Welcome to Pizza3 Discussions!

[ovitrac]

👋 Welcome to the 🍕Pizza3 Discussions!

We’re excited to open this space so that everyone can:

• 💬 Ask questions and explore ideas related to multiscale modeling with [LAMMPS](https://www.lammps.org/).
• 🤝 Engage with each other, sharing coarse-grained, meso, and atomistic approaches.
• 🚀 Brainstorm new directions for code templates, future evolution, and advanced functionalities.

Before you start a discussion, please read what is already available in this repository. It might save you time and give you a head start!

🍕 Topics to Explore

1. Reusable Codelets & Templates
   Pizza3 uses the concept of “codelets” (see examples in
   📁 [pizza.dscript.examples/](https://github.com/ovitrac/Pizza3/tree/main/pizza.dscript.examples)).
   Turn any existing LAMMPS code into a reusable template for various modeling scales. Discuss how to extend, optimize, or share your codelets!

2. Post-Processing & Visualization
   Curious about advanced visualization or new analysis workflows? Check out
   📁 [post/](https://github.com/ovitrac/Pizza3/tree/main/post/).
   Let’s chat about ways to integrate 2D/3D visuals, combine with external tools, or propose additional post-processing features.

3. Static Interpreter & Core Library
   The core of Pizza3 lives in
   📁 [pizza/](https://github.com/ovitrac/Pizza3/tree/main/pizza/), including a static interpreter and object-oriented library. Potential topics:

   • Compatibility with region, group, forcefield objects
   • Seamless transformations like move/displace/distort/deform
   • Restart file consolidation by merging DUMP & INPUT files from LAMMPS

4. Large Language Models (LLMs) & DSCRIPT
   Pizza3’s DSCRIPT format is well-suited to LLMs. This is a hot topic—share your experiences, questions, or suggestions on using AI-driven text generators and “zero Python” approaches.

5. Integration with LAMMPS-GUI
   We’re closely following the evolution of LAMMPS-GUI. How can we make Pizza3 and LAMMPS-GUI even more powerful for easy setups, fast testing, and user-friendly workflows?

---

💬 How to Participate

• Ask any question—no matter how small or complex.
• Share your ideas or workflows, including code snippets or references to relevant issues/files.
• Engage with others by replying to open discussions, reacting with emojis, or providing feedback.
• Encourage a helpful, welcoming, and inclusive environment. We all learn better together!

🍕 Ready to Start?

Introduce yourself below, or dive straight into a new discussion! We look forward to collaborating on Pizza3 development and unleashing the full potential of multiscale modeling in [LAMMPS](https://www.lammps.org/).

Thanks for being here, and happy coding! ✨

[bgut333]

Two ideas for expansion to better enable use:

1. Add a way to build up structure from micro-scale to macro scale (ref: "food" library" system APT repo) and have a set of "reference structures for d food arch-types (fluids: Newtonian, yield,colloids, emulsions, soft solids, composites, fracture solids, fiber foods, gels)
2. Add physics like phase fields to better jump time scale and length scale

[bgut333]

https://github.com/ovitrac/SFPPy

[ovitrac]

You are right a basic but solid class inheritance was implemented SFPPy even if I did not have time to complete the project. The core is there and with a little effort the existing Matlab code could be converted to Python.

---

Pizza³ implements two kinds of inheritance: one static (the one you refer to with SFPPy, and used by script,forcefield,generic) and one dynamic more versatile (used by dscript, dforcefield). Static inheritance is robust and safe but it requires programming. Let’s start with basic examples.

---

1. Analogy of Static Inheritance vs. Dynamic Inheritance

a. Static Inheritance (Class Inheritance: A → B)

• In Python, static inheritance refers to the traditional class-based inheritance, where a child class (B) inherits attributes and methods from a parent class (A).
• Example:

  class A:
      def __init__(self):
          self.x = 1
          self.y = 2
  
  class B(A):
      def __init__(self):
          super().__init__()
          self.z = 3
  
  obj = B()
  print(obj.x, obj.y, obj.z)  # Output: 1 2 3

  • Here, B inherits x and y from A and adds its own attribute z.

b. Dynamic Inheritance (Field Inheritance: sA + sB or sA << sB)

• Dynamic inheritance refers to combining or merging fields from two structures (sA and sB) at runtime, rather than at the class definition level.
• Two common operations for dynamic inheritance are:
  1. sC = sA + sB:
     • Adds all fields from sB to sA.
     • If a field exists in both sA and sB, the value from sB replaces the value in sA.
     • Example:

       sA = paramauto(a=1, b=2)
       sB = paramauto(b=3, c=4)
       sC = sA + sB
       print(sC)  # Output: {'a': 1, 'b': 3, 'c': 4}

  2. sC = sA << sB:
     • Only replaces undefined (None) or empty values in sA with values from sB.
     • Fields already defined in sA remain unchanged.
     • Example:

       sA = paramauto(a=1, b=None)
       sB = paramauto(b=2, c=3)
       sC = sA << sB
       print(sC)  # Output: {'a': 1, 'b': 2, 'c': 3}

---

2. Examples Using paramauto

The paramauto class (from pizza.private.mstruct) allows you to define fields with static values or mathematical expressions involving other fields. Here are some examples:

a. Basic Usage

from pizza.private.mstruct import paramauto

# Define a paramauto object with static values
s = paramauto(a=1, b=2)
print(s)  # Output: {'a': 1, 'b': 2}

b. Mathematical Expressions

You can define fields using mathematical expressions that reference other fields using ${field} syntax:

s = paramauto(a=1, b="${a} + 1", c="${a} + ${b}")
print(s)  # Output: {'a': 1, 'b': 2, 'c': 3}

c. Dynamic Inheritance with +

sA = paramauto(a=1, b=2)
sB = paramauto(b=3, c=4)
sC = sA + sB
print(sC)  # Output: {'a': 1, 'b': 3, 'c': 4}

d. Dynamic Inheritance with <<

sA = paramauto(a=1, b=None)
sB = paramauto(b=2, c=3)
sC = sA << sB
print(sC)  # Output: {'a': 1, 'b': 2, 'c': 3}

e. Combining Static and Dynamic Fields

sA = paramauto(a=1, b="${a} + 1")
sB = paramauto(b=3, c="${b} * 2")
sC = sA + sB
print(sC)  # Output: {'a': 1, 'b': 3, 'c': 6}

f. Handling Undefined Fields

sA = paramauto(a=1, b=None, c="${a} + ${b}")
sB = paramauto(b=2)
sC = sA << sB
print(sC)  # Output: {'a': 1, 'b': 2, 'c': 3}

g. Nested Expressions

s = paramauto(a=1, b="${a} + 1", c="${b} * 2", d="${c} + ${a}")
print(s)  # Output: {'a': 1, 'b': 2, 'c': 4, 'd': 5}

---

3. Key Differences Between Static and Dynamic Inheritance

Feature	Static Inheritance (Class)	Dynamic Inheritance (Field)
Definition	Defined at class creation.	Defined at runtime.
Flexibility	Less flexible (fixed hierarchy).	More flexible (runtime merging).
Use Case	Code reuse and polymorphism.	Combining configurations or data.
Example	class B(A): ...	sC = sA + sB or sC = sA << sB

---

4. Summary

• Static inheritance is used for defining class hierarchies and sharing behavior between classes.
• Dynamic inheritance is used for combining or merging fields between structures at runtime.
• The paramauto class allows you to define fields with static values or mathematical expressions, making it a powerful tool for dynamic configurations.

To be complete, below is a detailed comparison of static inheritance (class-based) and dynamic inheritance (field-based) in terms of their pros and cons. This will help you understand when to use each approach and their trade-offs.
Keep in mind that Pizza³ uses a hybrid approach with:

• codelets tracking the correct LAMMPS syntax stored in classes (the best since the syntax is almost immutable)
• data containers derived from param or paramauto, which orient the way codelets are used via variable interpolation and expression evaluation.

As a result, an existing class with default parameters and expressions can be muted either to another class (if the parameters are class attributes) or to a new instance (if the parameters are instance attributes). The forcefield class generic uses the first approach, and the user is invited to follow the generic template to customize his classes. Instead, the dforcefield class tunes an existing instance at runtime by injecting new parameters.

---

Static Inheritance (Class-Based: A → B)

PROS

1. Code Reusability:

   • Allows you to reuse code by inheriting attributes and methods from a parent class.
   • Example: A base class Animal can have common methods like eat() and sleep(), which are inherited by child classes like Dog and Cat.

2. Polymorphism:

   • Enables polymorphism, where a child class can override or extend the behavior of a parent class.
   • Example: A Dog class can override the speak() method of the Animal class to bark.

3. Type Safety:

   • Provides type safety at compile time (or design time in Python), ensuring that objects adhere to a defined structure.

4. Encapsulation:

   • Encapsulates data and behavior within classes, promoting modular and organized code.

5. Static Analysis:

   • Tools like linters and IDEs can analyze class hierarchies and improve code completion and error detection.

CONS

1. Rigidity:

   • The inheritance hierarchy is fixed at design time, making it less flexible for runtime changes.
   • Example: You cannot dynamically add or remove methods or attributes from a class at runtime.

2. Complexity:

   • Deep or multiple inheritance hierarchies can become difficult to maintain and understand.
   • Example: The "diamond problem" in multiple inheritance.

3. Tight Coupling:

   • Child classes are tightly coupled to their parent classes, making it harder to modify the parent class without affecting child classes.

4. Limited Runtime Flexibility:

   • Cannot easily adapt to runtime requirements, such as merging or modifying fields dynamically.

---

Dynamic Inheritance (Field-Based: sA + sB or sA << sB)

PROS

1. Runtime Flexibility:

   • Allows you to combine or modify fields dynamically at runtime.
   • Example: Merging configurations from two structures (sA and sB) based on runtime conditions.

2. Loose Coupling:

   • Structures are loosely coupled, combined dynamically rather than through a fixed hierarchy.

3. Adaptability:

   • Easily adapts to changing requirements, such as adding or overriding fields based on user input or external data.

4. Simplicity for Configuration:

   • Ideal for use cases like configuration management, where fields must be merged or dynamically overridden.
   • Example: Combining default settings with user-specific settings.

5. No Fixed Hierarchy:

   • Avoids the complexity of deep or multiple inheritance hierarchies.

CONS

1. Lack of Type Safety:

   • Fields are dynamically added or modified, which can lead to runtime errors if fields are misspelled or misused.

2. No Polymorphism:

   • Does not support polymorphism or method overriding, as it focuses on data (fields) rather than behavior.

3. Harder to Debug:

   • Dynamic field manipulation can make it harder to trace the origin of fields or debug issues.

4. Limited Code Reusability:

   • Does not provide a mechanism for reusing behavior (methods) across structures.

5. No Static Analysis:

   • Tools like linters and IDEs cannot easily analyze dynamic field manipulation, reducing code completion and error detection capabilities.

---

When to Use Static Inheritance

• Use static inheritance when:
  • You need to define a fixed hierarchy of classes with shared behavior.
  • You want to enforce type safety and encapsulation.
  • You need polymorphism to override or extend behavior.
  • Example: Modeling real-world entities like Material, Food, or Application.

---

When to Use Dynamic Inheritance

• Use dynamic inheritance when:
  • You need to combine or modify fields dynamically at runtime.
  • You are working with configurations, settings, or data structures that require flexibility.
  • You want to avoid the complexity of deep or multiple inheritance hierarchies.
  • Example: Merging configurations, combining datasets, or overriding default values.

---

Comparison Table

Feature	Static Inheritance (Class)	Dynamic Inheritance (Field)
Flexibility	Fixed at design time.	Dynamic at runtime.
Type Safety	High (compile-time checks).	Low (runtime errors possible).
Polymorphism	Supported.	Not supported.
Code Reusability	High (inheritance).	Limited (no behavior reuse).
Complexity	Can become complex (deep hierarchies).	Simpler (no fixed hierarchy).
Use Case	Modeling entities, polymorphism.	Configuration management, data merging.

---

Example Use Cases

Static Inheritance

class Material:
    def forcefield(self):
        return "Generic forcefield"

class Liquid(Material):
    def forcefield(self):
        return "ULSPH"

class Solid(Material):
    def forcefield(self):
        return "TLSPH"

gel = Solid()
print(gel.forcefield())  # Output: TLSPH

Dynamic Inheritance

from pizza.private.mstruct import paramauto

# Default configuration
default_config = paramauto(material="gel", density=950)

# User-specific configuration
user_config = paramauto(forcefield="TLSPH", density=1100)

# Merge configurations
final_config = default_config + user_config
print(final_config)  # Output: {'material': 'gel', 'density': 1100, 'forcefield': 'TLSPH'}

---

Conclusion

• Static inheritance is ideal for modeling fixed hierarchies and reusing behavior.
• Dynamic inheritance is ideal for runtime flexibility, such as merging configurations or datasets.
• Choose the approach that best fits your use case, considering the flexibility, type safety, and complexity trade-offs.