('images/gr.png', 'rb') as f:
+        with open('display_assets/gr.png', 'rb') as f:
             gr_img = f.read()
         
         # Y-axis I(Q) scale button
diff --git a/DebyeCalculator/submodules/images/a.png b/DebyeCalculator/display_assets/a.png
similarity index 100%
rename from DebyeCalculator/submodules/images/a.png
rename to DebyeCalculator/display_assets/a.png
diff --git a/DebyeCalculator/submodules/images/a_inv.png b/DebyeCalculator/display_assets/a_inv.png
similarity index 100%
rename from DebyeCalculator/submodules/images/a_inv.png
rename to DebyeCalculator/display_assets/a_inv.png
diff --git a/DebyeCalculator/submodules/images/a_sq.png b/DebyeCalculator/display_assets/a_sq.png
similarity index 100%
rename from DebyeCalculator/submodules/images/a_sq.png
rename to DebyeCalculator/display_assets/a_sq.png
diff --git a/DebyeCalculator/submodules/images/batch_size.png b/DebyeCalculator/display_assets/batch_size.png
similarity index 100%
rename from DebyeCalculator/submodules/images/batch_size.png
rename to DebyeCalculator/display_assets/batch_size.png
diff --git a/DebyeCalculator/submodules/images/choose_hardware.png b/DebyeCalculator/display_assets/choose_hardware.png
similarity index 100%
rename from DebyeCalculator/submodules/images/choose_hardware.png
rename to DebyeCalculator/display_assets/choose_hardware.png
diff --git a/DebyeCalculator/submodules/images/enter_path.png b/DebyeCalculator/display_assets/enter_path.png
similarity index 100%
rename from DebyeCalculator/submodules/images/enter_path.png
rename to DebyeCalculator/display_assets/enter_path.png
diff --git a/DebyeCalculator/submodules/images/file_1.png b/DebyeCalculator/display_assets/file_1.png
similarity index 100%
rename from DebyeCalculator/submodules/images/file_1.png
rename to DebyeCalculator/display_assets/file_1.png
diff --git a/DebyeCalculator/submodules/images/file_2.png b/DebyeCalculator/display_assets/file_2.png
similarity index 100%
rename from DebyeCalculator/submodules/images/file_2.png
rename to DebyeCalculator/display_assets/file_2.png
diff --git a/DebyeCalculator/submodules/images/fq.png b/DebyeCalculator/display_assets/fq.png
similarity index 100%
rename from DebyeCalculator/submodules/images/fq.png
rename to DebyeCalculator/display_assets/fq.png
diff --git a/DebyeCalculator/submodules/images/global_biso.png b/DebyeCalculator/display_assets/global_biso.png
similarity index 100%
rename from DebyeCalculator/submodules/images/global_biso.png
rename to DebyeCalculator/display_assets/global_biso.png
diff --git a/DebyeCalculator/submodules/images/gr.png b/DebyeCalculator/display_assets/gr.png
similarity index 100%
rename from DebyeCalculator/submodules/images/gr.png
rename to DebyeCalculator/display_assets/gr.png
diff --git a/DebyeCalculator/submodules/images/iq.png b/DebyeCalculator/display_assets/iq.png
similarity index 100%
rename from DebyeCalculator/submodules/images/iq.png
rename to DebyeCalculator/display_assets/iq.png
diff --git a/DebyeCalculator/submodules/images/iq_scaling.png b/DebyeCalculator/display_assets/iq_scaling.png
similarity index 100%
rename from DebyeCalculator/submodules/images/iq_scaling.png
rename to DebyeCalculator/display_assets/iq_scaling.png
diff --git a/DebyeCalculator/submodules/images/max_norm.png b/DebyeCalculator/display_assets/max_norm.png
similarity index 100%
rename from DebyeCalculator/submodules/images/max_norm.png
rename to DebyeCalculator/display_assets/max_norm.png
diff --git a/DebyeCalculator/submodules/images/qdamp.png b/DebyeCalculator/display_assets/qdamp.png
similarity index 100%
rename from DebyeCalculator/submodules/images/qdamp.png
rename to DebyeCalculator/display_assets/qdamp.png
diff --git a/DebyeCalculator/submodules/images/qslider.png b/DebyeCalculator/display_assets/qslider.png
similarity index 100%
rename from DebyeCalculator/submodules/images/qslider.png
rename to DebyeCalculator/display_assets/qslider.png
diff --git a/DebyeCalculator/submodules/images/qstep.png b/DebyeCalculator/display_assets/qstep.png
similarity index 100%
rename from DebyeCalculator/submodules/images/qstep.png
rename to DebyeCalculator/display_assets/qstep.png
diff --git a/DebyeCalculator/submodules/images/radiation_type.png b/DebyeCalculator/display_assets/radiation_type.png
similarity index 100%
rename from DebyeCalculator/submodules/images/radiation_type.png
rename to DebyeCalculator/display_assets/radiation_type.png
diff --git a/DebyeCalculator/submodules/images/radius_a.png b/DebyeCalculator/display_assets/radius_a.png
similarity index 100%
rename from DebyeCalculator/submodules/images/radius_a.png
rename to DebyeCalculator/display_assets/radius_a.png
diff --git a/DebyeCalculator/submodules/images/rslider.png b/DebyeCalculator/display_assets/rslider.png
similarity index 100%
rename from DebyeCalculator/submodules/images/rslider.png
rename to DebyeCalculator/display_assets/rslider.png
diff --git a/DebyeCalculator/submodules/images/rstep.png b/DebyeCalculator/display_assets/rstep.png
similarity index 100%
rename from DebyeCalculator/submodules/images/rstep.png
rename to DebyeCalculator/display_assets/rstep.png
diff --git a/DebyeCalculator/submodules/images/rthres.png b/DebyeCalculator/display_assets/rthres.png
similarity index 100%
rename from DebyeCalculator/submodules/images/rthres.png
rename to DebyeCalculator/display_assets/rthres.png
diff --git a/DebyeCalculator/submodules/images/scattering_parameters.png b/DebyeCalculator/display_assets/scattering_parameters.png
similarity index 100%
rename from DebyeCalculator/submodules/images/scattering_parameters.png
rename to DebyeCalculator/display_assets/scattering_parameters.png
diff --git a/DebyeCalculator/submodules/images/select_files.png b/DebyeCalculator/display_assets/select_files.png
similarity index 100%
rename from DebyeCalculator/submodules/images/select_files.png
rename to DebyeCalculator/display_assets/select_files.png
diff --git a/DebyeCalculator/submodules/images/show_hide.png b/DebyeCalculator/display_assets/show_hide.png
similarity index 100%
rename from DebyeCalculator/submodules/images/show_hide.png
rename to DebyeCalculator/display_assets/show_hide.png
diff --git a/DebyeCalculator/submodules/images/sq.png b/DebyeCalculator/display_assets/sq.png
similarity index 100%
rename from DebyeCalculator/submodules/images/sq.png
rename to DebyeCalculator/display_assets/sq.png
diff --git a/DebyeCalculator/submodules/generate_nanoparticles.py b/DebyeCalculator/submodules/generate_nanoparticles.py
deleted file mode 100644
index dbb3b55..0000000
--- a/DebyeCalculator/submodules/generate_nanoparticles.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""
-generate_nanoparticles.py
-
-This file contains a function to generate nanoparticle structures based on a given unit cell structure and a set of radii.
-
-Function:
-    generate_nanoparticle:
-        Generates nanoparticle structures by replicating and manipulating a given unit cell.
-
-Parameters:
-    structure_path (str): Path to the file containing the unit cell structure in a format supported by ASE (Atomic Simulation Environment).
-    radii (list): List of radii to generate nanoparticles with different sizes.
-    sort_atoms (bool): Flag to sort atoms in the generated nanoparticle structures. Default is True.
-
-Returns:
-    tuple: Tuple containing two lists: 
-        - The first list contains the generated nanoparticle structures as ASE Atom objects.
-        - The second list contains the corresponding nanoparticle sizes (diameters) for each generated nanoparticle.
-
-The `generate_nanoparticle` function reads the unit cell structure from the provided file and creates a supercell by replicating the unit cell based on the given radii. It then manipulates the positions of the atoms to center them within the supercell.
-
-The function iteratively generates nanoparticle structures by including atoms within a certain distance from the center of the supercell. The size of each nanoparticle is determined by the largest distance between metal atoms (if present) within the specified radius.
-
-The resulting nanoparticle structures are returned in sorted order (if `sort_atoms` is True) based on the atom type. The sorting is performed to ensure consistent orientation and to align the metal atoms towards the center of the nanoparticle.
-
-Note: The ASE (Atomic Simulation Environment) library is used for reading and manipulating the atomic structures. Make sure to install ASE and its dependencies before using this function.
-
-Author: Johansen & Anker et. al.
-Date: August 2023
-"""
-
-import os
-import torch
-import tempfile
-import numpy as np
-from tqdm.auto import tqdm
-from torch.nn.functional import pdist
-from torch import cdist
-from ase.io import read, write
-from ase.build import make_supercell
-from ase.build.tools import sort as ase_sort
-from diffpy.structure import loadStructure
-
-def generate_nanoparticles(
-    structure_path,
-    radii,
-    sort_atoms=True,
-    device = 'cpu',
-):
-    """
-    Generate nanoparticles from a given structure and list of radii.
-
-    Args:
-        structure_path (str): Path to the input structure file.
-        radii (list): List of radii for nanoparticles to be generated.
-        sort_atoms (bool, optional): Whether to sort atoms in the nanoparticle.
-            Defaults to True.
-
-    Returns:
-        list: List of ASE Atoms objects representing the generated nanoparticles.
-        list: List of nanoparticle sizes (diameter) corresponding to each radius.
-    """
-    # Read the input unit cell structure
-    unit_cell = read(structure_path)
-    cell_dims = np.array(unit_cell.cell.cellpar()[:3])
-    r_max = np.amax(radii)
-
-    # Create a supercell to encompass the entire range of nanoparticles
-    supercell_matrix = np.diag((np.ceil(r_max / cell_dims)) * 2)
-    cell = make_supercell(prim=unit_cell, P=supercell_matrix)
-
-    # Process positions and filter metal atoms
-    positions = torch.from_numpy(cell.get_positions()).to(dtype=torch.float32, device=device)
-    positions -= torch.mean(positions, dim=0)
-    metal_filter = torch.BoolTensor([a not in ligands for a in cell.get_chemical_symbols()]).to(device=device)
-    center_dists = torch.norm(positions, dim=1)
-    positions -= positions[metal_filter][torch.argmin(center_dists[metal_filter])]
-    center_dists = torch.norm(positions, dim=1)
-    min_metal_dist = torch.min(pdist(positions[metal_filter]))
-    cell.positions = positions.cpu()
-
-    # Initialize nanoparticle lists and progress bar
-    nanoparticle_list = []
-    nanoparticle_sizes = []
-    pbar = tqdm(desc='Generating nanoparticles', leave=False, total=len(radii))
-
-    # Generate nanoparticles for each radius
-    for r in radii:
-        incl_mask = (center_dists <= r)
-        interface_dists = cdist(positions, positions[incl_mask])
-        nanoparticle_size = 0
-
-        # Find interface atoms and determine nanoparticle size
-        for i in range(interface_dists.shape[0]):
-            interface_mask = (interface_dists[i] <= min_metal_dist) & ~metal_filter[i]
-            if torch.any(interface_mask):
-                nanoparticle_size = max(nanoparticle_size, center_dists[i] * 2)
-                incl_mask[i] = True
-
-        nanoparticle_sizes.append(nanoparticle_size)
-
-        # Extract the nanoparticle from the supercell
-        np_cell = cell[incl_mask.cpu()]
-        if sort_atoms:
-            np_cell = ase_sort(np_cell)
-            if np_cell.get_chemical_symbols()[0] in ligands:
-                np_cell = np_cell[::-1]
-
-        nanoparticle_list.append(np_cell)
-        pbar.update(1)
-
-    return nanoparticle_list, nanoparticle_sizes
-
-def ase_to_diffpy(particle):
-    """
-    Convert an ASE object to a DiffPy structure.
-
-    Parameters:
-    - particle (ase.Atoms): The ASE object representing the atomic structure.
-
-    Returns:
-    - diffpy.Structure.Structure: The corresponding DiffPy structure.
-
-    This function works by first writing the ASE object to a temporary XYZ file, 
-    then modifying the file for compatibility with DiffPy, and finally loading 
-    the modified file into a DiffPy structure.
-    """
-
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        tmp_structure_path = os.path.join(tmpdirname, 'tmp_struc.xyz')
-        write(tmp_structure_path, particle, 'xyz')
-
-        # Read the content of the saved tmp_struc.xyz file
-        with open(tmp_structure_path, "r") as file:
-            content = file.readlines()
-
-        # Modify the content for diffpy compatibility
-        modified_content = [content[0]]  # keep the number of atoms line
-
-        # Start from the third line and only keep element symbol and x, y, z positions
-        for line in content[2:]:
-            parts = line.split()
-            modified_line = f"{parts[0]} {parts[1]} {parts[2]} {parts[3]}\n"
-            modified_content.append(modified_line)
-
-        # Insert an empty line at the second position
-        modified_content.insert(1, '\n')
-
-        # Save the modified content back to tmp_struc.xyz
-        with open(tmp_structure_path, "w") as file:
-            file.writelines(modified_content)
-
-        diffpy_structure = loadStructure(tmp_structure_path)
-        
-    return diffpy_structure
-
-# Sample ligand list (add your ligands if different)
-ligands = ['O', 'H', 'Cl']
diff --git a/DebyeCalculator/submodules/interact.py b/DebyeCalculator/submodules/interact.py
deleted file mode 100644
index 20e8868..0000000
--- a/DebyeCalculator/submodules/interact.py
+++ /dev/null
@@ -1,419 +0,0 @@
-import ipywidgets as widgets
-from ipywidgets import HBox, VBox
-import numpy as np
-import matplotlib.pyplot as plt
-from IPython.display import display
-from IPython.display import clear_output
-from glob import glob
-import os
-import threading
-import time
-import sys
-import base64
-from IPython.display import display, HTML
-from datetime import datetime
-
-from debye_calculator import DebyeCalculator
-
-def run_interact(
-    debye_calc: DebyeCalculator,
-    _cont_updates: bool = False
-):
-    qmin = debye_calc.qmin
-    qmax = debye_calc.qmax
-    qstep = debye_calc.qstep
-    qdamp = debye_calc.qdamp
-    rmin = debye_calc.rmin
-    rmax = debye_calc.rmax
-    rstep = debye_calc.rstep
-    rthres = debye_calc.rthres
-    biso = debye_calc.biso
-    device = debye_calc.device
-    batch_size = debye_calc.batch_size
-    lorch_mod = debye_calc.lorch_mod
-    radiation_type = debye_calc.radiation_type
-    profile = False
-    
-    # Buttons
-    radtype_button = widgets.ToggleButtons(options=['xray', 'neutron'],
-        value=radiation_type,
-        description='Rad. type',
-        layout = widgets.Layout(width='900px'),
-        button_style='info'
-    )
-    select_radius = widgets.FloatText(
-        min = 0,
-        max = 50,
-        step=0.01,
-        value=5,
-        description='Radius (.cif):',
-        disabled = True,
-    )
-    device_button = widgets.ToggleButtons(
-        options=['cpu', 'cuda'],
-        value=device,
-        description='Hardware:',
-        button_style='info',
-    )
-    batch_size_button = widgets.IntText(
-        min = 100,
-        max = 10000,
-        value=batch_size,
-        description='Batch Size:',
-    )
-    qslider = widgets.FloatRangeSlider(
-        value=[qmin, qmax],
-        min=0.0,
-        max=50.0,
-        step=0.01,
-        description='Qmin/Qmax:',
-        continuous_update=_cont_updates,
-        orientation='horizontal',
-        readout=True,
-        style={'font_weight':'bold', 'slider_color': 'white'},
-        layout = widgets.Layout(width='900px'),
-    )
-    rslider = widgets.FloatRangeSlider(
-        value=[rmin, rmax],
-        min=0,
-        max=100.0,
-        step=rstep,
-        description='rmin/rmax:',
-        continuous_update=_cont_updates,
-        orientation='horizontal',
-        readout=True,
-        style={'font_weight':'bold', 'slider_color': 'white'},
-        layout = widgets.Layout(width='900px'),
-    )
-    qdamp_slider = widgets.FloatSlider(
-        min=0.00,
-        max=0.10,
-        value=qdamp, 
-        step=0.01,
-        description='Qdamp:',
-        layout = widgets.Layout(width='900px'),
-        continuous_update=_cont_updates,
-    )
-    biso_slider = widgets.FloatSlider(
-        min=0.00,
-        max=1.00,
-        value=biso,
-        step=0.01,
-        description='B-iso:',
-        continuous_update=_cont_updates,
-        layout = widgets.Layout(width='900px'),
-    )
-    qstep_box = widgets.FloatText(
-        min = 0.001,
-        max = 1,
-        step=0.001,
-        value=qstep,
-        description='Qstep:',
-    )
-    rstep_box = widgets.FloatText(
-        min = 0.001,
-        max = 1,
-        step=0.001,
-        value=rstep,
-        description='rstep:',
-    )
-    rthres_box = widgets.FloatText(
-        min = 0.001,
-        max = 1,
-        step=0.001,
-        value=rthres,
-        description='rthres:',
-    )
-    lorch_mod_button = widgets.Checkbox(
-        value=lorch_mod,
-        description='Lorch mod.:',
-    )
-    scale_type_button = widgets.ToggleButtons(
-        options=['linear', 'logarithmic'],
-        value='linear',
-        description='Axes scaling:',
-        button_style='info'
-    )
-    
-    # Download options
-    def create_download_link(filename_prefix, data, header=None):
-    
-        # Collect Metadata
-        metadata ={
-            'qmin': qslider.value[0],
-            'qmax': qslider.value[1],
-            'qdamp': qdamp_slider.value,
-            'qstep': qstep_box.value,
-            'rmin': rslider.value[0], 
-            'rmax': rslider.value[1],
-            'rstep': rstep_box.value, 
-            'rthres': rthres_box.value,
-            'biso': biso_slider.value,
-            'device': device_button.value,
-            'batch_size': batch_size_button.value, 
-            'lorch_mod': lorch_mod_button.value,
-            'radiation_type': radtype_button.value
-        }
-
-        # Join content
-        output = ''
-        content = "\n".join([",".join(map(str, np.around(row,len(str(qstep_box.value))))) for row in data])
-        for k,v in metadata.items():
-            output += f'{k}:{v}\n'
-        output += '\n'
-        if header:
-            output += header + '\n'
-        output += content
-    
-        # Encode as base64
-        b64 = base64.b64encode(output.encode()).decode()
-    
-        # Add Time
-        t = datetime.now()
-        year = f'{t.year}'[-2:]
-        month = f'{t.month}'.zfill(2)
-        day = f'{t.day}'.zfill(2)
-        hours = f'{t.hour}'.zfill(2)
-        minutes = f'{t.minute}'.zfill(2)
-        seconds = f'{t.second}'.zfill(2)
-        
-        # Make filename
-        filename = filename_prefix + '_' + select_file.value.split('/')[-1].split('.')[0] + '_' + month + day + year + '_' + hours + minutes + seconds + '.csv'
-    
-        # Make href and return
-        href = f'<a href="data:text/csv;base64,{b64}" download="{filename}">Download {filename}</a>'
-        return href
-    
-    def create_structure_download_link(filename_prefix, ase_atoms):
-        
-        # Get atomic properties
-        positions = ase_atoms.get_positions()
-        elements = ase_atoms.get_chemical_symbols()
-        num_atoms = len(ase_atoms)
-    
-        # Make header
-        header = str(num_atoms) + "\n\n"
-    
-        # Join content 
-        content = header + "\n".join([el + '\t' + "\t".join(map(str,np.around(row, 3))) for row, el in zip(positions, elements)])
-        
-        # Encode as base64
-        b64 = base64.b64encode(content.encode()).decode()
-        
-        # Add Time
-        t = datetime.now()
-        year = f'{t.year}'[-2:]
-        month = f'{t.month}'.zfill(2)
-        day = f'{t.day}'.zfill(2)
-        hours = f'{t.hour}'.zfill(2)
-        minutes = f'{t.minute}'.zfill(2)
-        seconds = f'{t.second}'.zfill(2)
-    
-        # Make ilename
-        filename = filename_prefix + '_' + select_file.value.split('/')[-1].split('.')[0] + str(select_radius.value) + '_' + month + day + year + '_' + hours + minutes + seconds + '.xyz'
-    
-        # Make href and return
-        href = f'<a href="data:text/xyz;base64,{b64}" download="{filename}">Download {filename}</a>'
-        return href
-    
-    # Download buttons
-    download_button = widgets.Button(description="Download Data")
-    
-    @download_button.on_click
-    def on_download_button_click(button):
-        # Try to compile all the data and create html link to download files
-        try:
-            # Data
-            iq_data = np.column_stack([q, iq])
-            sq_data = np.column_stack([q, sq])
-            fq_data = np.column_stack([q, fq])
-            gr_data = np.column_stack([r, gr])
-        
-            # Clear warning message
-            sys.stdout.write('\x1b[1A')
-            sys.stdout.write('\x1b[1A')
-            sys.stdout.write('\x1b[1A')
-            sys.stdout.write('\x1b[2K')
-    
-            # Display download links
-            display(HTML(create_download_link('iq', iq_data, "q,I(Q)")))
-            display(HTML(create_download_link('sq', sq_data, "q,S(Q)")))
-            display(HTML(create_download_link('fq', fq_data, "q,F(Q)")))
-            display(HTML(create_download_link('gr', gr_data, "r,G(r)")))
-    
-            if not select_radius.disabled:
-                ase_atoms, _ = DebyeCalculator().generate_nanoparticles(select_file.value, select_radius.value)
-                
-                display(HTML(create_structure_download_link('structure', ase_atoms[0])))
-    
-        except Exception as e:
-            raise(e)
-            print('FAILED: No data has been selected', end="\r")
-                  
-    # Folder dropdown widget
-    folder = widgets.Text(description='Data Dir.:', placeholder='Provide data directory', disabled=False)
-    
-    # Create a dropdown menu widget for selection of XYZ file and an output area
-    DEFAULT_MSG = ''
-    select_file = widgets.Dropdown(description='Select File:', options=[DEFAULT_MSG], value=DEFAULT_MSG, disabled=True)
-    
-    # Define a function to update the scattering patterns based on the selected parameters
-    def update_options(change):
-        folder = change.new
-        paths = sorted(glob(os.path.join(folder, '*.xyz')) + glob(os.path.join(folder, '*.cif')))
-        if len(paths):
-            select_file.options = ['Select data file'] + paths
-            select_file.value = 'Select data file'
-            select_file.disabled = False
-        else:
-            select_file.options = [DEFAULT_MSG]
-            select_file.value = DEFAULT_MSG
-            select_file.disabled = True
-    
-    # Link the update function to the dropdown widget's value change event
-    folder.observe(update_options, names='value')
-    
-    def update_options_radius(change):
-        #select_radius = change.new
-        selected_ext = select_file.value.split('.')[-1]
-        if selected_ext == 'xyz':
-            select_radius.disabled = True
-        elif selected_ext == 'cif':
-            select_radius.disabled = False
-    
-    select_file.observe(update_options_radius, names='value')
-    
-    plot_button = widgets.Button(
-        description='Plot',
-    )
-    
-    def update_figure(r, q, iq, sq, fq, gr, _unity_sq=True):
-        
-        fig, axs = plt.subplots(2, 2, figsize=(12, 8), dpi=75)
-        axs = axs.flatten()
-    
-        if scale_type_button.value == 'logarithmic':
-            axs[0].set_xscale('log')
-            axs[0].set_yscale('log')
-    
-        axs[0].plot(q, iq)
-        axs[0].set(xlabel='$Q$ [$\AA^{-1}$]', ylabel='$I(Q)$ [counts]')
-        
-        axs[1].axhline(1, alpha=0.5, ls='--', c='g')
-        axs[1].plot(q, sq+int(_unity_sq))
-        axs[1].set(xlabel='$Q$ [$\AA^{-1}$]', ylabel='$S(Q)$')
-        
-        axs[2].axhline(0, alpha=0.5, ls='--', c='g')
-        axs[2].plot(q, fq)
-        axs[2].set(xlabel='$Q$ [$\AA^{-1}$]', ylabel='$F(Q)$')
-        
-        axs[3].plot(r, gr)
-        axs[3].set(xlabel='$r$ [$\AA$]', ylabel='$G_r(r)$')
-    
-        labels = ['Scattering Intensity, I(Q)',
-                  'Structure Function, S(Q)',
-                  'Reduced Structure Function, F(Q)',
-                  'Reduced Pair Distribution Function, G(r)']
-    
-        for ax, label in zip(axs, labels):
-            ax.relim()
-            ax.autoscale_view()
-            ax.set_title(label)
-            ax.grid(alpha=0.2)
-    
-        fig.suptitle("XYZ file: " + select_file.value.split('/')[-1].split('.')[0])
-        fig.tight_layout()
-    
-    @plot_button.on_click
-    def update_parameters(b=None):
-    
-        clear_output(wait=True)
-        display_tabs()
-    
-        global r, q, iq, sq, fq, gr
-    
-        try:
-            path_ext = select_file.value.split('.')[-1]
-        except Exception as e:
-            return
-    
-        if (select_file.value is not None) and select_file.value != DEFAULT_MSG and path_ext in ['xyz', 'cif']:
-            try:
-                debye_calc = DebyeCalculator(
-                    device=device_button.value, 
-                    batch_size=batch_size_button.value,
-                    radiation_type=radtype_button.value,
-                    qmin=qslider.value[0], 
-                    qmax=qslider.value[1], 
-                    qstep=qstep_box.value, 
-                    qdamp=qdamp_slider.value,
-                    rmin=rslider.value[0],
-                    rmax=rslider.value[1], 
-                    rstep=rstep_box.value, 
-                    rthres=rthres_box.value, 
-                    biso=biso_slider.value,
-                    lorch_mod=lorch_mod_button.value
-                )
-                if not select_radius.disabled and select_radius.value > 8:
-                    print('Generating...')
-                r, q, iq, sq, fq, gr = debye_calc._get_all(select_file.value, select_radius.value)
-                
-                clear_output(wait=True)
-                display_tabs()
-                update_figure(r, q, iq, sq, fq, gr)
-                
-            except Exception as e:
-                raise(e)
-                print(f'FAILED: Could not load data file: {path}', end='\r')
-                
-    # Make File Tab
-    file_tab = VBox(children = [
-        folder,
-        select_file,
-    ])
-
-    # Make Generate Tab
-    generate_tab = VBox(children = [
-        folder,
-        select_file,
-        select_radius,
-    ])
-    
-    # Make Scattering Tab
-    scattering_tab = VBox(children = [
-        radtype_button,
-        HBox(children=[qslider, qstep_box]),
-        HBox(children=[rslider, rstep_box]),
-        HBox(children=[qdamp_slider, rthres_box]),
-        HBox(children=[biso_slider, lorch_mod_button]),
-    ])
-
-    # Make Plotting Tab
-    plotting_tab = VBox(children = [
-        scale_type_button,
-    ])
-
-    # Make Hardware Tab
-    hardware_tab = VBox(children = [
-        device_button,
-        batch_size_button,
-    ])
-
-    # Display Tabs
-    tabs = widgets.Tab(children=[
-        file_tab,
-        generate_tab,
-        scattering_tab,
-        plotting_tab,
-        hardware_tab,
-    ])
-
-    tabs.set_title(0, 'XYZ File Select')
-    tabs.set_title(1, 'CIF Nanoparticle Generation')
-    tabs.set_title(2, 'Scattering Parameters')
-    tabs.set_title(3, 'Plotting Options')
-    tabs.set_title(4, 'Hardware Options')
-
-    def display_tabs():
-        display(VBox(children=[tabs, HBox(children=[plot_button, download_button])]))
-    display_tabs()
diff --git a/DebyeCalculator/submodules/SASCalculator.py b/DebyeCalculator/utility/SASCalculator.py
similarity index 100%
rename from DebyeCalculator/submodules/SASCalculator.py
rename to DebyeCalculator/utility/SASCalculator.py
diff --git a/DebyeCalculator/submodules/__init__.py b/DebyeCalculator/utility/__init__.py
similarity index 100%
rename from DebyeCalculator/submodules/__init__.py
rename to DebyeCalculator/utility/__init__.py
diff --git a/DebyeCalculator/submodules/produce_figure_data.py b/DebyeCalculator/utility/produce_figure_data.py
similarity index 100%
rename from DebyeCalculator/submodules/produce_figure_data.py
rename to DebyeCalculator/utility/produce_figure_data.py
diff --git a/DebyeCalculator/submodules/profiling.py b/DebyeCalculator/utility/profiling.py
similarity index 100%
rename from DebyeCalculator/submodules/profiling.py
rename to DebyeCalculator/utility/profiling.py
diff --git a/quickstart/QuickStart.ipynb b/quickstart/QuickStart.ipynb
index bf49c8a..e78b0de 100644
--- a/quickstart/QuickStart.ipynb
+++ b/quickstart/QuickStart.ipynb
@@ -22,16 +22,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "10655853",
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": 4,
+   "id": "68b4179d",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b93995400054440a9d537225840590f",
+       "model_id": "c7585f8709db4c8591ad043ffeb23e06",
        "version_major": 2,
        "version_minor": 0
       },
@@ -41,30 +39,12 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Generating nanoparticle of radius 15.0 using icsd_001504_cc_r6_lc_2.85_6_tetragonal.xyz ...\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
     "import os\n",
     "os.chdir('../DebyeCalculator')\n",
     "from debye_calculator import DebyeCalculator\n",
-    "\n",
     "debye_calc = DebyeCalculator()\n",
     "debye_calc.interact()"
    ]