Dvi

[krystophny]

PR Type

Enhancement

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Description

• Add multiple numerical integration schemes for charged particle dynamics

  • Symplectic midpoint, variational midpoint, Lagrange, Runge-Kutta methods
  • Explicit-implicit and symplectic Euler implementations

• Create field evaluation module with tokamak magnetic field model

• Develop manifold analysis and visualization tools for particle trajectories

• Add plotting utilities for orbit and manifold visualization

• Reorganize and clean up codebase with test utilities

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Diagram Walkthrough

flowchart LR
  A["Field Model<br/>field_correct_test.py"] --> B["Integration Schemes"]
  B --> C["cpp_sym_midpoint.py"]
  B --> D["cp_sym_midpoint.py"]
  B --> E["cpp_lagrange.py"]
  B --> F["cpp_runge_kutta.py"]
  B --> G["cpp_expl_impl.py"]
  C --> H["Visualization"]
  D --> H
  E --> H
  F --> H
  G --> H
  H --> I["plotale.py<br/>manifolds.py"]

Loading

File Walkthrough

	Relevant files
Enhancement	13 files field_correct_test.py Tokamak magnetic field model implementation                            +57/-0    cpp_sym_midpoint.py Symplectic midpoint integration for charged particles        +121/-0  cp_sym_midpoint.py Classical particle symplectic midpoint scheme                        +221/-0  cpp_var_midpoint.py Variational midpoint integrator implementation                      +111/-0  cpp_lagrange.py Lagrange formalism integration scheme                                        +97/-0    cpp_runge_kutta.py Runge-Kutta explicit integration method                                    +113/-0  cpp_expl_impl.py Explicit-implicit hybrid integration scheme                            +84/-0    cpp_sym_euler.py Symplectic Euler integration method                                            +79/-0    cp_runge_kutta.py Classical particle Runge-Kutta integration                              +121/-0  plotale.py Orbit and manifold visualization utilities                              +98/-0    manifolds.py Manifold analysis and trajectory visualization                      +431/-0  field.py Simple magnetic field models with numba optimization          +143/-0  util.py Legendre polynomials and elliptic integral utilities          +197/-0
Formatting	1 files discrete_variational.py Minor formatting and print statement additions                      +3/-1
Tests	4 files DELETE.py Test script for symplectic midpoint method                              +44/-0    main.py Test implementation for guiding center approximation          +88/-0    mainHam.py Hamiltonian formalism test with implicit integration          +88/-0    mainLa.py Lagrangian formalism test implementation                                  +86/-0
Miscellaneous	1 files pauli_particle.py Removed obsolete Pauli particle implementation                      +0/-87

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[qodo-code-review]

PR Compliance Guide 🔍

Below is a summary of compliance checks for this PR:

Security Compliance
🟢	No security concerns identified No security vulnerabilities detected by AI analysis. Human verification advised for critical code.
Ticket Compliance
⚪	🎫 No ticket provided Create ticket/issue
Codebase Duplication Compliance
⚪	Codebase context is not defined Follow the guide to enable codebase context checks.
Custom Compliance
🟢	Generic: Secure Error Handling Objective: To prevent the leakage of sensitive system information through error messages while providing sufficient detail for internal debugging. Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Secure Logging Practices Objective: To ensure logs are useful for debugging and auditing without exposing sensitive information like PII, PHI, or cardholder data. Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
🔴	Generic: Meaningful Naming and Self-Documenting Code Objective: Ensure all identifiers clearly express their purpose and intent, making code self-documenting Status: Ambiguous Names: Variables like z1, z2, z3, X, Y, Z, c, and loop counters with reused names reduce clarity and violate meaningful naming guidance. Referred Code # x^i = x^i(q) x = (R0 + q[:,0] * np.cos(q[:,1])) * np.cos(q[:,2]) y = (R0 + q[:,0] * np.cos(q[:,1])) * np.sin(q[:,2]) z = z0 + q[:,0] * np.sin(q[:,1]) ### # # # # # # # # #vx5[j] = x_dot[0] #vy5[j] = x_dot[1] #vz5[j] = x_dot[2] ... (clipped 58 lines) Learn more about managing compliance generic rules or creating your own custom rules
⚪	Generic: Comprehensive Audit Trails Objective: To create a detailed and reliable record of critical system actions for security analysis and compliance. Status: No Logging: The new simulation steps, root-solver calls, and field evaluations perform critical numerical actions without any audit/logging of inputs, outputs, or outcomes. Referred Code for kt in range(nt): pold = p sol = root(F, z[:,kt], method='hybr',tol=1e-12,args=(z[:,kt], pold)) z[:,kt+1] = sol.x f.evaluate(z[0,kt+1], z[1,kt+1], z[2,kt+1]) g = metric(z[:,kt+1]) vel[:,kt+1] = 1/m * g['^ii']*(p - qe/c*np.array([0, f.co_Ath, f.co_Aph])) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Robust Error Handling and Edge Case Management Objective: Ensure comprehensive error handling that provides meaningful context and graceful degradation Status: Unhandled Solver: Calls to scipy.optimize.root assume success without checking sol.success or handling failures, which may cause silent numerical errors. Referred Code pold = p sol = root(F, z[:,kt], method='hybr',tol=1e-12,args=(z[:,kt], pold)) z[:,kt+1] = sol.x f.evaluate(z[0,kt+1], z[1,kt+1], z[2,kt+1]) g = metric(z[:,kt+1]) vel[:,kt+1] = 1/m * g['^ii']*(p - qe/c*np.array([0, f.co_Ath, f.co_Aph])) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Security-First Input Validation and Data Handling Objective: Ensure all data inputs are validated, sanitized, and handled securely to prevent vulnerabilities Status: Input Bounds: Mathematical functions (e.g., elliptic integrals, Legendre polynomials) accept raw inputs without explicit validation, relying on comments rather than enforcing domain constraints. Referred Code @njit def ellipe(m): """ Compute the complete elliptic integral of the second kind. Adapted from Cephes Math Library by Stephen L. Moshier MIT License Parameters: m (float): Input parameter (0 <= x <= 1) Returns: float: Computed value of E(m) """ P = np.array( [ 1.53552577301013293365e-4, 2.50888492163602060990e-3, 8.68786816565889628429e-3, 1.07350949056076193403e-2, 7.77395492516787092951e-3, ... (clipped 32 lines) Learn more about managing compliance generic rules or creating your own custom rules

Compliance status legend

🟢 - Fully Compliant
🟡 - Partial Compliant
🔴 - Not Compliant
⚪ - Requires Further Human Verification
🏷️ - Compliance label

[qodo-code-review]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
High-level	Consolidate duplicated code and experimental scripts The PR should be refactored to address widespread code duplication, especially of the metric function. Additionally, temporary and experimental files like DELETE.py, the test_delete_later directory, and manifolds.py should be removed or cleaned up. Examples: python/cp_runge_kutta.py [18-28] metric = lambda z: { "_11": 1, "_22": z[0]**2, "_33": (1 + z[0]*np.cos(z[1]))**2, "^11": 1, "^22": 1/z[0]**2, "^33": 1/(1 + z[0]*np.cos(z[1]))**2, "d_11": [0,0,0], "d_22": [2*z[0], 0,0], "d_33": [2*(1+z[0]*np.cos(z[1]))*np.cos(z[1]), -2*(1+z[0]*np.cos(z[1]))*np.sin(z[1]), 0], ... (clipped 1 lines) python/cp_sym_midpoint.py [17-23] metric = lambda z: { "_ii": np.array([1, z[0]**2, (1 + z[0]*np.cos(z[1]))**2]), "^ii": np.array([1, 1/z[0]**2, 1/(1 + z[0]*np.cos(z[1]))**2]), "d_11": np.array([0,0,0]), "d_22": np.array([2*z[0], 0,0]), "d_33": np.array([2*(1+z[0]*np.cos(z[1]))*np.cos(z[1]), -2*(1+z[0]*np.cos(z[1]))*np.sin(z[1]), 0]), } Solution Walkthrough: Before: # In file 'python/cp_runge_kutta.py' metric = lambda z: { ... } # Definition 1 ... # In file 'python/cp_sym_midpoint.py' metric = lambda z: { ... } # Definition 2 ... # In file 'python/cpp_expl_impl.py' metric = lambda z: { ... } # Definition 3 ... # ... and so on in 5 other files # In file 'python/DELETE.py' # ... temporary script ... # In directory 'python/test_delete_later/' # ... multiple temporary scripts ... After: # In a new file 'python/utils.py' or 'python/common.py' def get_metric(z): # Centralized metric function logic return { "_ii": np.array([1, z[0]**2, (1 + z[0]*np.cos(z[1]))**2]), "^ii": np.array([1, 1/z[0]**2, 1/(1 + z[0]*np.cos(z[1]))**2]), ... } # In file 'python/cp_runge_kutta.py' from .utils import get_metric metric = get_metric ... # In file 'python/cp_sym_midpoint.py' from .utils import get_metric metric = get_metric ... # ... and so on in other files # Files 'DELETE.py' and directory 'test_delete_later/' are removed. Suggestion importance[1-10]: 9 __ Why: This suggestion correctly identifies major structural issues, including significant code duplication of the metric function and the inclusion of numerous experimental/temporary files, which severely impact maintainability and code quality across the entire PR.	High
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