diff --git a/brainglobe_utils/IO/cells.py b/brainglobe_utils/IO/cells.py
index 136f254..68592f8 100644
--- a/brainglobe_utils/IO/cells.py
+++ b/brainglobe_utils/IO/cells.py
@@ -29,11 +29,9 @@ def get_cells(
     cells_only: bool = False,
     cell_type: Optional[int] = None,
 ):
-    # TODO: implement csv read
     if cells_file_path.endswith(".xml"):
         return get_cells_xml(cells_file_path, cells_only=cells_only)
     elif cells_file_path.endswith(".yml"):
-        # Not general
         return get_cells_yml(cells_file_path, ignore_type=True)
     elif os.path.isdir(cells_file_path):
         try:
@@ -211,14 +209,6 @@ def pretty_xml(elem, indentation_str="  "):
     return md_parsed.toprettyxml(indent=indentation_str, encoding="UTF-8")
 
 
-# TRANSCODE
-def transform_xml_file(xml_file_path, output_file_path, transform_params):
-    cells = get_cells(xml_file_path)  # TODO: check if cells_only
-    for cell in cells:
-        cell.transform(*transform_params)
-    cells_to_xml(cells, output_file_path)
-
-
 def find_relevant_tiffs(tiffs, cell_def):
     cells = [UntypedCell(tiff) for tiff in tiffs]
     if os.path.isdir(cell_def):
diff --git a/brainglobe_utils/array/filter.py b/brainglobe_utils/array/filter.py
deleted file mode 100644
index f6e7e8e..0000000
--- a/brainglobe_utils/array/filter.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import scipy.ndimage.filters as filters
-
-
-def smooth_opt(array, smoothing_kernel=None, smoothing_type="gaussian"):
-    """
-    Smoothes an array, but does nothing is smoothing=None
-    :param array: Input nD array
-    :param smoothing_kernel: Smoothing kernel default: None
-    :param smoothing_type: How to smooth, e.g. "gaussian"
-    :return: (Possibly) smoothed array
-    """
-    if smoothing_kernel is not None:
-        if smoothing_type == "gaussian":
-            array = filters.gaussian_filter(array, smoothing_kernel)
-        else:
-            raise ValueError(
-                f"Smoothing type: '{smoothing_type}' is not " f"supported"
-            )
-    return array
diff --git a/brainglobe_utils/array/fit.py b/brainglobe_utils/array/fit.py
deleted file mode 100644
index 1e15432..0000000
--- a/brainglobe_utils/array/fit.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import numpy as np
-
-
-def polyfit_curve(x, y, fit_degree=1):
-    """
-    Calculates the curve of best fit, to a given polynomial degree, for given
-    points in x & y.
-    :param x: Input points
-    :param y: Input points
-    :param fit_degree: Degree of the fitting polynomial
-    :return: 1D fitted curve
-    """
-    poly_fit = np.polyfit(x, y, fit_degree)
-    return np.polyval(poly_fit, x)
-
-
-def max_polyfit(x, y, fit_degree=1):
-    """
-    Calculates the max value of a series of points, based on a polynomial fit.
-    Should reduce the effect of outliers.
-    :param np.array x: Input points
-    :param np.array y: Input points
-    :param int fit_degree: Degree of the fitting polynomial
-    :return: Maximum value of the curve.
-    """
-    if x.ndim > 1 or y.ndim > 1:
-        raise NotImplementedError(
-            "max_polyfit only works for 1D arrays. "
-            "x has {} dimensions and y has {}.".format(x.ndim, y.ndim)
-        )
-    if len(x) != len(y):
-        raise ValueError(
-            "x and y must be the same length. x has length: {}, "
-            "and y has length: {}".format(len(x), len(y))
-        )
-
-    curve = polyfit_curve(x, y, fit_degree=fit_degree)
-    return curve.max()
diff --git a/brainglobe_utils/array/locate.py b/brainglobe_utils/array/locate.py
deleted file mode 100644
index a126cd3..0000000
--- a/brainglobe_utils/array/locate.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import numpy as np
-
-from .filter import smooth_opt
-
-
-def peak_nd_array(array, smoothing_kernel=None):
-    """
-    Finds the max value of an array (with optional smoothing)
-    :param np.array array: Input nD array
-    :param smoothing_kernel: Smoothing kernel default: None
-    :return: Maximum value
-    """
-
-    array = smooth_opt(array, smoothing_kernel=smoothing_kernel)
-    peak_magnitude, _ = max_nd_array(array)
-    return peak_magnitude
-
-
-def max_nd_array(array):
-    """
-    Finds the location, and magnitude of the maximum value of an nD array.
-    :param array: nD array
-    :returns:
-        - Magnitude of maximum value
-        - Coordinates of maximum value
-    """
-    peak_location = np.unravel_index(array.argmax(), array.shape)
-    peak_magnitude = array[peak_location]
-    return peak_magnitude, peak_location
diff --git a/brainglobe_utils/array/math.py b/brainglobe_utils/array/math.py
deleted file mode 100644
index dad6fee..0000000
--- a/brainglobe_utils/array/math.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import numpy as np
-
-
-def calculate_gradient_1d(array):
-    """
-    Calculates the first order gradient of a given array
-    :param array: 1D numpy array
-    :return: gradient of the array
-    """
-    x = range(1, len(array) + 1)
-    gradient, _ = np.polyfit(x, array, 1)
-    return gradient
-
-
-def multiply_across(array, column_vector):
-    """
-    Multiplies a column vector across a 2D array
-    :param array: 2D numpy array
-    :param column_vector: column vector
-    :return: numpy array
-    """
-
-    return array * column_vector[:, np.newaxis]
diff --git a/brainglobe_utils/array/misc.py b/brainglobe_utils/array/misc.py
deleted file mode 100644
index 9231a2b..0000000
--- a/brainglobe_utils/array/misc.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import numpy as np
-
-
-def split_array_half(array):
-    """
-    Splits an array like object in half, into two arrays.
-    If of an odd length, the first half will be larger by 1.
-
-    :param array: array like object to be split in half
-    :return: Two arrays (first_half and second_half)
-    """
-    midpoint = len(array) // 2
-    first_half = array[:midpoint]
-    second_half = array[midpoint:]
-    assert len(first_half) + len(second_half) == len(array)
-    return first_half, second_half
-
-
-def midpoints_of_series(series):
-    """
-    Given a series of N values, return a N-1 length array of the midpoints
-    between these points
-
-    :param series: 1D np.array of values
-    """
-    return np.ediff1d(series) / 2 + series[:-1]
-
-
-def weight_array(array, weights):
-    """
-    For a given 1D array, and a matching weights array of equal size, append
-    the array with the appropriate values when the corresponding weights value
-     is >1. I.e. for a an array (1, 5, 3, 10) and a weight array (1 2 3 1),
-     return (1 5 3 10 5 3 3)
-    :param array: Array of values
-    :param weights: Corresponding weighting of those values.
-    :return: Weighted array, with additional values appended
-    """
-    array = np.array(array)
-    original_array = np.copy(array)
-    weights = np.array(weights)
-    max_weight = max(weights)
-    for weight in range(2, int(max_weight) + 1):
-        array = np.append(array, original_array[weights == weight])
-    return array
-
-
-def sanitise_array(array, extreme_multiplier=None, exclude_zeros=False):
-    """
-    Remove nans and other extreme values, including those introduced by
-    np.nan_to_num
-    :param array: nD numpy array
-    :param extreme_multiplier: How extreme (compared to median) are values
-    allowed to be?
-    :param bool exclude_zeros: If True, don't include zero values in the
-    median value calculation. Default: False
-    :return np.array: Array with extreme values removed
-    """
-    array = np.nan_to_num(array)
-    if extreme_multiplier is not None:
-        array = reject_extreme(
-            array, extreme_multiplier, exclude_zeros=exclude_zeros
-        )
-    return array
-
-
-def reject_extreme(array, extreme_multiplier, exclude_zeros=False):
-    """
-    Removes extremely large values from an array (by setting to 0).
-
-    Useful following numpy.nan_to_num which replaces NaN values with very
-    large (10^300) values.
-    :param np.array array: Input array
-    :param extreme_multiplier: What multiple of the median value is classed
-    as "extreme"
-    :param bool exclude_zeros: If True, don't include zero values in the
-    median value calculation. Default: False
-    :return np.array: Array with the "extreme values" set to 0
-    """
-    if exclude_zeros:
-        array[
-            abs(array / np.median(array[array != 0])) > extreme_multiplier
-        ] = 0
-    else:
-        array[abs(array / np.median(array)) > extreme_multiplier] = 0
-    return array
-
-
-def mask_array_by_array_val(in_array, test_array, min_val):
-    """
-    Sets elements of array to 0, where test_array < min_val
-    :param in_array: array to be manipulated
-    :param test_array: array in which min_val must be reached. must be same
-    dimensions as in_array
-    :param min_val: value test_array must reach
-    :return: masked array
-    """
-
-    in_array[test_array < min_val] = 0
-    return in_array
diff --git a/brainglobe_utils/array/size.py b/brainglobe_utils/array/size.py
deleted file mode 100644
index 544ca08..0000000
--- a/brainglobe_utils/array/size.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import numpy as np
-
-
-def pad_with_number_1d(array, final_length, pad_number=0):
-    """
-    For a given array, pad with a value (symmetrically on either side) so that
-    the returned array is of a given length.
-    :param np.array array: Input array.
-    :param int final_length: How long should the final array be.
-    :param (float, int) pad_number: What value to pad with. Default: 0.
-    :return: New array of length: final_length
-    """
-    length = len(array)
-    pad_length = int((final_length - length) / 2)
-    pad = pad_number * np.ones((pad_length, 1))
-    new_array = np.append(pad, array)
-    new_array = np.append(new_array, pad)
-    if len(new_array) < final_length:
-        new_array = np.append(new_array, pad_number)
-
-    return new_array
diff --git a/brainglobe_utils/cells/cells.py b/brainglobe_utils/cells/cells.py
index 3265254..ab79eb4 100644
--- a/brainglobe_utils/cells/cells.py
+++ b/brainglobe_utils/cells/cells.py
@@ -2,18 +2,15 @@
 Based on https://github.com/SainsburyWellcomeCentre/niftynet_cell_count by
 Christian Niedworok (https://github.com/cniedwor).
 """
-import logging
 import math
 import os
 import re
 from collections import defaultdict
 from functools import total_ordering
-from typing import Any, DefaultDict, Dict, List, Optional, Tuple, Union
+from typing import Any, DefaultDict, Dict, List, Tuple, Union
 from xml.etree import ElementTree
 from xml.etree.ElementTree import Element as EtElement
 
-import numpy.typing as npt
-
 
 @total_ordering
 class Cell:
@@ -250,112 +247,6 @@ def pos_from_file_name(file_name: str) -> List[float]:
     return [int(p) for p in (x[-1][1:], y[-1][1:], z[-1][1:])]
 
 
-def transform(
-    cell: Cell,
-    deformation_field: npt.NDArray[Any],
-    field_scales: Tuple[float, float, float],
-    scales: Tuple[float, float, float],
-) -> Optional[Cell]:
-    """
-    Transforms cell position from one space, to another (defined by a
-    deformation field)
-    :param cell: Cells in original space
-    :param deformation_field: Deformation field
-    (shape (len(x), len(y), len(z), 3). For each spatial position, there is a
-    vector mapping onto a new coordinate space.
-    :param field_scales: Scaling of the deformation field values (in mm) into
-    voxel space (e.g. 100,100,100)
-    :param scales: Scale of cell x, y and z positions onto deformation
-    field (e.g. 0.2, 0.2, 0.5)
-    :return: Cell in the new space
-    """
-    scaled_x = int(round(cell.x * scales[0]))
-    scaled_y = int(round(cell.y * scales[1]))
-    scaled_z = int(round(cell.z * scales[2]))
-
-    try:
-        new_x = int(
-            round(
-                field_scales[0]
-                * deformation_field[scaled_x, scaled_y, scaled_z, 0, 0]
-            )
-        )
-        new_y = int(
-            round(
-                field_scales[1]
-                * deformation_field[scaled_x, scaled_y, scaled_z, 0, 1]
-            )
-        )
-        new_z = int(
-            round(
-                field_scales[2]
-                * deformation_field[scaled_x, scaled_y, scaled_z, 0, 2]
-            )
-        )
-
-        # if any new coordinates are negative
-        if any(position < 0 for position in [new_x, new_y, new_z]):
-            warn_outside_target_space(cell)
-
-        else:
-            cell.x = new_x
-            cell.y = new_y
-            cell.z = new_z
-        return cell
-
-    except IndexError:
-        warn_outside_target_space(cell)
-        return None
-
-
-def warn_outside_target_space(cell: Cell) -> None:
-    logging.warning(
-        "Position x:{}, y:{}, z{} is outside the target "
-        "coordinate space, skipping. If this happens for many "
-        "cells, something may be up.".format(cell.x, cell.y, cell.z)
-    )
-
-
-def transform_cell_positions(
-    cells: List[Cell],
-    deformation_field: npt.NDArray[Any],
-    field_scales: Tuple[float, float, float] = (100, 100, 100),
-    scales: Tuple[float, float, float] = (1, 1, 1),
-) -> List[Cell]:
-    """
-    Transforms cell positions from one space, to another (defined by a
-    deformation field)
-    :param cells: List of cells in original space
-    :param deformation_field: Deformation field
-    (shape (len(x), len(y), len(z), 3). For each spatial position, there is a
-    vector mapping onto a new coordinate space.
-    :param field_scales: Scaling of the deformation field values (in mm) into
-    voxel space (e.g. 100,100,100)
-    :param scales: Scale of cell x, y and z positions onto deformation
-    field (e.g. 0.2, 0.2, 0.5)
-    :return: list of cells in the new space
-    """
-    # TODO: parallelise (maybe not needed, very quick anyway)
-    # TODO: clarify this transformation, and the existing transformed_x
-    # property of the cells used for other things (e.g. summaries)
-    transformed_cells = [
-        transform(cell, deformation_field, field_scales, scales)
-        for cell in cells
-    ]
-
-    # Remove None's from list (where cell couldn't be transformed)
-    transformed_cells_no_none = [
-        cell for cell in transformed_cells if cell is not None
-    ]
-    cells_not_transformed = len(cells) - len(transformed_cells_no_none)
-    logging.warning(
-        "{} cells were not transformed to standard space".format(
-            cells_not_transformed
-        )
-    )
-    return transformed_cells_no_none
-
-
 def group_cells_by_z(cells: List[Cell]) -> DefaultDict[float, List[Cell]]:
     """
     For a list of Cells return a dict of lists of cells, grouped by plane.
diff --git a/brainglobe_utils/cells/utils.py b/brainglobe_utils/cells/utils.py
deleted file mode 100644
index 4864d91..0000000
--- a/brainglobe_utils/cells/utils.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import logging
-from typing import Any, Tuple, Union
-
-import numpy.typing as npt
-
-import brainglobe_utils.IO.cells as cell_io
-from brainglobe_utils.cells.cells import Cell
-
-
-def get_cell_location_array(
-    cell_file: str,
-    cell_position_scaling: Union[
-        Tuple[None, None, None], Tuple[float, float, float]
-    ] = (None, None, None),
-    cells_only: bool = False,
-    type_str: str = "type",
-    integer: bool = True,
-) -> npt.NDArray[Any]:
-    """
-    Loads a cell file, and converts to an array, with 3 columns of x,y,z
-    positions
-    :param cell_file: Any supported cell file, e.g. xml
-    :param cell_position_scaling: list of cell scaling (raw -> final) for
-    [x, y, z]
-    :param cells_only: If only cells (rather than unknown or artifacts)
-    should be included
-    :param str type_str: String defining the title of the cell type column
-    in the dataframe. Used to remove non cells (artifacts), and then to clean
-    up the dataframe to be converted into a numpy array.
-    :param integer: Force integer cell positions (default: True)
-    :return: Array of cell positions, with x,y,z columns
-    """
-
-    logging.debug("Loading cells")
-    cells = cell_io.get_cells(cell_file)
-
-    if cell_position_scaling != (None, None, None):
-        for cell in cells:
-            cell.transform(
-                x_scale=cell_position_scaling[0],
-                y_scale=cell_position_scaling[1],
-                z_scale=cell_position_scaling[2],
-                integer=integer,
-            )
-
-    cells = cell_io.cells_to_dataframe(cells)
-    num_cells = len(cells[cells[type_str] == Cell.CELL])
-    num_non_cells = len(cells[cells[type_str] == Cell.NO_CELL])
-    logging.debug(
-        "{} cells, and {} non-cells".format(num_cells, num_non_cells)
-    )
-    if cells_only:
-        logging.debug("Removing non cells")
-        cells = cells[cells[type_str] == Cell.CELL]
-
-    logging.debug("Tidying up dataframe to convert to array")
-    cells.drop(type_str, axis=1, inplace=True)
-    return cells.to_numpy()  # type: ignore[no-any-return]
diff --git a/brainglobe_utils/general/exceptions.py b/brainglobe_utils/general/exceptions.py
index 3e50dcf..036f0b0 100644
--- a/brainglobe_utils/general/exceptions.py
+++ b/brainglobe_utils/general/exceptions.py
@@ -12,8 +12,3 @@ def __init__(self, message):
 
     def __str__(self):
         return str(self.message)
-
-
-class ArgumentError(Exception):
-    def __init__(self, *args, **kwargs):
-        Exception.__init__(self, *args, **kwargs)
diff --git a/brainglobe_utils/general/list.py b/brainglobe_utils/general/list.py
index fcb9fca..fc72508 100644
--- a/brainglobe_utils/general/list.py
+++ b/brainglobe_utils/general/list.py
@@ -10,39 +10,3 @@ def remove_empty_string(str_list):
 def unique_elements_lists(list_in):
     """return the unique elements in a list"""
     return list(dict.fromkeys(list_in))
-
-
-def strip_spaces_list(list_in, strip_method="all"):
-    """
-    Remove spaces from all items in a list
-    :param list_in:
-    :param strip_method: Default: 'all' for leading and trailing spaces.
-    Can also be 'leading' or 'trailing'
-    :return: List with items stripped of spaces
-    """
-
-    if strip_method == "all":
-        list_out = [item.strip() for item in list_in]
-    elif strip_method == "leading":
-        list_out = [item.rstrip() for item in list_in]
-    elif strip_method == "trailing":
-        list_out = [item.lstrip() for item in list_in]
-    else:
-        raise NotImplementedError(
-            'Strip method: "{}" is not implemented. Please use "all", '
-            '"leading" or "trailing"'.format(strip_method)
-        )
-    return list_out
-
-
-def split_list(input_list):
-    """
-    Splits a list in half (assumes even length)
-    :param input_list:
-    :return: Tuple of the first and second halves of the list
-    """
-    if len(input_list) % 2 == 0:
-        half = len(input_list) // 2
-        return input_list[:half], input_list[half:]
-    else:
-        raise NotImplementedError("split_list requires a list of even length")
diff --git a/brainglobe_utils/general/numerical.py b/brainglobe_utils/general/numerical.py
index ee6c344..4d9df2f 100644
--- a/brainglobe_utils/general/numerical.py
+++ b/brainglobe_utils/general/numerical.py
@@ -1,5 +1,4 @@
 import argparse
-import decimal
 
 
 def is_even(num):
@@ -61,29 +60,3 @@ def check_positive_int(value, none_allowed=True):
             raise argparse.ArgumentTypeError("%s is an invalid value." % value)
 
     return ivalue
-
-
-def get_decimal_places(x):
-    """
-    Returns the number of decimal places of a number
-    :param float x: Input number
-    :return: Number of decimal places
-    """
-    d = decimal.Decimal(str(x))
-    return abs(d.as_tuple().exponent)
-
-
-def round_updown_to_x(num_in, x, direction="up"):
-    """
-    Rounds a given value (num_in) to the nearest multiple of x, in a given
-    direction (up or down)
-    :param num_in: Input value
-    :param x: Value to round to a multiple of
-    :param direction: Round up or down. Default: 'up'
-    :return: Rounded number
-    """
-    if direction == "down":
-        num_out = int(num_in) - int(num_in) % int(x)
-    else:
-        num_out = num_in + (x - num_in) % int(x)
-    return num_out
diff --git a/brainglobe_utils/general/parsing.py b/brainglobe_utils/general/parsing.py
deleted file mode 100644
index b474d7d..0000000
--- a/brainglobe_utils/general/parsing.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import operator
-
-
-def check_none(*args):
-    """
-    Checks if any of the arguments are None
-    :param args: A list of objects of any time
-    :return: True if any of the arguments are None
-    """
-    if any(arg is None for arg in args):
-        return True
-    else:
-        return False
-
-
-def convert_string_to_operation(string_operation, inclusive=True):
-    """
-    Converts a string (e.g. "higher") to an operation (e.g. operator.ge)
-    :param str string_operation: Operation as a string, e.g. "lower than"
-    :param inclusive: If True, the operation will be inclusive, i.e. ">="
-    rather than ">"
-    :return:
-    """
-    equal_list = ["equal", "same"]
-    lower_list = [
-        "lower",
-        "low",
-        "less",
-        "less than",
-        "lessthan",
-        "less_than",
-        "lower than",
-    ]
-    higher_list = [
-        "higher",
-        "high",
-        "higher",
-        "more" "more than",
-        "morethan",
-        "more_than",
-        "higher than",
-    ]
-
-    if string_operation in equal_list:
-        operation = operator.eq
-    elif string_operation in lower_list:
-        if inclusive:
-            operation = operator.le
-        else:
-            operation = operator.lt
-    elif string_operation in higher_list:
-        if inclusive:
-            operation = operator.ge
-        else:
-            operation = operator.gt
-    else:
-        raise NotImplementedError(
-            "convert_string_to_operation is only implemented for "
-            "equal conditions: {}, lower conditions: {} and higher conditions:"
-            '{}. Condition: "{}" is not recognised.'.format(
-                equal_list, lower_list, higher_list, string_operation
-            )
-        )
-    return operation
diff --git a/brainglobe_utils/general/system.py b/brainglobe_utils/general/system.py
index f6ee71b..d84f56b 100644
--- a/brainglobe_utils/general/system.py
+++ b/brainglobe_utils/general/system.py
@@ -218,8 +218,8 @@ def how_many_cores_with_sufficient_ram(
     this to ensure that the number of processes isn't too high.
     :param float fraction_free_ram: Fraction of the ram to ensure stays free
     regardless of the current program.
-    :param float max_ram_usage: Maximum amount of RAM (in bytes)
-    to use (allthough available may be lower)
+    :param float max_ram_usage: The Maximum amount of RAM (in bytes)
+    to use (although available may be lower)
     :return: How many CPU cores could be theoretically used based on
     the amount of free RAM
     """
@@ -271,21 +271,6 @@ def get_free_ram():
     return psutil.virtual_memory().available
 
 
-def sanitize_num_processes(num_processes, min_processes, parallel=False):
-    """
-    Returns False to prevent parallel processing in case more processes have
-    been requested than can be used.
-    :param num_processes: How many processes have been requested
-    :param min_processes: Minimum number of cores to keep free
-    :param parallel: If parallel is requested
-    :return bool: True if num_processes is sensible
-    """
-    if parallel:
-        if num_processes < min_processes:
-            parallel = False
-    return parallel
-
-
 def safe_execute_command(cmd, log_file_path=None, error_file_path=None):
     """
     Executes a command in the terminal, making sure that the output can
@@ -352,25 +337,6 @@ class SafeExecuteCommandError(Exception):
     pass
 
 
-def delete_temp(directory, paths, prefix="tmp__"):
-    """
-    Removes all temp files (properties of an object starting with "tmp__")
-    :param directory: Directory to delete tmp files from
-    :param paths: Paths object with temp paths.
-    :param prefix: String that temporary files (to be deleted) begin with.
-    """
-    for path_name, path in paths.__dict__.items():
-        if path_name.startswith(prefix):
-            if check_path_in_dir(path, directory):
-                try:
-                    os.remove(path)
-                except FileNotFoundError:
-                    logging.debug(
-                        f"File: {path} not found, not deleting. "
-                        f"Proceeding anyway."
-                    )
-
-
 def delete_directory_contents(directory, progress=False):
     """
     Removes all contents of a directory
@@ -383,19 +349,3 @@ def delete_directory_contents(directory, progress=False):
     else:
         for f in files:
             os.remove(os.path.join(directory, f))
-
-
-def filename_from_path(path, remove_extension=False):
-    """
-    Takes a filepath and returns only the filename, optionally removes the
-    file extension
-    :param path: Filepath
-    :param remove_extension: If True, remove the file extension too.
-    Default: False
-    :return: filename
-    """
-
-    filename = os.path.basename(path)
-    if remove_extension:
-        filename = os.path.splitext(filename)[0]
-    return filename
diff --git a/brainglobe_utils/image/objects.py b/brainglobe_utils/image/objects.py
deleted file mode 100644
index 4a1059f..0000000
--- a/brainglobe_utils/image/objects.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import numpy as np
-from skimage.measure import label
-
-
-def get_largest_non_zero_object(label_image):
-    """
-    In a labelled (each object assigned an int) numpy array. Return the
-    largest object with a value >= 1.
-    :param label_image: Output of skimage.measure.label
-    :return: Boolean numpy array or largest object
-    """
-    return label_image == np.argmax(np.bincount(label_image.flat)[1:]) + 1
-
-
-def keep_n_largest_objects(numpy_array, n=1, connectivity=None):
-    """
-    Given an input binary numpy array, return a "clean" array with only the
-    n largest connected components remaining
-
-    Inspired by stackoverflow.com/questions/47540926
-
-    TODO: optimise
-
-    :param numpy_array: Binary numpy array
-    :param n: How many objects to keep
-    :param connectivity: Labelling connectivity (see skimage.measure.label)
-    :return: "Clean" numpy array with n largest objects
-    """
-
-    labels = label(numpy_array, connectivity=connectivity)
-    assert labels.max() != 0  # assume at least 1 CC
-    n_largest_objects = get_largest_non_zero_object(labels)
-    if n > 1:
-        i = 1
-        while i < n:
-            labels[n_largest_objects] = 0
-            n_largest_objects += get_largest_non_zero_object(labels)
-            i += 1
-    return n_largest_objects
diff --git a/brainglobe_utils/image/orient.py b/brainglobe_utils/image/orient.py
deleted file mode 100644
index 69cfef1..0000000
--- a/brainglobe_utils/image/orient.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-
-
-def reorient_image(image, invert_axes=None, orientation="saggital"):
-    """
-    Reorients the image to the coordinate space of the atlas
-
-    :param image_path: str
-    :param threshold: float
-    :param invert_axes: tuple (Default value = None)
-    :param image:
-    :param orientation:  (Default value = "saggital")
-
-    """
-    if invert_axes is not None:
-        for axis in list(invert_axes):
-            image = np.flip(image, axis=axis)
-
-    if orientation != "saggital":
-        if orientation == "coronal":
-            transposition = (2, 1, 0)
-        elif orientation == "horizontal":
-            transposition = (1, 2, 0)
-
-        image = np.transpose(image, transposition)
-    return image
diff --git a/brainglobe_utils/image/shape.py b/brainglobe_utils/image/shape.py
deleted file mode 100644
index bb4db1d..0000000
--- a/brainglobe_utils/image/shape.py
+++ /dev/null
@@ -1,27 +0,0 @@
-def convert_shape_dict_to_array_shape(shape_dict, type="numpy"):
-    """
-    Converts a dict with "x", "y" (and optionally "z") attributes into
-    a tuple that can be used to e.g. initialise a numpy array
-    :param shape_dict: Dict with  "x", "y" (and optionally "z") attributes
-    :param type: One of "numpy" or "fiji", to determine whether the "x" or the
-    "y" attribute is the first dimension.
-    :return: Tuple array shape
-    """
-
-    shape = []
-    if type == "numpy":
-        shape.append(int(shape_dict["y"]))
-        shape.append(int(shape_dict["x"]))
-
-    elif type == "fiji":
-        shape.append(int(shape_dict["x"]))
-        shape.append(int(shape_dict["y"]))
-    else:
-        raise NotImplementedError(
-            "Type: {} not recognise, please specify "
-            "'numpy' or 'fiji'".format(type)
-        )
-    if "z" in shape_dict:
-        shape.append(int(shape_dict["z"]))
-
-    return tuple(shape)
diff --git a/brainglobe_utils/image/size.py b/brainglobe_utils/image/size.py
deleted file mode 100644
index da6d306..0000000
--- a/brainglobe_utils/image/size.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-from scipy.ndimage import zoom
-
-
-def resize_array(array, target_size, order=0):
-    """
-    Resizes an array to a target size. Uses less RAM than skimage
-    implementation
-    :param np.array array: numpy array to be resized
-    :param tuple target_size: target size of array
-    :param int order: Order for the interpolation (e.g. 0 for nearest
-    neighbours)
-    :return: Resized numpy array
-    """
-    factors = np.asarray(target_size, dtype=float) / np.asarray(
-        array.shape, dtype=float
-    )
-    array = zoom(array, factors, order=order)
-    return array
diff --git a/brainglobe_utils/math/trig.py b/brainglobe_utils/math/trig.py
deleted file mode 100644
index 63f137a..0000000
--- a/brainglobe_utils/math/trig.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import numpy as np
-
-
-def sine_min_max(frequency):
-    """
-    For a given frequency, return the minimum and maximum values that
-    correspond to a single full peak (i.e. -1 to -1)
-    :param frequency:
-    :return: minimum and maximum values (in radians)
-    """
-    period = 2 * np.pi / frequency
-    min_val = -period / 4
-    max_val = 3 * period / 4
-    return min_val, max_val
-
-
-def get_scaled_sine(x, y_max, frequency):
-    """
-    For a given input (x), returns a sine function, scaled to a given maximum
-    value.
-    :param x: Array of x values
-    :param y_max: Target maximum value of the function
-    :param frequency:  Sine wave frequency
-    :return: Sine function with a given max
-    """
-    y = np.sin(frequency * x) + 1
-    y = y * (y_max / y.max())
-    return y
diff --git a/brainglobe_utils/pandas/misc.py b/brainglobe_utils/pandas/misc.py
index f101f27..5bfe673 100644
--- a/brainglobe_utils/pandas/misc.py
+++ b/brainglobe_utils/pandas/misc.py
@@ -21,36 +21,6 @@ def sanitise_df(df):
     return df
 
 
-def move_column_first(df, column_name):
-    """
-    Moves a given dataframe column (given by name) to the first position
-    :param df: Dataframe
-    :param str column_name: Column name
-    :return: Dataframe with new first column
-    """
-
-    if not isinstance(df, pd.DataFrame):
-        raise ValueError("df must be a dataframe!")
-    if not isinstance(column_name, str):
-        raise ValueError("column_name must be a string!")
-    col = df[column_name]
-    df.drop(labels=[column_name], axis=1, inplace=True)
-    df.insert(0, column_name, col)
-    return df
-
-
-def regex_remove_df_columns(df, search_string_list):
-    """
-    Remove columns in a dataframe based on a list of search strings
-    :param df: Pandas dataframe
-    :param search_string_list: A list of regex strings to search for.
-    Columns matching these will be removed
-    """
-    for search_string in search_string_list:
-        df = df.drop(df.filter(regex=search_string).columns, axis=1)
-    return df
-
-
 def safe_pandas_concat(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:
     """
     Concatenate two DataFrames without relying on deprecated functionality
diff --git a/brainglobe_utils/pandas/query.py b/brainglobe_utils/pandas/query.py
deleted file mode 100644
index db48993..0000000
--- a/brainglobe_utils/pandas/query.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import numpy as np
-
-
-def column_as_array(df, column_name):
-    """
-    Returns an array of a specific dataframe column
-    :param df: Dataframe to be queried
-    :param column_name: Dataframe column heading to be queried
-    :return np.array: Resulting array of query
-    """
-    return np.array(df[column_name])
diff --git a/pyproject.toml b/pyproject.toml
index f132925..c5d4566 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,7 @@ dependencies = [
     "tqdm",
     "PyYAML",
     "scipy",
-    "scikit-image",
+    "numpy",
 ]
 
 license = {text = "MIT"}
@@ -56,7 +56,8 @@ dev = [
   "setuptools_scm",
   "pyqt5",
   "superqt",
-    "slurmio"
+  "slurmio",
+  "scikit-image"
 ]
 
 # For use with the SLURM job scheduler
diff --git a/tests/data/IO/obj/test.obj b/tests/data/IO/obj/test.obj
new file mode 100644
index 0000000..8e2c6de
--- /dev/null
+++ b/tests/data/IO/obj/test.obj
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diff --git a/brainglobe_utils/array/__init__.py b/tests/tests/test_IO/__init__.py
similarity index 100%
rename from brainglobe_utils/array/__init__.py
rename to tests/tests/test_IO/__init__.py
diff --git a/tests/tests/test_unit/test_IO/test_cell_io.py b/tests/tests/test_IO/test_cell_io.py
similarity index 79%
rename from tests/tests/test_unit/test_IO/test_cell_io.py
rename to tests/tests/test_IO/test_cell_io.py
index 40f9b01..9b44eee 100644
--- a/tests/tests/test_unit/test_IO/test_cell_io.py
+++ b/tests/tests/test_IO/test_cell_io.py
@@ -1,5 +1,6 @@
 import os
 
+import pandas as pd
 import pytest
 from natsort import natsorted
 
@@ -317,11 +318,31 @@ def test_get_cells():
         assert cell_io.get_cells("misc_format.abc")
 
 
-def test_cells_to_xml(tmpdir):
+def assert_cells_csv(csv_path):
+    cells_df = pd.read_csv(csv_path)
+    assert len(cells_df) == 65
+    assert cells_df.type.tolist() == type_vals
+    assert cells_df.x.tolist() == x_vals
+    assert cells_df.y.tolist() == y_vals
+    assert cells_df.z.tolist() == z_vals
+
+
+def test_save_cells(tmp_path):
     cells = cell_io.get_cells(xml_path)
-    tmp_cells_out_path = os.path.join(str(tmpdir), "cells.xml")
+    tmp_cells_out_path = tmp_path / "cells.xml"
+    cell_io.save_cells(cells, tmp_cells_out_path, save_csv=True)
+    assert cells == cell_io.get_cells(str(tmp_cells_out_path))
+
+    tmp_cells_out_path = tmp_path / "cells.csv"
+    cell_io.cells_to_csv(cells, tmp_cells_out_path)
+    assert_cells_csv(tmp_cells_out_path)
+
+
+def test_cells_to_xml(tmp_path):
+    cells = cell_io.get_cells(xml_path)
+    tmp_cells_out_path = tmp_path / "cells.xml"
     cell_io.cells_to_xml(cells, tmp_cells_out_path)
-    assert cells == cell_io.get_cells(tmp_cells_out_path)
+    assert cells == cell_io.get_cells(str(tmp_cells_out_path))
 
 
 def test_cells_xml_to_dataframe():
@@ -331,3 +352,10 @@ def test_cells_xml_to_dataframe():
     assert cells_df.x.tolist() == x_vals
     assert cells_df.y.tolist() == y_vals
     assert cells_df.z.tolist() == z_vals
+
+
+def test_cells_to_csv(tmp_path):
+    cells = cell_io.get_cells(xml_path)
+    tmp_cells_out_path = tmp_path / "cells.csv"
+    cell_io.cells_to_csv(cells, tmp_cells_out_path)
+    assert_cells_csv(tmp_cells_out_path)
diff --git a/tests/tests/test_IO/test_surfaces.py b/tests/tests/test_IO/test_surfaces.py
new file mode 100644
index 0000000..63d6ba9
--- /dev/null
+++ b/tests/tests/test_IO/test_surfaces.py
@@ -0,0 +1,26 @@
+from pathlib import Path
+
+from skimage import measure
+from skimage.draw import ellipsoid
+
+from brainglobe_utils.IO.surfaces import marching_cubes_to_obj
+
+test_obj = (
+    Path(__file__).parent.parent.parent / "data" / "IO" / "obj" / "test.obj"
+)
+
+
+def compare_text_files(a, b):
+    with open(a, "r") as file1:
+        a_contents = file1.readlines()
+    with open(b, "r") as file2:
+        b_contents = file2.readlines()
+    assert a_contents == b_contents
+
+
+def test_marching_cubes_to_obj(tmp_path):
+    output_file = tmp_path / "test.obj"
+    ellip_base = ellipsoid(6, 10, 16, levelset=True)
+    marching_cubes_out = measure.marching_cubes(ellip_base, 0)
+    marching_cubes_to_obj(marching_cubes_out, output_file)
+    compare_text_files(output_file, test_obj)
diff --git a/tests/tests/test_unit/test_IO/test_yaml_io.py b/tests/tests/test_IO/test_yaml_io.py
similarity index 100%
rename from tests/tests/test_unit/test_IO/test_yaml_io.py
rename to tests/tests/test_IO/test_yaml_io.py
diff --git a/brainglobe_utils/math/__init__.py b/tests/tests/test_cells/__init__.py
similarity index 100%
rename from brainglobe_utils/math/__init__.py
rename to tests/tests/test_cells/__init__.py
diff --git a/tests/tests/test_unit/test_cells/test_cells.py b/tests/tests/test_cells/test_cells.py
similarity index 100%
rename from tests/tests/test_unit/test_cells/test_cells.py
rename to tests/tests/test_cells/test_cells.py
diff --git a/tests/tests/test_unit/__init__.py b/tests/tests/test_general/__init__.py
similarity index 100%
rename from tests/tests/test_unit/__init__.py
rename to tests/tests/test_general/__init__.py
diff --git a/tests/tests/test_general/test_exceptions.py b/tests/tests/test_general/test_exceptions.py
new file mode 100644
index 0000000..5613f7a
--- /dev/null
+++ b/tests/tests/test_general/test_exceptions.py
@@ -0,0 +1,13 @@
+import pytest
+
+from brainglobe_utils.general.exceptions import CommandLineInputError
+
+
+def raise_CommandLineInputError():
+    raise CommandLineInputError("Error")
+
+
+def test_CommandLineInputError():
+    with pytest.raises(CommandLineInputError) as e:
+        raise_CommandLineInputError()
+    assert str(e.value) == "Error"
diff --git a/tests/tests/test_unit/test_general/test_list.py b/tests/tests/test_general/test_list.py
similarity index 100%
rename from tests/tests/test_unit/test_general/test_list.py
rename to tests/tests/test_general/test_list.py
diff --git a/tests/tests/test_unit/test_general/test_numerical.py b/tests/tests/test_general/test_numerical.py
similarity index 100%
rename from tests/tests/test_unit/test_general/test_numerical.py
rename to tests/tests/test_general/test_numerical.py
diff --git a/tests/tests/test_general/test_pathlib.py b/tests/tests/test_general/test_pathlib.py
new file mode 100644
index 0000000..dd42fec
--- /dev/null
+++ b/tests/tests/test_general/test_pathlib.py
@@ -0,0 +1,9 @@
+from pathlib import Path
+
+from brainglobe_utils.general.pathlib import append_to_pathlib_stem
+
+
+def test_append_to_pathlib_stem():
+    path = Path("path", "to", "file.txt")
+    appended_path = append_to_pathlib_stem(path, "_appended")
+    assert appended_path == Path("path", "to", "file_appended.txt")
diff --git a/tests/tests/test_unit/test_general/test_string.py b/tests/tests/test_general/test_string.py
similarity index 100%
rename from tests/tests/test_unit/test_general/test_string.py
rename to tests/tests/test_general/test_string.py
diff --git a/tests/tests/test_general/test_system.py b/tests/tests/test_general/test_system.py
new file mode 100644
index 0000000..7feb3f2
--- /dev/null
+++ b/tests/tests/test_general/test_system.py
@@ -0,0 +1,328 @@
+import os
+import platform
+import random
+from pathlib import Path
+from random import shuffle
+from unittest.mock import Mock, patch
+
+import pytest
+
+from brainglobe_utils.general import system
+from brainglobe_utils.general.string import get_text_lines
+
+data_dir = Path("tests", "data")
+cubes_dir = data_dir / "cubes"
+jabberwocky = data_dir / "general" / "jabberwocky.txt"
+jabberwocky_sorted = data_dir / "general" / "jabberwocky_sorted.txt"
+
+cubes = [
+    "pCellz222y2805x9962Ch1.tif",
+    "pCellz222y2805x9962Ch2.tif",
+    "pCellz258y3892x10559Ch1.tif",
+    "pCellz258y3892x10559Ch2.tif",
+    "pCellz413y2308x9391Ch1.tif",
+    "pCellz413y2308x9391Ch2.tif",
+    "pCellz416y2503x5997Ch1.tif",
+    "pCellz416y2503x5997Ch2.tif",
+    "pCellz418y5457x9489Ch1.tif",
+    "pCellz418y5457x9489Ch2.tif",
+    "pCellz433y4425x7552Ch1.tif",
+    "pCellz433y4425x7552Ch2.tif",
+]
+
+sorted_cubes_dir = [os.path.join(str(cubes_dir), cube) for cube in cubes]
+
+
+@pytest.fixture
+def mock_disk_usage():
+    """Fixture to mock shutil.disk_usage."""
+    return Mock(
+        return_value=(1000000, 500000, 500000)
+    )  # total, used, free space in bytes
+
+
+@pytest.fixture
+def mock_statvfs():
+    """Fixture to mock os.statvfs."""
+    mock_stats = Mock()
+    mock_stats.f_frsize = 1024  # Fragment size
+    mock_stats.f_bavail = 1000  # Free blocks
+    return mock_stats
+
+
+def test_replace_extension():
+    test_file = "test_file.sh"
+    test_ext = "txt"
+    test_ext_w_dot = ".txt"
+    validate_file = "test_file.txt"
+    assert validate_file == system.replace_extension(test_file, test_ext)
+    assert validate_file == system.replace_extension(test_file, test_ext_w_dot)
+
+
+def test_remove_leading_character():
+    assert ".ext" == system.remove_leading_character("..ext", ".")
+
+
+def test_ensure_directory_exists(tmpdir):
+    # string
+    exist_dir = os.path.join(tmpdir, "test_dir")
+    system.ensure_directory_exists(exist_dir)
+    assert os.path.exists(exist_dir)
+    os.rmdir(exist_dir)
+
+    # pathlib
+    exist_dir_pathlib = Path(tmpdir) / "test_dir2"
+    system.ensure_directory_exists(exist_dir_pathlib)
+    assert exist_dir_pathlib.exists()
+    exist_dir_pathlib.rmdir()
+
+
+def test_get_sorted_file_paths():
+    # test list
+    shuffled = sorted_cubes_dir.copy()
+    shuffle(shuffled)
+    assert system.get_sorted_file_paths(shuffled) == sorted_cubes_dir
+
+    # test dir
+    assert system.get_sorted_file_paths(cubes_dir) == sorted_cubes_dir
+    assert (
+        system.get_sorted_file_paths(cubes_dir, file_extension=".tif")
+        == sorted_cubes_dir
+    )
+
+    # test text file
+    # specifying utf8, as written on linux
+    assert system.get_sorted_file_paths(
+        jabberwocky, encoding="utf8"
+    ) == get_text_lines(jabberwocky_sorted, encoding="utf8")
+
+    # test unsupported
+    with pytest.raises(NotImplementedError):
+        system.get_sorted_file_paths(shuffled[0])
+
+
+def test_check_path_in_dir():
+    assert system.check_path_in_dir(jabberwocky, data_dir / "general")
+
+
+def test_get_num_processes():
+    cpu_count = 10
+    with patch(
+        "brainglobe_utils.general.system.psutil.cpu_count",
+        return_value=cpu_count,
+    ):
+        assert system.get_num_processes(min_free_cpu_cores=0) == cpu_count
+
+
+def test_max_processes():
+    max_proc = 5
+    correct_n = min(os.cpu_count(), max_proc)
+    assert correct_n == system.get_num_processes(
+        n_max_processes=max_proc, min_free_cpu_cores=0
+    )
+
+
+def test_max_processes_windows_low():
+    cpu_count = 10
+    with patch(
+        "brainglobe_utils.general.system.platform.system",
+        return_value="Windows",
+    ):
+        with patch(
+            "brainglobe_utils.general.system.psutil.cpu_count",
+            return_value=cpu_count,
+        ):
+            assert system.limit_cpus_windows(cpu_count) == cpu_count
+
+
+def test_max_processes_windows_high():
+    mock_cpu_count = 128
+    with patch(
+        "brainglobe_utils.general.system.platform.system",
+        return_value="Windows",
+    ):
+        with patch(
+            "brainglobe_utils.general.system.psutil.cpu_count",
+            return_value=mock_cpu_count,
+        ):
+            # 61 is max on Windows
+            assert system.limit_cpus_windows(mock_cpu_count) == 61
+
+
+@pytest.mark.parametrize("cores_available", [1, 100, 1000])
+def test_cores_available_in_slurm_environment(cores_available):
+    mock_slurm_parameters = Mock()
+    mock_slurm_parameters.allocated_cores = cores_available
+
+    with patch.dict(
+        "brainglobe_utils.general.system.os.environ", {"SLURM_JOB_ID": "1"}
+    ), patch(
+        "brainglobe_utils.general.system.slurmio.SlurmJobParameters",
+        return_value=mock_slurm_parameters,
+    ):
+        assert system.get_cores_available() == cores_available
+
+
+@pytest.mark.parametrize("cores_available", [1, 100, 1000])
+def test_cores_available(cores_available):
+    with patch(
+        "brainglobe_utils.general.system.psutil.cpu_count",
+        return_value=cores_available,
+    ):
+        assert system.get_cores_available() == cores_available
+
+
+@pytest.mark.parametrize(
+    "ram_needed_per_cpu, fraction_free_ram, max_ram_usage, "
+    "free_system_ram, expected_cores",
+    [
+        (
+            1024**3,
+            0.1,
+            None,
+            16 * 1024**3,
+            14,
+        ),  # 1 GB per core, 0.1 fraction free, no max ram,
+        # 16GB free on the system, expect 14
+        (
+            2 * 1024**3,
+            0.5,
+            None,
+            256 * 1024**3,
+            64,
+        ),  # 1 GB per core, 0.5 fraction free, no max ram,
+        # 256GB free on the system, expect 64
+        (
+            1024**3,
+            0.5,
+            10 * 1024**3,
+            256 * 1024**3,
+            5,
+        ),  # 1 GB per core, 0.5 fraction free, 10Gb max ram,
+        # 256GB free on the system, expect 5
+    ],
+)
+def test_how_many_cores_with_sufficient_ram(
+    ram_needed_per_cpu,
+    fraction_free_ram,
+    max_ram_usage,
+    free_system_ram,
+    expected_cores,
+):
+    with patch(
+        "brainglobe_utils.general.system.get_free_ram",
+        return_value=free_system_ram,
+    ):
+        assert (
+            system.how_many_cores_with_sufficient_ram(
+                ram_needed_per_cpu,
+                fraction_free_ram,
+                max_ram_usage=max_ram_usage,
+            )
+            == expected_cores
+        )
+
+
+def test_how_many_cores_with_sufficient_ram_in_slurm_environment():
+    ram_needed_per_cpu = 1024**3  # 1 GB
+    free_system_ram = 16 * 1024**3  # 16 GB
+
+    mock_slurm_parameters = Mock()
+    mock_slurm_parameters.allocated_memory = free_system_ram
+
+    with patch.dict(
+        "brainglobe_utils.general.system.os.environ", {"SLURM_JOB_ID": "1"}
+    ), patch(
+        "brainglobe_utils.general.system.slurmio.SlurmJobParameters",
+        return_value=mock_slurm_parameters,
+    ):
+        assert (
+            system.how_many_cores_with_sufficient_ram(ram_needed_per_cpu) == 14
+        )  # (0.9 * 16) GB / 1 GB per core
+
+
+def test_disk_free_gb_windows(mock_disk_usage):
+    with patch(
+        "brainglobe_utils.general.system.platform.system",
+        return_value="Windows",
+    ), patch(
+        "brainglobe_utils.general.system.os.path.splitdrive",
+        return_value=("C:\\", ""),
+    ), patch(
+        "brainglobe_utils.general.system.shutil.disk_usage", mock_disk_usage
+    ):
+        free_space = system.disk_free_gb("C:\\path\\to\\file")
+        assert free_space == 500000 / 1024**3
+
+
+@pytest.mark.skipif(
+    platform.system() == "Windows",
+    reason="os.statvfs not available on Windows",
+)
+def test_disk_free_gb_linux(mock_statvfs):
+    with patch(
+        "brainglobe_utils.general.system.platform.system", return_value="Linux"
+    ), patch(
+        "brainglobe_utils.general.system.os.statvfs", return_value=mock_statvfs
+    ):
+        free_space = system.disk_free_gb("/path/to/file")
+        assert free_space == (1024 * 1000) / 1024**3  # Free space in GB
+
+
+@pytest.mark.skipif(
+    platform.system() == "Windows",
+    reason="os.statvfs not available on Windows",
+)
+def test_disk_free_gb_macos(mock_statvfs):
+    with patch(
+        "brainglobe_utils.general.system.platform.system",
+        return_value="Darwin",
+    ), patch(
+        "brainglobe_utils.general.system.os.statvfs", return_value=mock_statvfs
+    ):
+        free_space = system.disk_free_gb("/path/to/file")
+        assert free_space == (1024 * 1000) / 1024**3  # Free space in GB
+
+
+def test_get_free_ram():
+    mock_free_ram = 1000000001
+
+    mock_virtual_memory = Mock()
+    mock_virtual_memory.available = mock_free_ram
+
+    with patch(
+        "brainglobe_utils.general.system.psutil.virtual_memory",
+        return_value=mock_virtual_memory,
+    ):
+        assert system.get_free_ram() == mock_free_ram
+
+
+def write_n_random_files(n, dir, min_size=32, max_size=2048):
+    sizes = random.sample(range(min_size, max_size), n)
+    for size in sizes:
+        with open(os.path.join(dir, str(size)), "wb") as fout:
+            fout.write(os.urandom(size))
+
+
+def test_delete_directory_contents_with_progress(tmp_path):
+    delete_dir = tmp_path / "delete_dir"
+    os.mkdir(delete_dir)
+    write_n_random_files(10, delete_dir)
+
+    # check the directory isn't empty first
+    assert not os.listdir(delete_dir) == []
+
+    system.delete_directory_contents(delete_dir, progress=True)
+    assert os.listdir(delete_dir) == []
+
+
+def test_delete_directory_contents(tmp_path):
+    delete_dir = tmp_path / "delete_dir"
+    os.mkdir(delete_dir)
+    write_n_random_files(10, delete_dir)
+
+    # check the directory isn't empty first
+    assert not os.listdir(delete_dir) == []
+
+    system.delete_directory_contents(delete_dir, progress=False)
+    assert os.listdir(delete_dir) == []
diff --git a/tests/tests/test_unit/test_IO/__init__.py b/tests/tests/test_image/__init__.py
similarity index 100%
rename from tests/tests/test_unit/test_IO/__init__.py
rename to tests/tests/test_image/__init__.py
diff --git a/tests/tests/test_unit/test_image/test_binning.py b/tests/tests/test_image/test_binning.py
similarity index 100%
rename from tests/tests/test_unit/test_image/test_binning.py
rename to tests/tests/test_image/test_binning.py
diff --git a/tests/tests/test_unit/test_image/test_masking.py b/tests/tests/test_image/test_masking.py
similarity index 100%
rename from tests/tests/test_unit/test_image/test_masking.py
rename to tests/tests/test_image/test_masking.py
diff --git a/tests/tests/test_unit/test_image/test_scale.py b/tests/tests/test_image/test_scale.py
similarity index 100%
rename from tests/tests/test_unit/test_image/test_scale.py
rename to tests/tests/test_image/test_scale.py
diff --git a/tests/tests/test_unit/test_array/__init__.py b/tests/tests/test_pandas/__init__.py
similarity index 100%
rename from tests/tests/test_unit/test_array/__init__.py
rename to tests/tests/test_pandas/__init__.py
diff --git a/tests/tests/test_unit/test_pandas/test_pandas_misc.py b/tests/tests/test_pandas/test_pandas_misc.py
similarity index 78%
rename from tests/tests/test_unit/test_pandas/test_pandas_misc.py
rename to tests/tests/test_pandas/test_pandas_misc.py
index 47f6938..3a3a7ac 100644
--- a/tests/tests/test_unit/test_pandas/test_pandas_misc.py
+++ b/tests/tests/test_pandas/test_pandas_misc.py
@@ -1,6 +1,5 @@
 import numpy as np
 import pandas as pd
-import pytest
 
 from brainglobe_utils.pandas import misc as pandas_misc
 
@@ -22,18 +21,6 @@ def test_sanitise_df():
     assert sanitised_df.equals(df_with_nan)
 
 
-def test_move_column_first():
-    column_first = pandas_misc.move_column_first(df_with_nan, "number")
-    assert column_first.columns[0] == columns[1]
-    assert column_first[columns[1]][1] == 15
-
-    with pytest.raises(ValueError):
-        pandas_misc.move_column_first(data_with_nan, "number")
-
-    with pytest.raises(ValueError):
-        pandas_misc.move_column_first(df_with_nan, columns)
-
-
 def test_safe_pandas_concat() -> None:
     """
     Test the following:
diff --git a/tests/tests/test_unit/test_cells/__init__.py b/tests/tests/test_qtpy/__init__.py
similarity index 100%
rename from tests/tests/test_unit/test_cells/__init__.py
rename to tests/tests/test_qtpy/__init__.py
diff --git a/tests/tests/test_unit/test_qtpy/test_collapsible_widget.py b/tests/tests/test_qtpy/test_collapsible_widget.py
similarity index 100%
rename from tests/tests/test_unit/test_qtpy/test_collapsible_widget.py
rename to tests/tests/test_qtpy/test_collapsible_widget.py
diff --git a/tests/tests/test_unit/test_qtpy/test_logo.py b/tests/tests/test_qtpy/test_logo.py
similarity index 100%
rename from tests/tests/test_unit/test_qtpy/test_logo.py
rename to tests/tests/test_qtpy/test_logo.py
diff --git a/tests/tests/test_unit/test_array/test_array_misc.py b/tests/tests/test_unit/test_array/test_array_misc.py
deleted file mode 100644
index 8b14e8b..0000000
--- a/tests/tests/test_unit/test_array/test_array_misc.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import numpy as np
-import pytest
-
-from brainglobe_utils.array import misc
-
-
-def test_midpoints_of_series():
-    assert (
-        np.array([1, 3, 5, 7, 9])
-        == misc.midpoints_of_series(np.array([0, 2, 4, 6, 8, 10]))
-    ).all()
-
-    assert (
-        np.array([-3, 4.5, 65])
-        == misc.midpoints_of_series(np.array([-5, -1, 10, 120]))
-    ).all()
-
-    assert np.array([-499.9, 500.1, 50500]) == pytest.approx(
-        misc.midpoints_of_series(np.array([-1000, 0.20, 1000, 100000]))
-    )
diff --git a/tests/tests/test_unit/test_cells/test_cell_utils.py b/tests/tests/test_unit/test_cells/test_cell_utils.py
deleted file mode 100644
index 905d0de..0000000
--- a/tests/tests/test_unit/test_cells/test_cell_utils.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import os
-from math import isclose
-
-import numpy as np
-
-from brainglobe_utils.cells.utils import get_cell_location_array
-
-data_dir = os.path.join("tests", "data")
-xml_path = os.path.join(data_dir, "cells", "cells_two_types.xml")
-
-
-def test_get_cell_location_array():
-    cell_array = get_cell_location_array(xml_path)
-    assert cell_array.shape == (827, 3)
-    assert (cell_array[0, :] == np.array([679, 80, 660])).all()
-
-    # cells only
-    cell_array = get_cell_location_array(xml_path, cells_only=True)
-    assert cell_array.shape == (787, 3)
-
-    # scaling w rounding
-    cell_array = get_cell_location_array(
-        xml_path, cell_position_scaling=[0.10, 10.1, 101]
-    )
-    assert (cell_array[0, :] == np.array([68, 808, 66660])).all()
-
-    # scaling w- rounding
-    cell_array = get_cell_location_array(
-        xml_path, cell_position_scaling=[0.101, 10.1, 101], integer=False
-    )
-    assert isclose(
-        cell_array[0, 0],
-        68.579,
-        abs_tol=0.01,
-    )
-    assert isclose(
-        cell_array[0, 1],
-        808,
-        abs_tol=0.01,
-    )
-    assert isclose(
-        cell_array[0, 2],
-        66660,
-        abs_tol=0.01,
-    )
diff --git a/tests/tests/test_unit/test_general/__init__.py b/tests/tests/test_unit/test_general/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/tests/test_unit/test_general/test_system.py b/tests/tests/test_unit/test_general/test_system.py
deleted file mode 100644
index 20bbd47..0000000
--- a/tests/tests/test_unit/test_general/test_system.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import os
-import random
-from pathlib import Path
-from random import shuffle
-from unittest.mock import patch
-
-import pytest
-
-from brainglobe_utils.general import system
-from brainglobe_utils.general.string import get_text_lines
-
-data_dir = Path("tests", "data")
-cubes_dir = data_dir / "cubes"
-jabberwocky = data_dir / "general" / "jabberwocky.txt"
-jabberwocky_sorted = data_dir / "general" / "jabberwocky_sorted.txt"
-
-cubes = [
-    "pCellz222y2805x9962Ch1.tif",
-    "pCellz222y2805x9962Ch2.tif",
-    "pCellz258y3892x10559Ch1.tif",
-    "pCellz258y3892x10559Ch2.tif",
-    "pCellz413y2308x9391Ch1.tif",
-    "pCellz413y2308x9391Ch2.tif",
-    "pCellz416y2503x5997Ch1.tif",
-    "pCellz416y2503x5997Ch2.tif",
-    "pCellz418y5457x9489Ch1.tif",
-    "pCellz418y5457x9489Ch2.tif",
-    "pCellz433y4425x7552Ch1.tif",
-    "pCellz433y4425x7552Ch2.tif",
-]
-
-sorted_cubes_dir = [os.path.join(str(cubes_dir), cube) for cube in cubes]
-
-
-def test_replace_extension():
-    test_file = "test_file.sh"
-    test_ext = "txt"
-    test_ext_w_dot = ".txt"
-    validate_file = "test_file.txt"
-    assert validate_file == system.replace_extension(test_file, test_ext)
-    assert validate_file == system.replace_extension(test_file, test_ext_w_dot)
-
-
-def test_remove_leading_character():
-    assert ".ext" == system.remove_leading_character("..ext", ".")
-
-
-def test_ensure_directory_exists(tmpdir):
-    # string
-    exist_dir = os.path.join(tmpdir, "test_dir")
-    system.ensure_directory_exists(exist_dir)
-    assert os.path.exists(exist_dir)
-    os.rmdir(exist_dir)
-
-    # pathlib
-    exist_dir_pathlib = Path(tmpdir) / "test_dir2"
-    system.ensure_directory_exists(exist_dir_pathlib)
-    assert exist_dir_pathlib.exists()
-    exist_dir_pathlib.rmdir()
-
-
-def test_get_sorted_file_paths():
-    # test list
-    shuffled = sorted_cubes_dir.copy()
-    shuffle(shuffled)
-    assert system.get_sorted_file_paths(shuffled) == sorted_cubes_dir
-
-    # test dir
-    assert system.get_sorted_file_paths(cubes_dir) == sorted_cubes_dir
-    assert (
-        system.get_sorted_file_paths(cubes_dir, file_extension=".tif")
-        == sorted_cubes_dir
-    )
-
-    # test text file
-    # specifying utf8, as written on linux
-    assert system.get_sorted_file_paths(
-        jabberwocky, encoding="utf8"
-    ) == get_text_lines(jabberwocky_sorted, encoding="utf8")
-
-    # test unsupported
-    with pytest.raises(NotImplementedError):
-        system.get_sorted_file_paths(shuffled[0])
-
-
-def test_check_path_in_dir():
-    assert system.check_path_in_dir(jabberwocky, data_dir / "general")
-
-
-def test_get_num_processes():
-    cpu_count = 10
-    with patch(
-        "brainglobe_utils.general.system.psutil.cpu_count",
-        return_value=cpu_count,
-    ):
-        assert system.get_num_processes(min_free_cpu_cores=0) == cpu_count
-
-
-def test_max_processes():
-    max_proc = 5
-    correct_n = min(os.cpu_count(), max_proc)
-    assert correct_n == system.get_num_processes(
-        n_max_processes=max_proc, min_free_cpu_cores=0
-    )
-
-
-def test_max_processes_windows_low():
-    cpu_count = 10
-    with patch(
-        "brainglobe_utils.general.system.platform.system",
-        return_value="Windows",
-    ):
-        with patch(
-            "brainglobe_utils.general.system.psutil.cpu_count",
-            return_value=cpu_count,
-        ):
-            assert system.limit_cpus_windows(cpu_count) == cpu_count
-
-
-def test_max_processes_windows_high():
-    cpu_count = 128
-    with patch(
-        "brainglobe_utils.general.system.platform.system",
-        return_value="Windows",
-    ):
-        with patch(
-            "brainglobe_utils.general.system.psutil.cpu_count",
-            return_value=cpu_count,
-        ):
-            # 61 is max on Windows
-            assert system.limit_cpus_windows(cpu_count) == 61
-
-
-class Paths:
-    def __init__(self, directory):
-        self.one = directory / "one.aaa"
-        self.two = directory / "two.bbb"
-        self.tmp__three = directory / "three.ccc"
-        self.tmp__four = directory / "four.ddd"
-
-
-def test_delete_tmp(tmpdir):
-    tmpdir = Path(tmpdir)
-    paths = Paths(tmpdir)
-    for attr, path in paths.__dict__.items():
-        path.touch()
-        print(path)
-    assert len([child for child in tmpdir.iterdir()]) == 4
-    system.delete_temp(tmpdir, paths)
-    assert len([child for child in tmpdir.iterdir()]) == 2
-
-    system.delete_temp(tmpdir, paths)
-
-
-def write_n_random_files(n, dir, min_size=32, max_size=2048):
-    sizes = random.sample(range(min_size, max_size), n)
-    for size in sizes:
-        with open(os.path.join(dir, str(size)), "wb") as fout:
-            fout.write(os.urandom(size))
-
-
-def test_delete_directory_contents(tmpdir):
-    delete_dir = os.path.join(str(tmpdir), "delete_dir")
-    os.mkdir(delete_dir)
-    write_n_random_files(10, delete_dir)
-
-    # check the directory isn't empty first
-    assert not os.listdir(delete_dir) == []
-
-    system.delete_directory_contents(delete_dir, progress=True)
-    assert os.listdir(delete_dir) == []
diff --git a/tests/tests/test_unit/test_image/__init__.py b/tests/tests/test_unit/test_image/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/tests/test_unit/test_image/test_objects.py b/tests/tests/test_unit/test_image/test_objects.py
deleted file mode 100644
index 212910c..0000000
--- a/tests/tests/test_unit/test_image/test_objects.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import numpy as np
-from skimage.measure import label
-
-from brainglobe_utils.image import objects
-
-many_objects = np.array(
-    [
-        [1, 1, 0, 1, 1, 1, 1, 0, 0],
-        [1, 1, 0, 1, 1, 1, 1, 0, 0],
-        [0, 0, 0, 1, 1, 1, 1, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 1],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 1],
-        [0, 1, 1, 1, 1, 1, 0, 0, 0],
-        [0, 1, 1, 1, 1, 1, 0, 0, 1],
-        [0, 1, 1, 1, 1, 1, 0, 0, 1],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-    ],
-    dtype=bool,
-)
-
-largest_2 = np.array(
-    [
-        [0, 0, 0, 1, 1, 1, 1, 0, 0],
-        [0, 0, 0, 1, 1, 1, 1, 0, 0],
-        [0, 0, 0, 1, 1, 1, 1, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 1, 1, 1, 1, 1, 0, 0, 0],
-        [0, 1, 1, 1, 1, 1, 0, 0, 0],
-        [0, 1, 1, 1, 1, 1, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-    ],
-    dtype=bool,
-)
-
-largest = np.array(
-    [
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 1, 1, 1, 1, 1, 0, 0, 0],
-        [0, 1, 1, 1, 1, 1, 0, 0, 0],
-        [0, 1, 1, 1, 1, 1, 0, 0, 0],
-        [0, 0, 0, 0, 0, 0, 0, 0, 0],
-    ],
-    dtype=bool,
-)
-
-
-def test_get_largest_non_zero_object():
-    assert (
-        largest == objects.get_largest_non_zero_object(label(many_objects))
-    ).all()
-
-
-def test_keep_n_largest_objects():
-    assert (largest == objects.keep_n_largest_objects(many_objects)).all()
-    assert (
-        largest_2 == objects.keep_n_largest_objects(many_objects, n=2)
-    ).all()
diff --git a/tests/tests/test_unit/test_image/test_shape.py b/tests/tests/test_unit/test_image/test_shape.py
deleted file mode 100644
index f3c6e8a..0000000
--- a/tests/tests/test_unit/test_image/test_shape.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import pytest
-
-from brainglobe_utils.image.shape import convert_shape_dict_to_array_shape
-
-
-def test_convert_shape_dict_to_array_shape():
-    shape_dict = {"x": "100", "y": "30"}
-    assert convert_shape_dict_to_array_shape(shape_dict) == (30, 100)
-    assert convert_shape_dict_to_array_shape(shape_dict, type="fiji") == (
-        100,
-        30,
-    )
-
-    shape_dict["z"] = 10
-    assert convert_shape_dict_to_array_shape(shape_dict) == (30, 100, 10)
-
-    with pytest.raises(NotImplementedError):
-        convert_shape_dict_to_array_shape(shape_dict, type="new type")
diff --git a/tests/tests/test_unit/test_image/test_size.py b/tests/tests/test_unit/test_image/test_size.py
deleted file mode 100644
index de3ed33..0000000
--- a/tests/tests/test_unit/test_image/test_size.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import numpy as np
-
-from brainglobe_utils.image import size
-
-img = np.array(
-    [
-        [0, 0, 1, 1, 0],
-        [0, 10, 100, 10, 0],
-        [0, 1, 10, 1, 0],
-    ]
-)
-
-resized_test = np.array(
-    [
-        [0, 0, 0, 1, 1, 1, 1, 0, 0],
-        [0, 0, 0, 1, 1, 1, 1, 0, 0],
-        [0, 0, 0, 1, 1, 1, 1, 0, 0],
-        [0, 10, 10, 100, 100, 10, 10, 0, 0],
-        [0, 10, 10, 100, 100, 10, 10, 0, 0],
-        [0, 10, 10, 100, 100, 10, 10, 0, 0],
-        [0, 10, 10, 100, 100, 10, 10, 0, 0],
-        [0, 1, 1, 10, 10, 1, 1, 0, 0],
-        [0, 1, 1, 10, 10, 1, 1, 0, 0],
-        [0, 1, 1, 10, 10, 1, 1, 0, 0],
-    ]
-)
-
-
-def test_resize_array():
-    target_size = (10, 9)
-    resized_image = size.resize_array(img, target_size)
-    assert (resized_image == resized_test).all()
diff --git a/tests/tests/test_unit/test_pandas/__init__.py b/tests/tests/test_unit/test_pandas/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/tests/test_unit/test_pandas/test_pandas_query.py b/tests/tests/test_unit/test_pandas/test_pandas_query.py
deleted file mode 100644
index 93c79ec..0000000
--- a/tests/tests/test_unit/test_pandas/test_pandas_query.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import numpy as np
-import pandas as pd
-
-from brainglobe_utils.pandas import query as pandas_query
-
-
-def test_column_as_array():
-    columns = ["name", "number"]
-    data = [["one", 1], ["two", 8.6], ["three", 100]]
-    df = pd.DataFrame(data, columns=columns)
-    array = np.array([1, 8.6, 100])
-    assert (pandas_query.column_as_array(df, columns[1]) == array).all()
diff --git a/tests/tests/test_unit/test_qtpy/__init__.py b/tests/tests/test_unit/test_qtpy/__init__.py
deleted file mode 100644
index e69de29..0000000