Update DTDV401_overview.ipynb

Author: giovannivolpe

tutorials/4-developers/DTDV401_overview.ipynb

@@ -101,7 +101,7 @@
    "source": [
     "## 3. Type Hints for Code Readability\n",
     "\n",
-    "Since type hints are used extensively to improve code readability in accordance with [DTDT411. Style Guide](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/4-developers/DTDV411_style.ipynb), DeepTrack2 introduces a few custom type hints for internal use, which are declared in the [types.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/types.py) module."
+    "Since type hints are used extensively to improve code readability in accordance with [DTDV411_style.ipynb](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/4-developers/DTDV411_style.ipynb), DeepTrack2 introduces a few custom type hints for internal use, which are declared in the [types.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/types.py) module."
    ]
   },
   {
@@ -169,13 +169,13 @@
     "\n",
     "    In particular, the [core.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/backend/core.py) module contains the `DeepTrackNode` class which is used to represent a node in a computation graph, which when used together with the `DeepTrackDataObject` class can store data and compute new data based on its dependencies and child nodes. These classes track dependencies and validate data with ID and index addresses.  \n",
     "\n",
-    "    The [core.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/backend/core.py) module also provides the base class for the [features.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/features.py) module, which is the largest module in DeepTrack2 in terms of code volume, and provides the base class for all other modules in the deeptrack directory; the only exceptions are [image.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/image.py) and [properties.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/properties.py).\n",
+    "    The [core.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/backend/core.py) module also provides the base class for the [features.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/features.py) module, which is the largest module in DeepTrack2 in terms of code volume, and provides the base class for all other modules in the deeptrack directory; the only exceptions is [properties.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/properties.py).\n",
     "\n",
     "- [mie.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/backend/mie.py) provides functions to perform Mie scattering calculations often used in simulations.\n",
     "\n",
     "    In particular, it provides methods to compute coefficients for both spherical and stratified spherical harmonics, and to calculate the spherical harmonics of the Mie field with an iterative method.\n",
     "\n",
-    "- [pint_definition.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/backend/pint_definition.py)extends Pint's default definitions by introducing project-specific constants and unit modifications for flexible calculations in the context of DeepTrack2.\n",
+    "- [pint_definition.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/backend/pint_definition.py) extends Pint's default definitions by introducing project-specific constants and unit modifications for flexible calculations in the context of DeepTrack2.\n",
     "\n",
     "- [polynomials.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/backend/polynomials.py) provides a set of functions which compute Bessel and Riccati-Bessel polynomials and their derivatives.\n",
     "\n",
@@ -242,7 +242,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The value of a `Property` has no datatype restrictions and can represent a constant, a function, a list, an `Image`, etc."
+    "The value of a `Property` has no datatype restrictions and can represent a constant, a function, a list, etc."
    ]
   },
   {
@@ -263,19 +263,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 7. Containers for Array-Like Structures\n",
-    "\n",
-    "The [image.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/image.py) module only contains a single class, namely `Image`. This class serves as a wrapper for array-like data and provides a unified interface for array operations and property management with NumPy and PyTorch compatibility.\n",
-    "\n",
-    "Several utility functions are also included, which can be used to manipulate `Image` objects within pipelines, such as image coercion to ensure a consistent type across a series of images, or padding to optimize Fast Fourier Transform performance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## 8. High-Level Modules\n",
+    "## 7. High-Level Modules\n",
     "\n",
     "The remaoning modules jointly implement the main functionality of DeepTrack2, which is synthetic data generation using simulations."
    ]
@@ -303,8 +291,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
-    "## 9. Handle and Utility Modules\n",
+    "## 8. Handle and Utility Modules\n",
     "\n",
     "The following modules play a mediating role between external libraries and DeepTrack2, such as integrating Pytorch classes or Numpy functions, as well as utilities for type consistency and radial center calculations."
    ]
@@ -313,7 +300,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 9.1. Numpy\n",
+    "### 8.1. Numpy\n",
     "The following modules provide handles to enable the use of NumPy functions with DeepTrack2 objects as well as mathematical utilities:\n",
     "\n",
     "- [elementwise.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/elementwise.py) provides handles to perform various elementary NumPy functions elementwise and sequentially when using `Feature` objects. For example, some of these functions include trigonometric, hyperbolic, rounding, exponents.\n",
@@ -327,7 +314,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 9.2. Pytorch\n",
+    "### 8.2. Pytorch\n",
     "Located in the [pytorch](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/pytorch/) directory, there are two modules to facilitate PyTorch integration with DeepTrack2 objects:\n",
     "\n",
     "- [pytorch.data.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/pytorch/data.py) extends the PyTorch [Dataset](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html) class to work with DeepTrack2 `Image` objects.\n",
@@ -339,7 +326,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 9.3. Deeplay\n",
+    "### 8.3. Deeplay\n",
     "\n",
     "The init file in [deeplay/\\_\\_init__.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/deeplay/__init__.py) enables users to import Deeplay from DeepTrack2 with:\n",
     "\n",
@@ -352,7 +339,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 9.4. Other Modules\n",
+    "### 8.4. Other Modules\n",
     "\n",
     "- [utils.py](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/utils.py) provides various utilities to streamline common operations, ensuring type and argument consistency with various check methods and safe call.\n",
     "\n",
@@ -363,7 +350,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 10. Unit testing\n",
+    "## 9. Unit testing\n",
     "\n",
     "The unit testing scripts are found in the [tests](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/deeptrack/tests/) directory, and follow the same structure as the [deeptrack](https://github.com/DeepTrackAI/DeepTrack2/tree/develop/deeptrack) directory with a test script for each module."
    ]